# Sedai: Blog (part 19 of 27)

Full content of 10 pages from https://sedai.io/blog, published 2026-03-24 to 2026-04-15. Only pages published in the last 12 months are included. Each entry starts with the page title and its source URL. Every full-content file is listed in https://sedai.io/llms.txt.

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# The Case Against LLMs in Your CI/CD Pipeline
Source: https://sedai.io/blog/case-against-llms-cicd-pipeline

AI agents are replacing traditional CI/CD pipelines, but LLMs don't belong in production decision-making. Here's what safe agentic CI/CD actually requires.

My content team is constantly sending me articles to read. When we sat down last week to talk about the “end of CI/CD pipelines,” and how AI is upending what we know as CI/CD, I initially balked.

**In what world would an engineering team ever let AI touch CI/CD?**

The article I read highlights [how AI agents are now replacing traditional pipelines](https://hackernoon.com/the-end-of-cicd-pipelines-the-dawn-of-agentic-devops?source=rss&utm_source=tldrdevops) by debugging tests, deploying code, & triaging incidents without human intervention (GitHub is already[ embedding agent runners natively into CI/CD](https://thenewstack.io/github-agentic-workflows-overview/)). 

But as someone who thinks about autonomy constantly, the idea of agentic CI/CD pipelines is a bit concerning to me. 

Most systems that claim to be autonomous still rely on human-defined rules & scripts that break the moment something unexpected happens. Agentic CI/CD is no different, and in a pipeline that touches production, that's a problem.

As we start to see engineering teams shift their CI/CDs to "agentic judgement," debugging changes entirely. 

In traditional CI/CDs, a failed health check tells you exactly what broke and a misconfigured YAML file is at least findable. But introducing probabilistic AI, like LLMs, into the pipeline makes production decisions non-deterministic. You can log what the agent did, but you can't guarantee it would make the same call twice.

It’s understandable why the industry is responding to this real shift by reaching for the most familiar AI tool available: LLMs. But LLMs don't belong in production decision-making, not because agentic DevOps is a bad idea, but because we can't trust "close enough" when the action is a production deployment. 

Here's what getting it right actually requires.

## What CI/CD Actually Got Right

While I was an engineer at eBay, we had train release cycles where we would release every two months, sometimes longer. When CI/CD was introduced, it fundamentally broke that release model. What used to take months collapsed into hours, then minutes. 

The feedback loop got so tight that releasing stopped being an event and started being a routine. The key to that was **determinism**: every step in the pipeline was explicit, traceable, & repeatable. If something failed, you knew exactly where & why it happened.

And for a long time, that was enough. But the judgment calls — what to do when a deployment behaved unexpectedly, how to interpret a failure that didn't fit a known pattern, or when to roll back versus push forward — still fell back to us engineers.

We trusted ourselves to make the right decisions, but the toil of making them slowed us down. So now, when engineering teams try to hand that judgment to AI agents, the instinct makes sense: you want to operationalize judgment.

However, agents can only approximate judgment, and in a production pipeline, that approximation introduces risk.

## The Problem With LLMs in Your Pipeline

Most of what the industry is calling "agentic DevOps" right now is just LLMs layered on top of existing pipelines. 

But because [LLMs are probabilistic reasoners](https://sedai.io/blog/autonomy-context-problem-not-model-problem), they're both incredibly smart & unbelievably dumb at the same time, which is what makes them so dangerous in production.

### Where It Breaks In Practice

Here's what that actually looks like in practice: a rollout looks clean, SLOs hold, and the agent moves on. An hour later, latency spikes under real traffic. 

The agent never sees it. It checked the metrics at deploy time, saw green, & considered the job done. What happens under real traffic an hour later isn't its problem anymore because it's already on to the next task.

And if the agent does catch the problem and decides to roll back, that's when things get worse. If that deployment included a database schema change, for example, rolling back the code without handling the schema leaves you in a broken state. 

Or, maybe you made a customer commitment tied to that release, and rolling back means breaking an SLA. The agent doesn't know any of that. It only knows "metrics look bad, roll back."

I think about this in the same way I think about cars & aircraft. You can build a faster & faster car, but no matter how fast it goes, it will never fly. If you want to fly, you have to build an aircraft. It's a different vehicle entirely, designed for a different problem. 

Teams layering LLMs onto DevOps pipelines are just building a faster car. They’re not turning into an autonomous engine, and that leaves them open to vulnerabilities.

### What Actually Works

LLMs make decisions based on patterns they've learned without ever seeing the real outcomes in your actual system. Reinforcement learning-based autonomous systems are different: they make decisions and learn from what actually happens. 

That feedback loop, decisions grounded in real outcomes, is the foundation of safe agentic DevOps. The question is what you build on top of it.

## How To Build A Safe Agentic CI/CD Pipeline

The teams that get agentic DevOps right will be the ones who treat it as an architecture problem. Teams often focus on what an agent can do and how fast it can do it rather than what constrains it. 

Safe agentic systems must rely on layers of **guardrails**, each one constraining the one above it. 

There are three layers to safely implement an agentic CI/CD pipeline:

- Agent observability
- Incremental execution & explicit rollback
- Continuous validation against live signals

### Agent Observability

Observability is nothing new; the market is flooded with observability tools & dashboards. But what is missing from current observability is the ability to see how agents are reasoning.

In a CI/CD pipeline, this is especially dangerous. An agent misclassifying a test failure or misreading an incident signal doesn't just produce one wrong output, it cascades into compounding errors.

"In production, only act on true confidence. There's a difference between 99.9% and 100%. At 99.9%, you wait. At 100%, you move."

— Suresh Mathew, CEO, Sedai

This might mean a deployment proceeds when it shouldn't or a rollback gets skipped. By the time something visibly breaks in production, you're three or four decisions deep with no decision log to trace back through.

So how can engineers effectively implement observability? 

At Sedai, we log everything: the prior state, the new state, [and the reasoning behind the decision](https://sedai.io/blog/how-to-survive-death-of-saas). We ask questions like:

- **Why did we resize that deployment?** Because a new release slowed the application down & created a cascade effect. 
- **Why did we adjust resources there?** Because traffic shifted & dependencies changed. 

An engineer reviewing that log should see the complete picture; not just "action taken," but the signals that triggered it and what we expected to happen. 

For me, that's non-negotiable. If you can't explain a decision, you can't trust it.

### Incremental Execution & Explicit Rollback

The most terrifying possibility I see in agentic CI/CD is the lack of a clearly defined rollback. Errors are inevitable, especially in systems that are still learning, but no action that affects production should be irreversible without explicit confirmation.  

Most of the time when rollback happens, it's not because a decision was wrong, but because the context around the decision shifted. This context is what we build into Sedai; we can watch for novel signals that make a previously safe decision unsafe going forward

For teams starting out, keep it simple. Build a rule: if you detect behavior or data you've never observed, air on the side of caution and roll back the last autonomous change. 

It’s better to revert & investigate than push forward into unknown territory. Honestly, with proper guardrails & continuous observation, you shouldn't hit rollback often.

### Continuous Validation Against Live Signals

Continuous validation is where most agentic DevOps implementations fall apart.

Everyone’s instinct is to validate at deployment time: run the tests, check the thresholds, and confirm the rollout looks clean. 

But in an agentic system, you must watch *everything* observable, from the obvious signals like SLOs & latency, to the non-obvious, like dependency shifts & resource allocation. Any deviation from baseline is a signal worth capturing.

As for when to act: never act on high confidence. Act only on true confidence. There's a meaningful difference between 99.9% and 100%. At 99.9%, you wait; at 100%, you move.

## Conclusion

Agentic DevOps is coming because the productivity gains are real: faster deployments, fewer manual interventions, and pipelines that can respond without waiting for a human. But with that comes risk, and engineering teams must be realistic about what LLMs can’t do, and build for failure before it happens.

At Sedai, this is the only model we've ever known. Every autonomous action we take is grounded in real system behavior, constrained by explicit policy, & reversible by design. Not because we're cautious, [but because that's what safe autonomy requires](https://sedai.io/platform).

---

# SaaS vs On-Premise Explained
Source: https://sedai.io/blog/saas-vs-on-prem

SaaS vs on-prem explained for modern teams. Compare control, cost, security, customization, and the hidden operational responsibility that remains in both models.

When teams evaluate **SaaS vs. on-prem**, the obvious trade-offs, speed vs. control, subscription vs. upfront cost, are only part of the picture.

What actually matters shows up later: how your team handles scaling decisions, manages cost drift, responds to incidents, & keeps systems efficient as workloads change.

This isn’t a one-time choice. It’s a decision that shapes how your systems behave in production & how much ongoing effort they require to keep them running well.

In this guide, we’ll break down:

- [What Do We Mean by SaaS & On-Prem?](https://sedai.io/blog/saas-vs-on-prem#:~:text=After%20the%20Decision-,What%20Do%20We%20Mean%20by%20SaaS%20%26%20On%2DPrem%3F,-SaaS%20(Software%20as)
- [Ownership vs. Convenience](https://sedai.io/blog/saas-vs-on-prem#:~:text=pressure%2C%20or%20misconfigured.-,Ownership%20vs.%20Convenience,-SaaS%20removes%20entire)
- [Getting Started: Time to First Value](https://sedai.io/blog/saas-vs-on-prem#:~:text=as%20systems%20grow.-,Getting%20Started%3A%20Time%20to%20First%20Value,-SaaS%20significantly%20reduces)
- [Cost: Where It Actually Accumulates](https://sedai.io/blog/saas-vs-on-prem#:~:text=long%2Dterm%20control.-,Cost%3A%20Where%20It%20Actually%20Accumulates,-The%20common%20comparison)
- [Flexibility & Control](https://sedai.io/blog/saas-vs-on-prem#:~:text=Book%20a%20Demo-,Flexibility%20%26%20Control,-On%2Dprem%20environments)
- [Security: Responsibility in Practice](https://sedai.io/blog/saas-vs-on-prem#:~:text=managed%20system.-,Security%3A%20Responsibility%20in%20Practice,-In%20on%2Dprem)
- [Scaling: What Breaks First](https://sedai.io/blog/saas-vs-on-prem#:~:text=requires%20continuous%20attention.-,Scaling%3A%20What%20Breaks%20First,-SaaS%20systems%20scale)
- [Operational Reality After the Decision](https://sedai.io/blog/saas-vs-on-prem#:~:text=as%20systems%20grow.-,Operational%20Reality%20After%20the%20Decision,-Once%20systems%20are)
- [A More Practical Way to Evaluate the Choice](https://sedai.io/blog/saas-vs-on-prem#:~:text=A%20More%20Practical%20Way%20to%20Evaluate%20the%20Choice)
- [A More Grounded Way to Think About It](https://sedai.io/blog/saas-vs-on-prem#:~:text=A%20More%20Grounded%20Way%20to%20Think%20About%20It)
- [Bringing It Together](https://sedai.io/blog/saas-vs-on-prem#:~:text=Bringing%20It%20Together)

## **What Do We Mean by SaaS & On-Prem?**

SaaS (Software as a Service) runs on infrastructure managed by a provider. Your team interacts with it through APIs & interfaces rather than managing servers directly. Provisioning, patching, & most scaling decisions are handled for you.

On-prem runs on infrastructure your team owns or directly controls. That includes capacity planning, upgrades, failover design, & performance tuning.

The difference isn’t abstract, it comes down to who is responsible when systems are inefficient, under pressure, or misconfigured.

Most cost visibility tools show you the problem. [Book a demo](https://sedai.io/demo) to see how Sedai fixes it, taking autonomous action based on your actual workload behavior.

## **Ownership vs. Convenience**

SaaS removes entire categories of operational work. There’s no need to provision clusters, manage patch cycles, or maintain hardware. That allows teams to focus more on application logic & delivery.

This shift isn’t just theoretical. In the [Flexera 2024 State of the Cloud Report](https://www.flexera.com/about-us/press-center/flexera-2024-state-of-the-cloud-managing-spending-top-challenge), organizations consistently rank **managing cloud infrastructure & spend as their top challenge**, highlighting how much operational effort is abstracted away in managed environments.

But abstraction comes with limits. You don’t control how resources are allocated under the hood, & when performance or cost issues arise, your ability to fix them is constrained by the platform.

This is where the cracks tend to show. Industry data suggests that [**20–30% of cloud spend is wasted**](https://www.ibm.com/think/insights/how-fin-ops-drives-smarter-cloud-and-ai-for-government) due to overprovisioning & limited visibility into actual resource usage, often a byproduct of operating within abstracted systems. Even with modern tooling, inefficiencies persist because teams can’t always fine-tune infrastructure behavior directly.

On-prem gives you full control over infrastructure decisions. You can fine-tune resource allocation, customize scaling behavior, & design systems around your exact workload.

That control comes with a cost: every optimization, upgrade, & failure scenario becomes your responsibility to handle, & that surface area expands as systems grow.

## **Getting Started: Time to First Value**

SaaS significantly reduces time-to-first-value. Teams can deploy, test workflows, & go to production within hours because the infrastructure layer is already in place.

On-prem, by contrast, requires sequencing. Infrastructure must be provisioned, environments configured, networks secured, & systems validated before applications can run reliably.

Enterprise infrastructure studies from [IDC](https://www.idc.com/getdoc.jsp?containerId=prUS48087621) & [Gartner](https://www.gartner.com/en/information-technology/insights/cloud-computing) show that traditional environment setup & provisioning can take **weeks**, particularly in regulated or large-scale environments.

Even with strong automation, this adds friction. The trade-off is straightforward: SaaS accelerates early progress, while on-prem delays it in exchange for deeper long-term control.

## **Cost: Where It Actually Accumulates**

The common comparison; subscription vs. upfront cost, misses where most inefficiency actually occurs.

In SaaS environments, costs scale with usage, but inefficiencies are often embedded in that usage:

- Over-provisioned resources based on conservative defaults
- Idle capacity during off-peak periods
- Misconfigured resource requests that increase spend without improving performance

According to the [**Flexera 2024 State of the Cloud Report**](https://www.flexera.com/resources/research/state-of-the-cloud-report/), organizations estimate that **27–32% of cloud spend is wasted** due to underutilized resources.

At scale, these issues compound quietly. Cost increases are gradual, & without continuous adjustment, they become difficult to control.

On-prem shifts the cost challenge. Research from [**IDC**](https://www.idc.com/getdoc.jsp?containerId=US44413318) shows that average on-prem infrastructure utilization often sits around **20–30%**, as systems are provisioned for peak demand. Instead of variable spend, you deal with capacity risk:

- Overestimating demand & underutilizing infrastructure
- Manual tuning cycles that lag behind workload changes
- Engineering time spent maintaining systems rather than improving them

In both cases, cost is a byproduct of how well systems adapt to real workload behavior.

### Understand SaaS vs On-Premise Explained

See how Sedai breaks down SaaS vs On-Premise for smarter decisions, better scalability & cost efficiency.

## **Flexibility & Control**

On-prem environments allow deep customization. You can tailor infrastructure, integrate tightly with internal systems, & enforce specific architectural patterns.

According to [**Gartner**](https://www.gartner.com/en/documents/3983626), organizations in regulated industries often retain on-prem or hybrid models to maintain **greater control over data residency, compliance, & performance tuning**

SaaS platforms offer configuration & extensibility, but within defined boundaries. Those constraints simplify operations but can limit the precision with which systems can be tuned.

The trade-off is between **freedom to optimize everything** & **simplicity of operating within a managed system**.

## **Security: Responsibility in Practice**

In on-prem environments, your team owns the full security stack, patching, monitoring, access control, & compliance.

This provides control but requires consistent execution. Gaps in processes or delayed updates can introduce risk.

SaaS operates on a shared responsibility model. Providers handle infrastructure-level security, while your team remains responsible for configuration, access management, & data handling.

Security doesn’t disappear in either model. It shifts & in both cases, it requires continuous attention.

## **Scaling: What Breaks First**

SaaS systems scale easily at the surface. Increasing usage typically doesn’t require any infrastructure changes on your end.

But scaling introduces second-order effects: rising costs, performance variability, & limited visibility into how resources are managed under load.

On-prem scaling is explicit. You add capacity, rebalance workloads, & adjust architecture. This gives you precision, but also introduces friction; each scaling decision requires planning & validation.

The key difference is responsiveness: how quickly your team can identify & correct inefficiencies as systems grow.

## **Operational Reality After the Decision**

Once systems are in production, the day-to-day challenges start to look surprisingly similar, regardless of whether you chose SaaS or on-prem. Resources tend to get over-allocated as a safety measure, performance can fluctuate as workloads change, & costs don’t always stay where you expect them to. Over time, teams find themselves stepping in repeatedly to tune & adjust things, trying to keep up with demand that doesn’t behave predictably.

In SaaS environments, this usually shows up as steadily increasing bills or inconsistent performance without an obvious cause. In on-prem setups, it’s more visible as underutilised infrastructure & the ongoing effort required to keep systems running efficiently.

Underneath it all, the issue is the same in both models. Systems are often configured based on assumptions made at one point in time, but workloads keep evolving. Without something that continuously adapts to that reality, inefficiencies tend to build quietly in the background.

## **A More Practical Way to Evaluate the Choice**

The more useful framing is operational:

Where will your team spend time once systems are live?
How quickly can you respond to inefficiencies or performance issues?
Can your systems continuously adapt, or do they rely on manual intervention?

These questions tend to matter more than the initial deployment model.

## **A More Grounded Way to Think About It**

The more useful question isn't which deployment model is technically superior. It's where your engineering time actually goes once systems are live.

Teams that underestimate this end up in the same place regardless of which model they choose: engineers spending disproportionate time chasing configuration drift, tuning resource allocations, & responding to cost spikes that build up gradually & invisibly.

Workloads don't stay static. A system sized correctly at launch will drift out of alignment as traffic patterns shift, new services are added, & usage behavior changes. If keeping things efficient depends on manual intervention every time that happens, the operational burden compounds quietly, & it shows up in engineering capacity before it shows up on a dashboard.

The deployment model matters less than whether your systems can continuously adapt to real workload behavior without requiring someone to step in each time.

On-premises full stack control requires ongoing capital investment and operational overhead that doesn't scale as efficiently as cloud. [Book a demo](https://sedai.io/demo) to see how Sedai keeps cloud costs predictable as you migrate workloads and reduce that on-prem burden.

## **Bringing It Together**

Across both SaaS & on-prem environments, teams face the same core issue: infrastructure decisions are often static, while workloads are not.

Sedai addresses this by continuously analyzing real-time application behaviour and autonomously adjusting resource configurations, compute, memory, & scaling policies without manual intervention.

If your systems still rely on periodic tuning or manual optimization, it’s worth exploring how [autonomous optimization](https://sedai.io/contact) changes that model.

---

# The FinOps Maturity Model: What Actually Stalls Teams at Each Stage
Source: https://sedai.io/blog/finops-maturity-model

Explore the FinOps maturity model through Crawl, Walk, & Run stages, plus the blockers, metrics, & operating model changes that help teams mature safely.

Most teams know which FinOps maturity stage they're in. What they don't know is why they can't leave it.

The FinOps Foundation's[ crawl-walk-run model](https://www.finops.org/framework/maturity-model/) describes what each stage looks like. That definitional work is useful. But practitioners don't need a better description of the stages. They need to understand the specific technical blockers that prevent moving between them.

These blockers are technical, not cultural. Culture & buy-in matter, but they cannot overcome a tooling gap. Each transition stalls because of a precise engineering gap: wrong data at crawl, wrong signal at walk, & no continuity at run.

- [Crawl: The Attribution Gap](https://sedai.io/blog/finops-maturity-model#:~:text=a%20System%20Property-,Crawl%3A%20The%20Attribution%20Gap,-Billing%20exports%20and)
- [Walk: The Trust Problem](https://sedai.io/blog/finops-maturity-model#:~:text=expose%20by%20default.-,Walk%3A%20The%20Trust%20Problem,-Teams%20at%20walk)
- [Run: The Continuity Problem](https://sedai.io/blog/finops-maturity-model#:~:text=at%20a%20%E2%80%98walk%E2%80%99.-,Run%3A%20The%20Continuity%20Problem,-Teams%20at%20run)
- [Beyond the FinOps Maturity Model: Optimization as a System Property](https://sedai.io/blog/finops-maturity-model#:~:text=Book%20a%20Demo-,Beyond%20the%20FinOps%20Maturity%20Model%3A%20Optimization%20as%20a%20System%20Property,-What%20distinguishes%20teams)

## **Crawl: The Attribution Gap**

Billing exports and Cost Explorer allocate spend to accounts & services. They do not attribute spend to the pods, containers, or functions actually consuming resources.

**Tag coverage is incomplete.** Not because teams are careless, but because tagging discipline is hard to enforce across engineering teams with different deployment patterns. When tags are inconsistent, attribution is approximate. When attribution is approximate, prioritization is guesswork.

**Namespace-level metrics are missing.** A team can see that an EKS cluster costs $40,000 per month, but cannot determine which workloads or application teams are responsible for that spend. Without workload-level attribution, you cannot have a credible conversation with engineering about where to focus.

**"Good enough" means:** 80% of controllable cluster spend is traced to the controller (Deployment, StatefulSet, Job) level, refreshed daily. That threshold is where you stop guessing & start profiling. It requires tooling beyond billing exports & namespace-level metrics that billing APIs do not expose by default.

Most FinOps tools tell you what to fix. [Book a demo](https://sedai.io/demo) to see how Sedai fixes it, taking real-time autonomous action so your team spends time on strategy, not tickets.

## **Walk: The Trust Problem**

Teams at walk have data. They cannot act safely on it.

**The blocker: wrong signal.** Average CPU and memory utilization tells you what a workload consumes under normal conditions. It does not tell you what happens at the 95th or 99th percentile of traffic. It does not account for seasonality, batch job bursts, or the behavior of a service hours after a new deploy.

A cold start on a fresh deploy or a batch job spike ten minutes after a config change can cascade into latency breaches or error spikes that a month of average-based optimization never saw coming. A configuration that looks safe at mean utilization can push latency past SLO thresholds under peak load. That's why every rightsizing action still requires **human review**, a **change window**, & a **rollback plan**.

The approval loop is the symptom. The blocker is the absence of a signal.

For that to change, the system making the recommendation needs to observe application behavior before acting. Not average utilization. Traffic patterns, seasonal variance, & p99 latency. As covered in[ The Hard Truth: FinOps Inform Doesn't Pay the Bills](https://sedai.io/blog/the-hard-truth-finops-inform-doesnt-pay-the-bills), a recommendation is not a result. The execution gap is what keeps teams at a ‘walk’.

## **Run: The Continuity Problem**

Teams at run execute optimization. The blocker: they execute it once, then move on.

**Rightsizing becomes a project, not a practice.** A team runs an exercise, captures savings, & closes the ticket. A single Friday afternoon deploy — a code change, a replica addition, a new data dependency invalidates weeks of manual work. 

**The half-life of a one-time rightsizing exercise is measured in weeks, not months.** Three months later, the application has been redeployed multiple times, traffic patterns have shifted, & the configuration that was right-sized is now wrong-sized again. McKinsey found that only[ 10% of cloud transformations achieve full value](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/unlocking-cloud-value-achieving-operational-excellence-through-sre#:~:text=10%20percent%20of%20cloud%20transformations). Optimization, durability, & **continuous re-evaluation** are the core challenges separating leaders from laggards in this space.

Continuous re-evaluation requires a different *technical capability* than periodic review. It requires the system to observe workload behavior after every deploy & reassess resource configuration against current behavior, not the snapshot from the last optimization pass. Most teams at run have tooling that supports review-and-act. What they lack is tooling that observes & re-evaluates continuously.

For a broader look at what optimization tactics look like at this stage,[ Top 17 FinOps Cloud Optimization Strategies for 2026](https://sedai.io/blog/finops-cloud-optimization-strategies) covers the full range. The pattern across all of them is the same: episodic optimization fails because the system it optimized for what changed.

The difference between a team that optimizes & a team that stays optimized is this: the first treats optimization as an *event*. The second has made it a part of the operating system.

### Understand FinOps Maturity Model

See how Sedai explains FinOps maturity models in 2026 for growth, control & cost efficiency.

## **Beyond the FinOps Maturity Model: Optimization as a System Property**

What distinguishes teams that reach "optimized" is not better tooling selection at the start. It is treating optimization as an ongoing operational function. Cloud spend is not a state to be achieved. It is a variable that shifts with every change in workload behavior.

Closing the loop between workload observation, configuration change, & outcome validation requires a system that acts continuously. That loop cannot run on human approval cycles at scale. Not every single time, at least.[ Beyond Recommendations: The Case for Autonomous Cloud Optimization](https://sedai.io/blog/beyond-recommendations-the-case-for-autonomous-cloud-optimization) makes this case in full.

Each blocker has a technical solution. 

- **At crawl-to-walk, the answer is workload-level attribution** using application-aware observability, connecting resource consumption to the namespace, service, & workload generating it. 
- **At walk-to-run, the blocker is the signal**. The solution is observation of application behavior — traffic patterns, seasonality, & p99 latency before acting. Safety verification moves from post-deployment (rollback) to pre-deployment (signal-driven decision).
- **At run-to-optimize, the blocker is an episodic action**. The solution is continuous re-evaluation triggered by behavioral shifts, not calendar cycles. Re-evaluation after every deploy keeps configuration aligned to current workload behavior, not the snapshot from yesterday's optimization pass.
- Organizations at the Run stage of FinOps maturity have integrated cost accountability into engineering workflows — but reaching that stage requires the execution layer that most [FinOps tools](https://sedai.io/blog/top-finops-tools-for-engineering-leaders) don't provide. [Book a demo](https://sedai.io/demo) to see how Sedai closes that gap.

## **Sedai: Removing Each Blocker**

[Sedai](https://sedai.io/) removes each blocker through workload-level attribution, signal-aware decisioning, & continuous re-evaluation.

- **Crawl→Walk:** Application-aware observability traces resource consumption to workloads, enabling controller-level attribution without perfect tagging.
- **Walk→Run:** Observation of traffic patterns & p99 latency before acting, with incremental reversible changes.
- **Run→Optimize:** Continuous re-evaluation after every deploy, keeping configuration aligned to current behavior, not yesterday's snapshot.

---

# FinOps RACI Execution in 2026
Source: https://sedai.io/blog/finops-raci

Learn how the FinOps RACI model must evolve in 2026 to support AI-driven infrastructure, autonomous optimization, and real-time cloud cost control.

## Introduction

FinOps nailed the basics: it made cloud cost optimization someone's job. But honestly, it never answered the real question: why aren't teams actually doing it?

Most companies have the RACI charts sorted out. Everyone knows who owns what, the responsibilities are written down, & dashboards are stacked with suggestions. Still, the promised savings never show up.

[Flexera's numbers](https://www.flexera.com/about-us/press-center/flexera-finds-cloud-value-is-rising-while-ai-waste-grows) say it all: even with mature FinOps, companies waste nearly 29% of what they spend on the cloud. 

The gap is between knowing what to optimize and actually making those changes in production.

We've built systems that keep people accountable, but not systems that drive action.

Clouds shift constantly. But FinOps still moves at the speed of humans meaning optimization just gets shoved into the backlog, logged as a ticket, or stuck on the "later" pile.

Most FinOps tools tell you what to fix. [Book a demo](https://sedai.io/demo) to see how Sedai fixes it, taking real-time autonomous action so your team spends time on strategy, not tickets.

This article digs into where FinOps RACI misses the mark & lays out what a real, execution-focused model could look like in 2026.

## Table of Contents

- [The Promise of FinOps & the Reality in 2026  ](https://sedai.io/blog/finops-raci#:~:text=FAQ-,The%20Promise%20of%20FinOps%20%26%20the%20Reality%20in%202026,-FinOps%20promised%20to)
- [What Is a FinOps RACI Model?  ](https://sedai.io/blog/finops-raci#:~:text=execution%20never%20happens.-,What%20Is%20a%20FinOps%20RACI%20Model%3F,-So%2C%20what%27s%20a)
- [Where FinOps RACI Breaks Down in Modern Cloud Environments  ](https://sedai.io/blog/finops-raci#:~:text=to%20work%2C%20anyway.-,Where%20FinOps%20RACI%20Breaks%20Down%20in%20Modern%20Cloud%20Environments,-Teams%20can%20clearly)
- [The Real Villain: Blind Optimization & Manual Execution](https://sedai.io/blog/finops-raci#:~:text=Book%20a%20Demo-,The%20Real%20Villain%3A%20Blind%20Optimization%20%26%20Manual%20Execution,-Most%20teams%20reach)  
- [RACI Solves Coordination Not Execution  ](https://sedai.io/blog/finops-raci#:~:text=lack%20application%20context-,RACI%20Solves%20Coordination%20Not%20Execution,-RACI%20helps%20teams)
- [What a Modern FinOps Operating Model Looks Like in 2026  ](https://sedai.io/blog/finops-raci#:~:text=true%20execution%20layer.-,What%20a%20Modern%20FinOps%20Operating%20Model%20Looks%20Like%20in%202026,-Modern%20FinOps%20isn%27t)
- [Closing the Execution Gap with Application-Aware Autonomy  ](https://sedai.io/blog/finops-raci#:~:text=the%20cloud%20does.-,Closing%20the%20Execution%20Gap%20with%20Application%2DAware%20Autonomy,-RACI%20defines%20ownership)
- [Real-World Example: RACI vs Autonomous Execution  ](https://sedai.io/blog/finops-raci#:~:text=the%20environment%20evolves.-,Real%2DWorld%20Example%3A%20RACI%20vs%20Autonomous%20Execution,-Here%27s%20how%20it)
- [Conclusion](https://sedai.io/blog/finops-raci#:~:text=Conclusion%3A%20RACI%20Is%20Necessary.%20But%20Not%20Sufficient)
- [FAQ](https://sedai.io/blog/finops-raci#:~:text=to%20truly%20work.-,FAQ,-Is%20RACI%20still)  

## The Promise of FinOps & the Reality in 2026

FinOps promised to bring order to the wild world of cloud computing and, honestly, it did. Suddenly, everyone could see who owned what. Cost wasn't some murky, end-of-month surprise anymore. Teams actually knew who to call when bills shot up. That felt like a big step forward.

But here's the thing: the cloud kept picking up speed. Infrastructure flips every minute, something's always scaling up or shifting because of a traffic spike, or workloads jump unexpectedly. 

But FinOps is still stuck on checklists, meetings, and approval cycles that can't keep pace with infrastructure that shifts by the minute. That gap is the real problem. You can have all the ownership charts and RACI models in place, but if teams can't act fast enough, it breaks down when it matters most.

Most teams reach a point where they can clearly see optimization opportunities, but execution never happens.

## What Is a FinOps RACI Model?

So, what's a FinOps RACI model? It's basically a chart that spells out who handles each part of cloud cost optimization.

There are four roles:

- Responsible: these folks actually make the changes,
- Accountable: they're the ones who own the results,
- Consulted: they check to make sure nothing breaks or becomes unreliable,
- Informed: they keep everyone up to date.

Most companies follow a pretty standard setup. FinOps takes charge of cost outcomes. Engineering rolls up their sleeves & does the work. Platform and SRE teams get a say, making sure production stays steady.

On paper, it all lines up neatly. Everybody has their job, nothing gets missed.

That's how it's supposed to work, anyway.

## Where FinOps RACI Breaks Down in Modern Cloud Environments

Teams can clearly see idle resources and over-provisioned services, but acting on those insights requires multiple approvals, cross-team coordination, and time that teams rarely have. That's why RACI, while useful for defining ownership, fails to keep up with how quickly cloud environments change.

Just because someone "owns" a task doesn't mean anything actually gets done. Everyone sees the same issues: bloated services, idle resources, and workloads that could be right-sized. The dashboards are full of cost-saving opportunities, but those fixes remain unimplemented.

The problem isn't awareness it's execution friction. Every change needs alignment across teams, stakeholder approval, and time that competes with product work. Cost optimization rarely feels urgent compared to shipping features or resolving incidents, so it keeps getting pushed out.

This becomes more obvious in real-time systems. Infrastructure is constantly shifting with traffic, scaling events, and deployments. RACI, however, operates in a batch model. By the time a recommendation is approved and implemented, the system has already changed.

McKinsey estimates that up to a [quarter of cloud spend](https://www.cfodive.com/news/5-ways-cut-waste-cloud-spending-mckinsey-computing-technology/636539/) is wasted not because teams don't know what to fix, but because they can't execute consistently across dynamic environments.

Risk makes this worse. Engineers hesitate because they can't fully predict how a change will behave in production. A simple rightsizing decision affects more than cost; it changes how the system handles traffic, latency headroom, and downstream dependencies.

Without that context, every [cloud cost optimization](https://sedai.io/blog/cloud-cost-optimization-strategies-practices) becomes a tradeoff. The savings are clear, but the downside is immediate and uncertain. Even with rollback, a bad change can quickly show up as latency spikes, errors, or cascading failures.

So nothing really changes. Optimization work piles up as a backlog while the system keeps evolving.

### Understand FinOps RACI in 2026

See how Sedai explains FinOps RACI in 2026 for clear roles, accountability & cost efficiency.

## **The Real Villain: Blind Optimization & Manual Execution**

Most teams reach a point where neither option feels right: continue with slow, manual optimization or rely on automated systems they don’t fully trust.

Manual execution doesn’t scale. Engineers spend hours reviewing dashboards, debating changes, submitting tickets, and waiting for approvals before anything reaches production. Google’s SRE teams call this toil — repetitive work that consumes time but doesn’t improve system behavior.

[Cost optimization](https://sedai.io/blog/cloud-cost-optimization-strategies) is full of this overhead, which is why teams start looking to automated tools. But that introduces a different problem.

Most tools operate on isolated signals like CPU or memory, without understanding how the application behaves. They don’t account for traffic patterns, service dependencies, or SLO constraints. 

A change that looks efficient at the infrastructure level can degrade performance when real traffic hits the system, & that's precisely where trust in automated tooling breaks down. In production, even small changes carry risk: reducing resources might save cost, but it can also reduce buffer capacity during traffic spikes, increase latency under load, or create pressure on downstream services. 

These effects aren’t always visible in metrics before the change is made. As a result, engineers treat these recommendations cautiously. 

The potential savings are clear, but the failure modes are harder to predict and far more expensive to recover from. So teams stall on decisions they could see coming weeks earlier,  not because they don't want to optimize, but because they don't trust recommendations that lack application context

## **RACI Solves Coordination Not Execution**

RACI helps teams stay aligned, but it operates at the level of coordination, not execution. Earlier, coordination was the challenge. Today, execution is where teams struggle.

Today's cloud environments demand constant decisions. Stuff like scaling and resource tuning can't sit around waiting for the next weekly meeting or a slow approval chain. These changes need to happen instantly.

That's why the conversation has shifted toward building an execution layer. Instead of just assigning responsibility, you need a system that can execute optimization decisions continuously, driven by real-time application behavior rather than static rules or manual triggers. 

[DORA's research](https://octopus.com/devops/metrics/dora-metrics/) supports this: teams that move toward autonomous, systematized execution consistently see improvements in both reliability and deployment frequency. The risk isn't in acting faster, it's in acting without application context. That's the distinction between brittle automation and safe autonomy.

Teams are already exploring more advanced[ FinOps tools](https://sedai.io/blog/top-finops-tools-for-engineering-leaders) to bridge this gap, but tools alone aren’t enough without a true execution layer.

## **What a Modern FinOps Operating Model Looks Like in 2026**

Modern FinOps isn't just about governance anymore it's about actually getting things done. The old approach of checking in & tweaking things every so often isn't enough. Now, organizations need systems that constantly adjust in real time, just like the cloud itself.

Forget waiting on tickets or approvals. Teams are moving toward closed-loop optimization, where decisions happen automatically within set boundaries. Cost, performance, & reliability aren't tradeoffs anymore; they're optimized together.

This matters most when you're dealing with dynamic cloud workloads. Static decisions, like rightsizing once & hoping it sticks, just don't make sense because things change fast. The real goal isn't to land on some mythical perfect configuration it's to keep your setup close to ideal as things shift.

Traditional tools like RACI can't keep up. You need an execution system that moves as fast as the cloud does.

## Closing the Execution Gap with Application-Aware Autonomy

RACI defines ownership clearly, but it doesn't ensure execution. Teams know what needs to be optimized, but acting on those decisions consistently in a live environment is still the bottleneck.

What's missing isn't more visibility or better recommendations. It's the ability to execute optimization decisions continuously, with full awareness of how the application behaves in production.

That requires a different kind of system, one that understands traffic patterns, service dependencies, and SLO boundaries, and can make changes safely as conditions evolve.

This is where a system like Sedai becomes necessary.

Sedai uses application-aware intelligence to understand how systems behave in real time. Instead of relying on static rules or isolated metrics, it continuously makes autonomous decisions based on workload behavior, while validating the impact of every change.

Each adjustment is incremental, auditable, and reversible, which allows optimization to happen without introducing production risk.

In production environments, this approach enables continuous execution at scale. At [Palo Alto Networks](https://sedai.io/blog/palo-alto-networks), for example, Sedai executed over 89,000 changes directly in production with zero incidents. This is what consistent, safe execution looks like in practice not recommendations, but real outcomes.

This shift changes how RACI operates in practice:

- Responsible: Execution is no longer manual. Sedai continuously executes optimization decisions based on real workload behavior, adjusting resource configurations, rightsizing requests, and retuning scaling policies autonomously, with safety verification at each step.
- Accountable: Teams move from tracking potential savings to realizing measurable cost and performance outcomes.
- Consulted: Reliability is built into every decision, reducing repeated validation cycles and approval delays.
- Informed: Every action is visible and auditable, maintaining full transparency and control.

In this model, optimization is no longer a backlog item. It becomes a continuous process that runs alongside production systems, keeping cost, performance, and reliability aligned as the environment evolves.

## **Real-World Example: RACI vs Autonomous Execution**

Here's how it played out before:

Someone spots a cost-saving idea. They put in a ticket. Engineering takes a look. SRE checks for risks. Approval drags on for days, sometimes weeks. By the time anyone does anything, the workload has shifted, or the ticket's forgotten.

Now, things work differently:

The system catches an optimization chance. It looks at the app context, figures out the impact, & rolls out small, safe changes all the time. No tickets piling up. No waiting around. No guessing. Just action.

It happens at the pace of the cloud, and production keeps running smoothly.

The most effective FinOps RACI models assign accountability at the service and team level — and back it up with autonomous execution so accountability translates to actual savings. [Book a demo](https://sedai.io/demo) to see how Sedai supports both.

## **Conclusion: RACI Is Necessary. But Not Sufficient**

RACI brought structure to FinOps, sure. But just having structure isn't enough to get results. What matters is execution.

In today's cloud world, you can't rely on tickets, approvals, or waiting for someone to step in. Optimization needs to run nonstop, all the time, inside clear boundaries.

The shift is straightforward: ownership alone isn't enough. Teams need systems that can execute decisions continuously, without relying on manual intervention or delayed approvals.

The next step for FinOps isn't more meetings or tighter coordination. It's about creating systems that carry out the intent automatically, so engineers can focus but still stay in control.

That's when cloud optimization starts to truly work.

## **FAQ**

### Is RACI still relevant in FinOps?

Yes, RACI is still essential for defining ownership & accountability.

But on its own, it only organizes work. It doesn't ensure that optimization actually happens.

### Why doesn't RACI ensure cost savings are realized?

Because RACI operates at a coordination level, not an execution level. It defines who should act but execution is still manual, slow, & often deprioritized against product work.

### How can teams optimize cloud costs without risking reliability?

By moving away from blind, metric-driven changes & toward application-aware decision-making. When systems understand workload behavior & SLO boundaries, optimizations can be applied safely & incrementally without introducing risk.

### What role does automation play in FinOps today?

Traditional automation is often too risky because it lacks context. The shift is toward safe autonomy where systems continuously execute optimizations with built-in safeguards, validation, & full control.

That's what enables teams to scale optimization without sacrificing reliability.

---

# How to Choose FinOps Tools in 2026
Source: https://sedai.io/blog/choosing-finops-tools

Most FinOps tool evaluations compare the wrong things. Learn the four questions that separate tools that report waste from tools that actually reduce it.

A FinOps tool that surfaces $2M in waste but requires 47 engineering tickets to act on it hasn't saved you anything. It's given you a longer backlog.

Most evaluations miss this entirely. They compare dashboards, savings estimates, & cloud provider coverage, none of which tells you whether the tool will actually reduce your bill. The questions that matter are the ones nobody asks during the demo.

This guide covers what to actually look for when choosing [FinOps tools](https://sedai.io/blog/top-finops-tools-for-engineering-leaders), starting with the questions most teams don't think to ask, then covering the standard criteria with an opinion on each, & finishing with a weighting framework you can adapt to your own situation.

**In this article:**

- [The Questions Most Teams Don't Ask](https://sedai.io/blog/choosing-finops-tools#:~:text=FAQs-,The%20Questions%20Most%20Teams%20Don%27t%20Ask,-These%20are%20the)
- [The Standard Evaluation Criteria (With an Opinion)](https://sedai.io/blog/choosing-finops-tools#:~:text=that%20sustains%20them.-,The%20Standard%20Evaluation%20Criteria%20%28With%20an%20Opinion%29,-The%20questions%20above)
- [A Weighting Framework for Your Situation](https://sedai.io/blog/choosing-finops-tools#:~:text=delivers%20sustained%20value.-,A%20Weighting%20Framework%20for%20Your%20Situation,-Not%20every%20criterion)
- [How Can Sedai Help You Choose FinOps Tools?](https://sedai.io/blog/choosing-finops-tools#:~:text=results%20over%20time.-,How%20Can%20Sedai%20Help%20You%20Choose%20FinOps%20Tools%3F,-The%20tools%20that)
- [FAQs](https://sedai.io/blog/choosing-finops-tools#:~:text=Sedai%20works.-,FAQs,-What%20is%20the)

## **The Questions Most Teams Don't Ask**

These are the questions that separate tools that look good in a demo from tools that deliver durable savings. If you ask nothing else during an evaluation, ask these.

### **Does it act, or does it recommend?**

Most FinOps tools generate recommendations. Fewer execute them. And the difference between the two is the difference between knowing you're wasting money and actually stopping.

When a tool recommends, someone has to review the recommendation, validate it won't break anything, create a ticket, assign it to an engineer, wait for a maintenance window, & deploy the change. That workflow takes days to weeks per recommendation. Multiply by the hundreds of recommendations a decent tool surfaces monthly, and you've built a backlog that never gets cleared.

Ask the vendor: what percentage of your recommendations get implemented by your customers, and in what timeframe? If they can't answer that, they've never measured the thing that actually matters.

Most FinOps tools tell you what to fix. [Book a demo ](https://sedai.io/demo)to see how Sedai fixes it, taking real-time autonomous action so your team spends time on strategy, not tickets.

### **How does it handle rollback?**

Every optimization carries risk. An instance gets rightsized, and latency spikes. An autoscaling threshold gets adjusted, and a traffic peak causes degradation. What happens next?

Tools that only recommend don't have an answer here, rollback is the engineer's problem. Tools that act on recommendations should have a clear rollback mechanism: automatic reversion when performance degrades, or at minimum a one-click path back to the previous configuration.

Ask specifically: if an optimization causes a performance regression, how fast does the system detect it, and what does it do? The answer reveals whether the tool was designed for production environments or for generating reports.

### **Does it understand your SLOs before making a change?**

This is the question that catches most vendors off guard. A rightsizing recommendation based on average CPU utilization looks great on paper. But if the workload spikes to 90% CPU during a 10-minute batch window every morning, rightsizing to the average will cause throttling.

Safe optimization requires knowing each workload's SLO boundaries, latency targets, error rate thresholds, availability requirements, before making a change. A tool that optimizes without this context is optimizing blind. Ask: does your platform know my SLOs, and does it factor them into every recommendation?

### **What happens to savings after six months?**

This question is almost never asked in evaluations, and it resonates immediately with anyone who's been burned by a tool that looked great in a demo.

Workloads change. New services get deployed. Traffic patterns shift. A rightsizing exercise that saved 30% in January may have quietly eroded to 10% by June because the workload profile has drifted. Ask the vendor: how does your tool handle savings decay? Does it re-evaluate continuously, or does it optimize once and move on? If the answer is a periodic re-scan, monthly, quarterly, that's a tool that captures savings at a point in time, not a tool that sustains them.

## **The Standard Evaluation Criteria (With an Opinion)**

The questions above filter out tools that won't deliver durable results. For the tools that pass, these are the standard criteria buyers expect, with a point of view on what actually matters at each level.

The FinOps Foundation's 2025 State of FinOps report found that the top challenge cited by practitioners, for the third consecutive year, is getting engineers to take action on cloud cost data. That finding should shape how you weight every criterion below. A tool that scores well on coverage and dashboards but poorly on execution capability is solving the wrong problem.

For a broader comparison of what's available, see our guide to[ FinOps platforms and tools](https://sedai.io/blog/18-essential-finops-platforms-and-tools).

### **Coverage**

Which clouds does the tool support, and which service types does it cover? Most tools handle AWS, Azure, & GCP compute. Fewer cover Kubernetes-native attribution, storage optimization, or data transfer analysis. Determine where your spend concentrates and verify the tool covers those specific services, broad provider coverage means nothing if it doesn't go deep on the categories driving your bill.

### **Workflow integration**

A tool that requires a separate workflow to act on its findings creates friction. Look for integration with how your team actually works, IaC pipelines (Terraform, Pulumi), ITSM systems (Jira, ServiceNow), & communication tools (Slack, Teams). The question isn't whether integrations exist. It's whether the tool fits into your existing process or requires a process change.

### **Pricing model**

How does the tool's cost scale as your environment grows? Some tools price by managed spend (a percentage of your cloud bill), others by node count, cluster count, or flat subscription. Each model has different scaling characteristics. A spend-percentage model that costs $5K/month at $500K monthly spend becomes $50K/month at $5M, which may exceed the savings the tool generates. Model the cost at 2x and 5x your current scale before committing.

### **Time to value**

What does onboarding actually look like, and what does "working" mean at 30, 60, & 90 days? Some tools deliver visibility within hours but take months before they're trusted enough to act on recommendations. Others require weeks of data collection before producing any output. Ask for a specific timeline with milestones, not a vague "quick setup" claim. The faster a tool gets to its first validated action, not its first dashboard, the more likely it delivers sustained value.

### FinOps Tool Selection Strategies That Separate Visibility From Execution

See how Sedai evaluates and executes cloud optimizations using SLO-aware automation, continuous re-evaluation, and autonomous remediation to eliminate savings decay and turn FinOps insights into sustained, production-safe cost reductions.

## **A Weighting Framework for Your Situation**

Not every criterion matters equally for every team. Here's how to prioritize based on what you're actually trying to solve:

**If speed to savings matters most** — weight execution capability and time to value highest. You need a tool that acts, not one that reports. Dashboards don't reduce bills; automated rightsizing and scaling adjustments do. De-prioritize breadth of coverage in favor of depth on the services driving your spend.

**If you're in a regulated environment** — weight governance, auditability, & rollback capability highest. Every change needs a paper trail. You need a tool that documents what it changed, why, & what the impact was, and can revert automatically if something goes wrong. Compliance teams will want to review the tool's decision logic before it touches production.

**If you're managing Kubernetes at scale** — weight SLO-awareness and workload-level optimization highest. Container workloads change behavior with every deployment. A tool that rightsizes based on last month's averages will miss the performance impact of this week's release. You need continuous re-evaluation that understands pod-level behavior, not just node-level cost.

For a comparison of tools that handle Kubernetes specifically, see our[ Kubernetes cost optimization tools](https://sedai.io/blog/kubernetes-cost-management-top-tools) guide.

### **What the weighting looks like in practice**

| Priority | Weight execution | Weight governance | Weight K8s depth |
| --- | --- | --- | --- |
| Acts vs. recommends | Critical | Important | Critical |
| Rollback mechanism | Important | Critical | Critical |
| SLO awareness | Important | Important | Critical |
| Continuous re-evaluation | Critical | Important | Critical |
| Audit trail | Nice to have | Critical | Important |
| Multi-cloud coverage | Situational | Important | Situational |
| Time to first action | Critical | Important | Important |

Regardless of which column you weight highest, "acts vs. recommends" and "continuous re-evaluation" appear in every one. Those two capabilities are what separate tools that deliver a one-time improvement from tools that sustain results over time.

Choosing FinOps tools means evaluating visibility, commitment optimization, and Kubernetes cost allocation separately then finding a platform that unifies them into action.[ Book a demo ](https://sedai.io/demo)to see how Sedai handles all three continuously.

## **How Can Sedai Help You Choose FinOps Tools?**

The tools that look best in demos are often the ones that show the biggest numbers. The tools that deliver durable savings are the ones that can act safely on what they see.

Sedai acts rather than recommends, it understands each workload's SLOs before making any change, reverts autonomously when performance degrades, & re-evaluates continuously so savings don't decay as workloads shift.

[KnowBe4](https://sedai.io/customers/knowbe4) used this approach to reach 98% autonomous optimization across their services, cutting cloud costs by 27% with over 1,100 autonomous actions per quarter. They achieved ROI in under five months, not because their visibility improved, but because the execution finally matched the insight.

If you've worked through the evaluation framework above and want to see what a tool that passes every question looks like in practice,[ see how Sedai works](https://sedai.io/platform).

## **FAQs**

### **What is the most important criterion when choosing a FinOps tool?**

Whether the tool acts or only recommends. Visibility and dashboards are table stakes in 2026, what separates tools that reduce your bill from tools that describe it is execution capability. Ask vendors what percentage of their recommendations get implemented by customers and in what timeframe.

### **How do you evaluate FinOps tools for Kubernetes?**

Focus on workload-level optimization, not just namespace-level cost reporting. The tool should understand pod behavior under load, factor in SLO boundaries before making changes, & re-evaluate continuously after every deployment. Static rightsizing recommendations based on averages will miss performance impacts from code changes and traffic shifts.

### **What is savings decay in FinOps?**

Savings decay is the gradual erosion of cost savings over time as workloads change. A rightsizing exercise that saved 30% in January may only be delivering 10% by June because new services were deployed, traffic patterns shifted, & resource configurations drifted. Tools that optimize once and move on can't sustain results; continuous re-evaluation is required.

### **Should I choose a FinOps platform or a point solution?**

It depends on where your spend concentrates. If 80% of your optimization opportunity is in compute and Kubernetes, a platform focused on workload-level execution will deliver more value than a broad FinOps suite with shallow coverage across many categories. Match the tool's depth to your highest-impact cost driver rather than selecting for breadth alone.

---

# Kubernetes Cost Visibility in 2026
Source: https://sedai.io/blog/kubernetes-visibility

Learn what the standard K8s cost visibility stack actually gives you, where it breaks, & how to turn visibility into safe action, including GPU workloads.

Most teams have dashboards. They can see total cluster spend, break it down by namespace, & generate monthly reports. What they can't see is whether it's safe to act on any of it.

You know a service is over-provisioned by 40%. But rightsizing it means answering questions your cost tooling doesn't touch, what happens to p99 latency during the next traffic spike, whether the downstream service can absorb the reduced headroom, & whether the workload's behavior has shifted since the last time anyone load-tested it. That's the work that keeps optimization recommendations sitting in a backlog for months.

This guide covers what the standard Kubernetes cost visibility stack actually gives you, where it stops short, & what's required to turn visibility into safe action, including the[ GPU](https://sedai.io/platform/gpu) visibility challenge that most cost tooling still doesn't address.

**In this article:**

- [The Standard Kubernetes Cost Visibility Stack](https://sedai.io/blog/kubernetes-visibility#:~:text=Kubernetes%20Cost%20Visibility%3F-,The%20Standard%20Kubernetes%20Cost%20Visibility%20Stack,-The%20instrumentation%20layer)
- [What Actionable Visibility Actually Requires](https://sedai.io/blog/kubernetes-visibility#:~:text=optimizing%20at%20all.-,What%20Actionable%20Visibility%20Actually%20Requires,-Knowing%20what%20a)
- [GPU Cost Visibility](https://sedai.io/blog/kubernetes-visibility#:~:text=Book%20a%20Demo-,GPU%20Cost%20Visibility,-GPU%20visibility%20is)
- [How Can Sedai Help You With Kubernetes Cost Visibility?](https://sedai.io/blog/kubernetes-visibility#:~:text=happens%20at%20all.-,How%20Can%20Sedai%20Help%20You%20With%20Kubernetes%20Cost%20Visibility%3F,-The%20pattern%20across)

## **The Standard Kubernetes Cost Visibility Stack**

The instrumentation layer most teams work with starts with Prometheus & kube-state-metrics, often extended with Kubecost or OpenCost for cost allocation. Each layer adds value, but each also carries assumptions that break under real-world conditions.

### **Prometheus + kube-state-metrics**

Prometheus scrapes resource metrics from your cluster. kube-state-metrics exposes the state of Kubernetes objects, including resource requests, limits, & pod status. Together, they give you the raw data to answer questions like "how much CPU did this namespace consume last week?"

Where this stops: Prometheus has no concept of price. It doesn't know that an `m5.large` in `us-east-1` costs $0.096/hour or that your Savings Plan gives you a 40% discount. It's excellent for performance monitoring but doesn't translate resource consumption into dollars without additional tooling.

### **Kubecost & OpenCost**

Kubecost (and its open-source foundation, OpenCost) bridge this gap by ingesting cloud billing data & mapping it to Kubernetes-native objects, namespaces, deployments, labels, & pods. This gives you cost-per-namespace, cost-per-deployment, & cost-per-label breakdowns that finance teams can work with. For a comparison of how different tools handle this, see our guide on [Kubernetes cost optimization tools](https://sedai.io/blog/kubernetes-cost-management-top-tools).

Where this breaks: Kubecost splits node cost across pods using an allocation model based on resource requests. When pods don't have requests set, which is common in dev & staging namespaces, the allocation becomes an estimate, not a measurement. Under bin-packing or variable workload patterns, these estimates can diverge
 significantly from actual consumption.

### **The Node-Sharing Problem**

When multiple workloads share a node, cost attribution is inherently an allocation model. Two pods on the same node, one CPU-bound & one memory-bound, share the same underlying hardware cost. How you split that cost depends on the allocation logic, by requests, by limits, by actual usage, & each method produces a different number. None of them is objectively "correct."

This matters because cost decisions downstream depend on these numbers being reliable. If your cost-per-namespace data is built on allocation estimates rather than measurements, every optimization decision based on that data carries inherited uncertainty.

### **Diagnosing Broken Attribution**

You can check whether your namespace attribution is reliable by surfacing pods without resource requests set, a leading indicator that Kubecost's cost allocation is estimating rather than measuring.

Run this to find pods in a namespace with no CPU requests:

`kubectl get pods -n <namespace> -o json | \`
  `jq '.items[] | select(.spec.containers[].resources.requests == null) | .metadata.name'`

And this PromQL query to check the ratio of requested CPU to allocatable CPU per namespace, namespaces where this ratio is near zero or missing entirely are the ones where cost attribution is unreliable:

*sum by (namespace) (kube\_pod\_container\_resource\_requests{resource="cpu", container\_id!=""})*

  */ sum by (namespace) (kube\_node\_status\_allocatable{resource="cpu"})*

# Surfaces namespaces where resource requests are unset,

## Making Cost Allocation an Estimate Rather Than a Measurement

Missing or inaccurate resource requests don't just affect scheduling — they corrupt the cost attribution data that every downstream optimization decision depends on.

You can verify the scale of the problem in your own cluster with two queries:

`sum by (namespace) (kube_pod_container_resource_requests{resource="cpu", container_id!=""})`

  `/ sum by (namespace) (kube_node_status_allocatable{resource="cpu"})`

Then check the ratio of requested CPU to allocatable CPU per namespace — namespaces with near-zero values are where cost data is unreliable:

*sum by (namespace) (kube\_pod\_container\_resource\_requests{resource="cpu", container\_id!=""})*

  */ sum by (namespace) (kube\_node\_status\_allocatable{resource="cpu"})*

If either of these returns results in your production namespaces, fix resource requests before trusting your cost data. Attribution built on incomplete requests produces misleading conclusions, & optimizing based on misleading data is worse than not optimizing at all.

## **What Actionable Visibility Actually Requires**

Knowing what a workload is consuming and what it costs is the starting point, not the destination. Safe action requires three things that standard observability tooling doesn't provide:

**Understanding workload behavior under load, not just at rest.** A service running at 30% average CPU utilization looks like a rightsizing candidate. But if that same service spikes to 85% during batch processing windows or during the first 10 minutes after a deployment, rightsizing to the average will cause throttling. You need utilization curves; p95 & p99 behavior over time; not averages.

**Knowing the SLO boundaries for each workload.** A cost tool can tell you that a service is over-provisioned. It can't tell you whether reducing its allocation will push latency past your p99 SLO. Actionable visibility requires knowing what "safe" means for each specific workload, & that definition varies by service, by traffic pattern, & by time of day.

**Understanding what a resource change will do to downstream services.** Rightsizing a frontend service might look safe in isolation. But if that service calls a backend that's already running at capacity, reducing the frontend's headroom can cascade into backend latency increases that the cost tool never sees.

This is where cost visibility tooling & observability tooling diverge. [Cost tools answer](https://sedai.io/blog/cloud-cost-visibility) "What does this cost?" Observability tools answer "How does this behave?" Neither answers the question that actually matters for optimization: "What happens if I change this?" 

That question requires both datasets combined with an understanding of workload relationships, something that manual analysis can approximate for a handful of services but can't sustain across a production environment with dozens or hundreds of workloads.

### Understand Kubernetes Cost Visibility

See how Sedai explains Kubernetes cost visibility in 2026 for clarity, control & cost efficiency.

## **GPU Cost Visibility**

GPU visibility is a different challenge entirely. The metrics exist, but interpreting them correctly requires understanding what each one actually measures, & why no single metric tells you whether a GPU workload is right-sized.

### **The DCGM Metrics That Matter**

NVIDIA's DCGM exporter surfaces GPU telemetry to Prometheus. The three metrics teams look at first:

- **`DCGM_FI_DEV_GPU_UTIL`** — Percentage of time the GPU was active. This is the metric most teams treat as "GPU utilization," but it's a coarse signal. A GPU can show 90% utilization while only using a fraction of its compute capacity if the workload is memory-bound.
- **`DCGM_FI_DEV_MEM_COPY_UTIL`** — Memory bandwidth utilization. High values indicate the workload is saturating memory transfers between GPU & host.
- **`DCGM_FI_DEV_FB_USED`** — Framebuffer memory consumed. This tells you how much GPU memory the workload has allocated, but not whether that allocation is efficient.

### **Why No Single Metric Tells the Full Story**

A GPU cluster can show high memory usage while compute cores sit nearly idle, meaning you're paying for compute capacity the workload isn't using. Conversely, high compute utilization with low memory bandwidth suggests a compute-bound workload that might benefit from a different GPU type rather than more memory.

The right-sizing question for GPU workloads requires combining these signals:

`# Memory pressure vs. compute utilization per workload`

`# High mem_copy_util + low gpu_util = memory-bound (candidate for MIG or smaller GPU)`

`# High gpu_activity + low mem_copy_util = compute-bound (different optimization path)`

`label_replace(`

  `avg by (pod, namespace) (DCGM_FI_DEV_MEM_COPY_UTIL),`

  `"signal", "memory_bandwidth", "", ""`

`)`

`or`

`label_replace(`

  `avg by (pod, namespace) (DCGM_FI_DEV_GPU_UTIL),`

  `"signal", "gpu_activity", "", ""`

`)`

This query surfaces the relationship between memory pressure & compute utilization per workload. Memory-bound workloads (`high mem_copy_util, low gpu_util`) are candidates for MIG partitioning or a smaller GPU type. Compute-bound workloads need a different optimization path entirely.

### **The MIG Visibility Problem**

Multi-Instance GPU (MIG) lets you partition a single GPU into isolated slices, each with dedicated compute & memory. This is powerful for right-sizing, instead of paying for a full A100 for a workload that only needs a fraction of its capacity, you partition the GPU & [run multiple workloads on it](https://sedai.io/blog/ai-driven-cloud-optimization-strategies).

The problem: standard DCGM exporters don't provide MIG-aware visibility out of the box. `DCGM_FI_DEV_GPU_UTIL` is incompatible with MIG entirely. For MIG-partitioned GPUs, you need `DCGM_FI_PROF_GR_ENGINE_ACTIVE` instead, which reports per-partition utilization but requires explicit configuration in your DCGM exporter's metrics CSV. Without this, your monitoring shows whole-GPU aggregates that mask per-slice inefficiency.

Getting slice-level cost attribution requires mapping each MIG instance to the pod that's using it, then associating that pod's resource consumption with its namespace & cost center. Most teams don't have this pipeline built, which means GPU cost attribution in MIG environments is a manual exercise, if it happens at all.

## **How Can Sedai Help You With Kubernetes Cost Visibility?**

The pattern across both standard K8s & GPU workloads is the same: teams can see the waste, but acting on it safely requires understanding workload behavior in ways that visibility tooling alone doesn't provide.

Sedai closes that loop. For standard Kubernetes workloads, Sedai learns each service's performance profile, including p95/p99 behavior, SLO boundaries, & downstream dependencies, & autonomously optimizes resource allocation based on that understanding. 

It doesn't just flag over-provisioned pods; it right-sizes them continuously while respecting the application's actual performance constraints. [Palo Alto Networks](https://sedai.io/customers/palo-alto-networks) runs over 89,000 production changes through Sedai with zero incidents, because the platform understands what's safe to change before changing it.

For GPU workloads, Sedai's proprietary GPU utilization model synthesizes the fragmented DCGM signals into a single true utilization score, solving the measurement problem that keeps most teams over-provisioning GPU capacity.

[See how Sedai turns visibility into safe, continuous action](https://sedai.io/platform).

## **FAQs**

### **What tools provide Kubernetes cost visibility?**

The standard stack is Prometheus with kube-state-metrics for resource data, & Kubecost or OpenCost for cost allocation. Kubecost maps cloud billing data to Kubernetes objects like namespaces, deployments, & pods. For GPU workloads, NVIDIA's DCGM exporter surfaces utilization metrics to Prometheus, though interpreting those metrics correctly requires combining multiple signals.

### **Why is Kubernetes cost attribution unreliable?**

Cost attribution breaks down when pods don't have resource requests set, Kubecost has to estimate allocation instead of measuring it. The node-sharing problem compounds this: when multiple workloads share a node, splitting cost depends on the allocation model used (by requests, limits, or usage), & each produces different numbers. Fixing resource requests across all namespaces is the prerequisite for reliable attribution.

### **How do you monitor GPU costs in Kubernetes?**

Deploy the NVIDIA DCGM exporter to surface GPU metrics to Prometheus. The key metrics are `DCGM_FI_DEV_GPU_UTIL` (compute activity), `DCGM_FI_DEV_MEM_COPY_UTIL` (memory bandwidth), & `DCGM_FI_DEV_FB_USED` (framebuffer memory). No single metric answers whether a GPU workload is right-sized, you need to combine compute & memory signals to determine whether the workload is memory-bound, compute-bound, or genuinely idle.

### **What is the difference between cost visibility & cost optimization in Kubernetes?**

Cost visibility tells you what you're spending & where. Cost optimization acts on that information, rightsizing pods, adjusting autoscaling, reclaiming idle resources. The challenge is that acting safely requires understanding workload behavior under load, SLO boundaries, & downstream dependencies, which visibility tooling doesn't provide. Teams that bridge this require either significant manual analysis or autonomous systems that learn workload behavior before making changes.

---

# Common Cloud Cost Management Mistakes in 2026
Source: https://sedai.io/blog/common-cloud-cost-management-mistakes

The cloud cost mistakes that hurt most look like correct behavior at the time. Learn five non-obvious errors — from average-based rightsizing to AI spend mismanagement — and how to fix them.

Your team rightsized everything last quarter. Savings looked great in the review. Three months later, costs are back where they started, & nobody can explain why.

The expensive cloud cost management mistakes aren't over-provisioned instances or missing tags. Those are visible & fixable. The ones that compound are the ones that look like correct behavior at the time, optimizing against the wrong signal, treating a one-time exercise as a strategy, & applying frameworks built for predictable workloads to AI infrastructure that doesn't behave predictably.

Most cost visibility tools show you the problem. [Book a demo](https://sedai.io/demo) to see how Sedai fixes it, taking autonomous action based on your actual workload behavior.

This guide covers five of them.

**In this article:**

- [Optimizing the Average, Not the Workload](https://sedai.io/blog/common-cloud-cost-management-mistakes#:~:text=FAQs-,Optimizing%20the%20Average%2C%20Not%20the%20Workload,-Rightsizing%20based%20on)
- [Treating All Environments Equally](https://sedai.io/blog/common-cloud-cost-management-mistakes#:~:text=around%20the%20clock.-,Treating%20All%20Environments%20Equally,-Dev%20and%20staging)
- [One-Time Optimization as a Strategy](https://sedai.io/blog/common-cloud-cost-management-mistakes#:~:text=control%20%26%20operational%20efficiency.-,One%2DTime%20Optimization%20as%20a%20Strategy,-Rightsizing%20exercises%20capture)
- [Confusing Cost Reduction With Cost Management](https://sedai.io/blog/common-cloud-cost-management-mistakes#:~:text=optimization%20framework.-,Confusing%20Cost%20Reduction%20With%20Cost%20Management,-Cutting%20spend%20during)
- [AI Workloads & the 2026 Problem](https://sedai.io/blog/common-cloud-cost-management-mistakes#:~:text=process%2C%20tooling%2C%20%26%20accountability.-,AI%20Workloads%20%26%20the%202026%20Problem,-Traditional%20cloud%20cost)
- [How Can Sedai Help You Avoid These Mistakes?](https://sedai.io/blog/common-cloud-cost-management-mistakes#:~:text=our%20optimization%20guide.-,How%20Can%20Sedai%20Help%20You%20Avoid%20These%20Mistakes%3F,-Every%20mistake%20in)
- [FAQs](https://sedai.io/blog/common-cloud-cost-management-mistakes#:~:text=from%20decaying.-,FAQs,-Why%20does%20average)

## **Optimizing the Average, Not the Workload**

Rightsizing based on average CPU or memory utilization is the most common starting point for cloud cost management. It's also incomplete.

A service running at 20% average CPU looks like it's overprovisioned by 80%. But pull the p95 utilization curve & you might see that same service spiking to 90% for 8 minutes every hour during batch processing. Rightsize to the average, & that batch job starts getting throttled, which doesn't show up as a cost problem. It shows up as a latency regression that takes three engineers two days to diagnose.

That spread between average & peak is where most rightsizing exercises create new problems. Average utilization tells you what a workload does most of the time. p95 & p99 tell you what it does when it matters most, during traffic spikes, batch windows, & deployment warmup periods.

### **How to identify dangerous gaps**

Look at any workload your team has flagged for rightsizing & compare the average utilization against the p95 over the same time window. If the p95 is more than 3x the average, that workload has spiky behavior that average-based rightsizing will miss. The wider that ratio, the higher the risk of performance impact from reducing allocation.

The fix isn't to avoid rightsizing. It's to rightsize against the right signal. Use p95 or p99 utilization as your baseline, not the mean. And factor in time-of-day patterns a service that's idle at 3 AM & maxed out at 11 AM needs different treatment than one with flat utilization around the clock.

## **Treating All Environments Equally**

Dev and staging environments accumulate waste because nobody is watching them with the same rigor applied to production. That's expected. The less obvious version is worse: dev environments configured to mirror production "just in case," running at production prices for 10% of the load.

We see this pattern consistently. A team sets up staging to be an exact replica of prod, same instance types, same cluster sizes, same autoscaling policies, because they want confidence that deployments tested in staging will behave the same way in production. The intent is reasonable. The cost is not.

### **What environment-level waste looks like in billing data**

Filter your cost data by environment tag. In most organizations, non-production environments account for 25-40% of total cloud spend. Now compare that to the utilization data for those same environments. If dev & staging are running at under 20% utilization during business hours & near zero overnight, you're paying production prices for resources that are active a fraction of the time.

### **What right-sized non-prod environments look like**

Non-production environments don't need to match production's capacity. They need to match production's configuration fidelity at a fraction of the scale. Use smaller instance types, lower replica counts, & aggressive scale-to-zero policies for environments that sit idle overnight & on weekends. Scheduled shutdown policies that turn off non-prod clusters outside business hours can cut non-production costs by 60-70% with minimal impact on development velocity.

### Understand Cloud Cost Management Mistakes

See how Sedai explains cloud cost mistakes in 2026 for savings, control & operational efficiency.

## **One-Time Optimization as a Strategy**

Rightsizing exercises capture savings at a point in time. Six months later, the workload has changed, a new service has been deployed, traffic patterns have shifted, & the savings have quietly decayed.

This is savings decay — and it's the reason most organizations see their cloud costs drift back toward pre-optimization levels within two to three quarters of a one-time exercise.

### **How savings decay works**

Every deployment can change a workload's resource profile. A code change that adds a caching layer reduces CPU usage. A dependency update increases memory consumption. A new feature increases request volume. Each change shifts what "right-sized" means for that service, but the resource configuration stays frozen at whatever the last optimization exercise set.

The decay curve is predictable. In our experience, a one-time rightsizing exercise loses 30-50% of its savings within six months as workloads drift. By the time the next quarterly review comes around, many of the original recommendations are no longer valid, & the team has to start the analysis from scratch.

### **What continuous optimization requires**

Sustained savings require a system that re-evaluates resource configurations against live workload behavior after every deployment, not on a fixed schedule. That means monitoring how each service's resource consumption changes over time, detecting when a configuration has drifted from optimal, & adjusting before the waste compounds.

For a structured approach to building this practice, see our[ cloud cost optimization framework](https://sedai.io/blog/cloud-cost-optimization-strategies-practices).

## **Confusing Cost Reduction With Cost Management**

Cutting spend during a slow quarter feels like success. Costs go down, finance is happy, & the initiative gets reported as a win. But if those cuts weren't tied to workload performance & SLOs, if you just turned things off or downsized aggressively during a lull, nothing has changed structurally.

The next traffic spike arrives, resources get re-provisioned to handle the load, & costs are back where they started. Or worse: the aggressive rightsizing causes performance issues under load, & the team over-corrects by provisioning even more capacity than before.

The difference between a cost event & a cost practice is sustainability. A cost event is a one-time reduction that decays. A cost practice is an ongoing operational function that keeps costs aligned with workload requirements continuously, through traffic changes, deployments, & seasonal shifts.

Organizations that treat cost optimization as a quarterly project are running cost events. The ones that sustain results treat it the way they treat incident management or capacity planning, as a continuous practice with dedicated process, tooling, & accountability.

## **AI Workloads & the 2026 Problem**

Traditional cloud cost management logic assumes workloads behave predictably: you provision resources, they consume at a roughly consistent rate, & you optimize by matching allocation to actual usage. AI workloads break every part of that assumption.

### **Why traditional cost frameworks fail for AI**

Inference is non-deterministic. A single API call to an LLM can generate wildly different resource consumption depending on prompt length, context window, & model behavior. Agentic workflows compound this, one agent call can trigger multiple downstream calls, each with its own compute cost. The result is resource consumption that accumulates continuously rather than in predictable cycles, making traditional capacity planning unreliable.

Training workloads have the opposite problem: they're predictable but massive. GPU-intensive training runs can consume millions of dollars in compute over days or weeks, & the cost models for GPU infrastructure are fundamentally different from CPU-based pricing. Teams that budget for AI the way they budget for standard compute consistently undershoot.

### **The attribution problem**

Most organizations have cloud resource tags. Almost none have cost-per-inference visibility. You can see that your GPU cluster cost $200K last month, but you can't tell which model, which use case, or which team generated that spend. Without this attribution, managing AI costs the same way you manage VM costs is impossible, you're optimizing the infrastructure without understanding what's driving consumption.

### **Why this is a structural problem, not a planning failure**

[IDC projects](https://www.cio.com/article/4107377/cios-will-underestimate-ai-infrastructure-costs-by-30.html) that Global 1,000 companies will underestimate their AI infrastructure costs by 30% through 2027. This isn't because CIOs are bad at forecasting. It's because AI infrastructure costs don't follow the patterns that existing forecasting models were built for. Usage expands rapidly once models are deployed into business workflows, what starts as a single team's prototype often becomes a shared service across the company within months, with demand scaling 2-5x beyond original projections.

Savings decay compounds faster for AI workloads because model updates & changing inference patterns shift behavior more frequently than traditional application releases. A GPU configuration that was cost-optimal for last month's model version may be overprovisioned or underprovisioned for this month's.

For a deeper look at how[ cloud cost management & optimization](https://sedai.io/blog/cloud-cost-management-and-optimization-best-practices) practices need to evolve for these workloads, see our optimization guide.

## **How Can Sedai Help You Avoid These Mistakes?**

Every mistake in this guide has the same root cause: static configurations in a dynamic environment. Optimizing against averages, running one-time exercises, treating cost cuts as cost management, all of these fail because workloads change faster than manual review cycles can keep up with. Sedai addresses this directly. It optimizes against live application signals, p95/p99 behavior, SLO boundaries, & real traffic patterns, rather than utilization averages. And it re-evaluates continuously, so configurations don't drift back to waste after the initial exercise.

[Palo Alto Networks](https://sedai.io/customers/palo-alto-networks) runs over 89,000 production changes through Sedai with zero incidents, because each change is validated against the workload's actual performance requirements before it's applied. Their configurations stay current through every release, traffic shift, & infrastructure change, not because someone ran a quarterly review, but because the system adapts continuously.

Treating cloud cost management as a finance function rather than an engineering discipline is where most cost programs stall. [Book a demo](https://sedai.io/demo) to see how Sedai embeds continuous optimization into engineering workflows without adding process overhead.

If your team is running periodic optimization exercises that keep losing their impact,[ see how Sedai keeps savings from decaying](https://sedai.io/platform).

## **FAQs**

### **Why does average CPU utilization lead to bad rightsizing decisions?**

Average utilization masks spiky behavior. A service at 20% average can spike to 90% during batch processing or traffic peaks. Rightsizing to the average causes throttling during those spikes, which creates latency regressions that are harder to diagnose than the original waste problem. Use p95 or p99 utilization as the baseline instead.

### **What is savings decay in cloud cost management?**

Savings decay is the gradual erosion of optimization results as workloads change over time. A rightsizing exercise that saved 30% in January may deliver only 10-15% by June because deployments, traffic shifts, & new services have changed what "right-sized" means. Continuous re-evaluation is the only way to sustain results.

### **How should organizations manage AI infrastructure costs differently?**

AI workloads break traditional cost management assumptions because inference is non-deterministic, consumption scales unpredictably as adoption grows, & per-inference cost attribution barely exists in most organizations. Start by building cost-per-inference visibility before trying to optimize, you can't manage what you can't attribute to a specific model, team, or use case.

### **What is the difference between cost reduction & cost management?**

Cost reduction is a one-time event, cutting spend at a specific point. Cost management is an ongoing practice, continuously aligning resource allocation with workload requirements through traffic changes, deployments, & seasonal patterns. Reductions without sustained management decay within two to three quarters as workloads drift.

---

# Cloud Vendor Lock-in: Why Operational Dependency Is the Real Trap
Source: https://sedai.io/blog/vendor-lock-in

What is vendor lock-in in cloud computing? Learn the risks, architectural trade-offs, and strategies organizations use to reduce dependency without slowing innovation.

Switching cloud providers can cost less than you think. Rebuilding how your team operates costs a lot more.

Vendor lock-in in cloud computing is what makes leaving expensive: the proprietary APIs you've built on, the contracts you can't exit, & the operational knowledge your team has built for one platform.

Every migration estimate accounts for compute, storage, & dev hours to refactor proprietary services. Almost none account for rebuilding the runbooks, automation scripts, & incident playbooks that make infrastructure operational, not just deployed.

Table of Contents

- [Three Forms of Vendor Lock-in: One That Actually Compounds](https://sedai.io/blog/vendor-lock-in#:~:text=Operational%20Lock%2Din-,Three%20Forms%20of%20Vendor%20Lock%2Din%3A%20One%20That%20Actually%20Compounds,-Vendor%20lock%2Din)
- [What Operational Lock-in Actually Looks Like](https://sedai.io/blog/vendor-lock-in#:~:text=new%20cloud%20contract.-,What%20Operational%20Lock%2Din%20Actually%20Looks%20Like,-The%20term%20sounds)
- [How Multi-Cloud Adds to Operational Lock-in Challenges](https://sedai.io/blog/vendor-lock-in#:~:text=has%20no%20invoice.-,How%20Multi%2DCloud%20Adds%20to%20Operational%20Lock%2Din%20Challenges,-Multi%2Dcloud%20is)
- [The Lock-in That Doesn't Appear on Migration Estimates](https://sedai.io/blog/vendor-lock-in#:~:text=Book%20a%20Demo-,The%20Lock%2Din%20That%20Doesn%27t%20Appear%20on%20Migration%20Estimates,-At%2010%20services)
- [Portability Is the Wrong Problem to Optimize For](https://sedai.io/blog/vendor-lock-in#:~:text=to%20production%20scale.-,Portability%20Is%20the%20Wrong%20Problem%20to%20Optimize%20For,-Cloud%20providers%20have)
- [How Sedai Removes Operational Lock-in](https://sedai.io/blog/vendor-lock-in#:~:text=on%20manual%20operations.-,How%20Sedai%20Removes%20Operational%20Lock%2Din,-Lock%2Din%20is)

## **Three Forms of Vendor Lock-in: One That Actually Compounds**

Vendor lock-in shows up in three forms. Only one gets harder to escape the longer you wait.

- **Technical lock-in** is the most visible. Build on[ DynamoDB](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html),[ Lambda](https://aws.amazon.com/lambda/), or[ Azure Speech](https://azure.microsoft.com/en-us/products/ai-foundry/tools/speech), & you've committed to an architecture with no clean equivalent elsewhere. Migration means re-architecting, not redeploying. The cost is real, but you can estimate it beforehand.
- **Commercial lock-in** is the most quantifiable. Reserved Instances, Committed Use Discounts, & enterprise spend agreements carry exit costs you can calculate on a spreadsheet. When your architecture shifts, those commitments don't. You'll either eat the waste or negotiate a way out.
- **Operational lock-in** is the one that compounds silently. Your team's runbooks are written for one cloud's primitives. Your automation scripts assume one provider's API conventions. Your on-call playbooks route to engineers who know the exact nuances on one specific platform.

The first two have known, finite costs. Operational lock-in grows with your infrastructure, & the automation you build to manage it becomes the thing that keeps you on it. It doesn't appear on migration estimates, & it doesn't resolve when you sign a new cloud contract.

Fragmented dashboards and billing chaos across AWS, Azure, and GCP compound fast. [Book a demo](https://sedai.io/demo) to see how Sedai gives you a unified optimization layer across all three.

## **What Operational Lock-in Actually Looks Like**

The term sounds abstract. The reality isn't.

- **Runbook specificity** is the most immediate symptom. Your runbooks don't describe generic operations. They reference specific components: EC2 instance families, S3 bucket event patterns, & Azure NSG rule syntax. They can't be ported any more than the infrastructure can.
- **Automation assumptions** compound faster. Every script in your repo assumes one provider's API conventions, rate limits, & error response formats. Rewriting them isn't migration overhead. It's a rebuild.
- **Incident knowledge** is the hardest to see until it's gone. Your on-call engineers know which services degrade under load, which alerts are noise, & which escalation paths work. That knowledge takes years to build. It doesn't just transfer with a new cloud contract.

This is why operational lock-in has no clean exit. The other two forms have costs you can model. This one has no invoice.

## **How Multi-Cloud Adds to Operational Lock-in Challenges**

Multi-cloud is the industry's[ reflexive answer to lock-in risk](https://www.flexera.com/blog/finops/the-latest-cloud-computing-trends-flexera-2025-state-of-the-cloud-report/). Run workloads across AWS & Azure, maintain portability at the infrastructure layer, & no single provider holds you hostage.

The theory is sound. The practice is different.

Managing two clouds means separate networking models, separate security postures, separate[ cost optimization strategies](https://sedai.io/blog/multi-cloud-cost-management-strategies), & separate support contracts. None of that reduces your dependence on the team.

[27%, roughly $182 billion is wasted per year on cloud](https://www.spendark.com/blog/state-of-cloud-waste-2026/#:~:text=27%25%20is%20roughly%20%24182B). That is why most critical workloads stay on the primary cloud, because that's where the team's expertise is. The second environment becomes technically present, underused, & bills you regardless.

Operational lock-in doesn't split across clouds. It compounds. You're locked in to the engineers who know how to operate all of it. That creates a different kind of dependency: not on the provider, but on the people who understand how to keep everything running on that specific provider. When those engineers leave, they take years of platform-specific context with them.

Engineers leave. Cloud services don't.

### Sedai Avoids Vendor Lock-In For You.

See how Sedai helps you stay flexible and reduce cloud dependency risks for your infrastructure. Safely.

## **The Lock-in That Doesn't Appear on Migration Estimates**

At 10 services, a skilled SRE can manage configuration drift, tune performance, & keep costs close to optimal.

Runbooks exist. Alerts are calibrated. The team has context. It kind of makes sense.

At 100 services, or two clouds, that equation breaks. The same manual approach now requires a linearly growing team or deprioritized optimization work.

Cloud complexity undermines[ cloud ROI](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/in-search-of-cloud-value-can-generative-ai-transform-cloud-roi), not because cloud is expensive, but because operational overhead scales faster than the team does.

Your infrastructure keeps needing more: more tuning, more context specific to one platform's primitives, more logic that only works in one environment. No migration solves that. Cloud-native tooling doesn't eliminate it either, because those tools still require engineers to write & maintain the logic when conditions change.

Automation executes what you tell it to. **Autonomy** decides what needs to be done.[ That distinction matters more the closer you get to production scale.](https://sedai.io/blog/automated-vs-autonomous-why-the-difference-matters-for-modern-cloud-operations)

## **Portability Is the Wrong Problem to Optimize For**

Cloud providers have gotten good at egress credits, migration tooling, & compatibility layers. If you needed to move workloads tomorrow, you probably could.

What you can't move is operational knowledge. The runbooks. The escalation matrices.

The engineer who knows why that one Lambda function has a 10-second timeout, nobody is allowed to touch. That knowledge lives in people, not infrastructure, & it grows more entrenched every quarter your team operates the same way.

The real escape from vendor lock-in isn't a second cloud. It's reducing your infrastructure's dependence on manual operations.

## **How Sedai Removes Operational Lock-in**

Lock-in is an architectural risk, but inefficiency is a bigger one. Most conversations about cloud portability focus on escaping provider APIs, but the harder trap is the operational dependency that builds up quietly underneath: the runbooks written for one cloud, the scripts built around one provider's primitives, the team context that doesn't transfer.

That's where the real cost lives. And it's why solving for lock-in at the infrastructure layer alone isn't enough.

Application-aware optimization breaks both problems at once, because it reads workload behavior, not platform-specific APIs or metrics. It doesn't matter which cloud the workload is running on. What matters is how it's behaving: latency, errors, traffic, saturation. Those golden signals are universal, and they're what Sedai's decision engine acts on.

Multi-cloud complexity grows as you add providers. [Book a demo](https://sedai.io/demo) to see how Sedai gives you a single optimization layer across AWS, Azure, and GCP so operational overhead doesn't scale with your cloud footprint.

With[ over 100,000 autonomous operations in production & zero incidents](https://tfir.io/the-self-driving-cloud-is-real-sedai-has-run-100k-ops-without-incident/), Sedai's autonomous management layer operates across[ AWS](https://aws.amazon.com/),[ Azure](https://azure.microsoft.com/en-in),[ GCP](https://cloud.google.com/), &[ OCI](https://www.oracle.com/in/cloud/) on the same deterministic engine, no playbooks tuned to one cloud's behavior, no manual intervention required. The system decides, adjusts, & acts at[ whatever level of autonomy](https://sedai.io/blog/autonomous-cloud-spectrum) your team is ready to hand over.

The portability, then, isn't just at the infrastructure layer. It's at the operational layer — which is where the inefficiency has always compounded. The operational knowledge stays with the people who should be using it on higher-value work. The routine decisions run themselves.

---

# AWS EC2 Cost Optimization: Tips, Tools, & Best Practices
Source: https://sedai.io/blog/ec2-cost-optimization

Master EC2 cost optimization with advanced strategies, tools, and frameworks. Learn how to reduce costs without risking performance or reliability.

Well, you already know which EC2 instances are over-provisioned. That's not why your bill is still high.

AWS Cost Explorer shows you the waste. Compute Optimizer tells you which instance families to move to. A half-dozen FinOps tools will generate a ranked rightsizing list before your next meeting. The knowledge problem was solved years ago.

The execution problem wasn't. Engineers know the right move & don't make it, because the cost of a production incident is higher than the cost of over-provisioning. Padding compute is the rational call when your team gets paged at 2 a.m. for a latency spike, not when you're sitting in a budget review. It's systemic:[ 84% of organizations report that managing cloud spend is their top cloud challenge](https://www.flexera.com/about-us/press-center/new-flexera-report-finds-84-percent-of-organizations-struggle-to-manage-cloud-spend#:~:text=84%25%20of%20Organizations%20Struggle%20to%20Manage%20Cloud%20Spend), not because they lack visibility, but because translating insight into safe action is a fundamentally different problem. No checklist changes that calculus. A different kind of system does.

EC2 rightsizing works when it happens continuously, not quarterly. [Book a demo](https://sedai.io/demo) to see how Sedai keeps your instances right-sized based on real workload behavior.

In this article, we will cover:

- [Why the Standard Advice Breaks Down in Practice](https://sedai.io/blog/ec2-cost-optimization#:~:text=Data%20Behind%20It-,Why%20the%20Standard%20Advice%20Breaks%20Down%20in%20Practice,-The%20EC2%20cost)
- [EC2 Cost Optimization Tips That Actually Work](https://sedai.io/blog/ec2-cost-optimization#:~:text=optimization%20teams.-,EC2%20Cost%20Optimization%20Tips%20That%20Actually%20Work,-These%20tips%20are)
- [The Tools You're Probably Already Using & Their Limits](https://sedai.io/blog/ec2-cost-optimization#:~:text=The%20Tools%20You%27re%20Probably%20Already%20Using%20%28%26%20Their%20Limits%29)
- [Best Practices for Production-Safe Optimization](https://sedai.io/blog/ec2-cost-optimization#:~:text=to%20fix%20it.-,Best%20Practices%20for%20Production%2DSafe%20Optimization,-The%20difference%20between)
- [What This Looks Like When It Goes Wrong](https://sedai.io/blog/ec2-cost-optimization#:~:text=Book%20a%20Demo-,What%20This%20Looks%20Like%20When%20It%20Goes%20Wrong,-A%20mid%2Dsized)
- [What Closes the Execution Gap](https://sedai.io/blog/ec2-cost-optimization#:~:text=in%20one%20sentence.-,What%20Closes%20the%20Execution%20Gap,-The%20real%20constraint)
- [The Tips Are Fine. The Problem Is Execution & the Data Behind It](https://sedai.io/blog/ec2-cost-optimization#:~:text=The%20Tips%20Are%20Fine.%20The%20Problem%20Is%20Execution%20%26%20the%20Data%20Behind%20It.)

## **Why the Standard Advice Breaks Down in Practice**

The EC2 cost optimization playbook is correct: Reserved Instances for predictable workloads, rightsizing for bloated instances, Spot for interruptible jobs, & cleanup for idle resources. The advice is sound. Execution is where it falls apart.

Rightsizing requires confidence that downsizing an instance won't spike latency or fail a health check during the next traffic surge. Most engineering teams don't have per-service observability sharp enough to make that call reliably. So the recommendations sit in dashboards for months.[ FinOps Foundation's 2024 State of FinOps](https://www.finops.org/insights/key-priorities-shift-in-2024/#:~:text=7.%20Humans%20are%20still%20in%20the%20loop%20for%20most%20automation) found that for most organizations, humans are still taking these actions manually. Reserved Instances & Savings Plans lock you into[ 1-year or 3-year commitments that cannot be cancelled](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-reserved-instances.html#:~:text=You%20can%20purchase%20a%20Reserved%20Instance%20for%20a%20one%2Dyear%20or%20three%2Dyear%20commitment%2C%20with%20the%20three%2Dyear%20commitment%20offering%20a%20bigger%20discount.). When your architecture shifts, you're locked into commitments that no longer fit. The savings evaporate. The commitment doesn't.

Spot Instances can[ cut compute costs by up to 90% versus On-Demand pricing](https://aws.amazon.com/ec2/spot/#:~:text=fault%2Dtolerant%20workloads%20for%20up%20to%2090%25%20off), but only for workloads built to handle interruptions. Most production services weren't architected that way, which is why Spot adoption stays concentrated in batch workloads & dev environments.[ Learn more about the Spot strategy for EC2](https://sedai.io/blog/optimizing-spot-instances-in-aws).

The problem isn't the advice. It's the execution gap: the distance between what a tool recommends & what an engineer can safely act on, alone, in production, without a rollback plan ready.[ The FinOps Foundation identifies this as the defining challenge for cloud optimization teams](https://www.finops.org/wg/encouraging-engineers-to-take-action/#:~:text=FinOps%20practitioners%20are%20encountering%20difficulty%20in%20getting%20engineers%20to%20act%20on%20their%20cost%20optimization%20recommendations).

## **EC2 Cost Optimization Tips That Actually Work**

These tips are valid. The challenge isn't knowing them. It's acting on them without breaking something.

1. **Right-size by workload type, not average utilization:** Average CPU over 30 days masks spikes. A service running at 15% average can burst to 90% for 10 minutes at peak. Sizing decisions made on averages create incidents at peaks.
2. **Move eligible workloads to Graviton:**[ AWS Graviton-based instances cost up to 20% less than comparable x86 instances](https://aws.amazon.com/ec2/graviton/#:~:text=of%20cloud%20workloads-,Optimize%20cost,-AWS%20Graviton%2Dbased) for compute-intensive services, containerized APIs, & in-memory caches. Migration effort is low if you're already in containers.
3. **Tune Auto Scaling policies per service, not per fleet:** A single policy across a heterogeneous fleet underserves some services & overserves others. Cooldown periods, target tracking metrics, & scale-in thresholds should reflect how each service actually behaves under load.
4. **Prioritize Spot for stateless, fault-tolerant workloads first:** CI/CD pipelines, batch jobs, data processing, dev/test environments: these absorb interruptions without application changes. Don't start with production APIs.
5. **Match RI & Savings Plan coverage to your known minimum, not your average:** Commit on the floor; the compute you'll run regardless of traffic patterns. Leave burst capacity on On-Demand. Over-committing on variable workloads turns savings plans into liabilities.

The bottleneck is never the tip. It's the confidence to act on it in a live environment.

## **The Tools You're Probably Already Using (& Their Limits)**

[**AWS Cost Explorer**](https://aws.amazon.com/aws-cost-management/aws-cost-explorer/) shows historical spend & RI utilization: the right starting point for identifying where money is going, not for deciding what to do about it.

[**AWS Compute Optimizer**](https://aws.amazon.com/compute-optimizer/) generates rightsizing recommendations from CloudWatch metrics. The gap exists. Static snapshots with no awareness of application behavior, traffic seasonality, or downstream dependencies. Acting on them still requires judgment.

[**CloudHealth**](https://aws.amazon.com/marketplace/pp/prodview-btyciyjmdewhm) & **Apptio Cloudability** give FinOps teams visibility, tagging enforcement, & chargeback reporting across multi-cloud. Strong on governance, weak on remediation. *Spot.io* handles Spot lifecycle well: the right tool if Spot migration is your primary lever.

The pattern across all of these: visibility is strong, execution is weak. They surface the problem. Someone still has to fix it.

## **Best Practices for Production-Safe Optimization**

The difference between optimization that works & optimization that creates incidents is how changes are made, not which.

Bind every optimization decision to SLOs. Make changes one service at a time, at low-traffic windows, with a *rollback* trigger defined before you start. Validate continuously for 24–48 hours after any compute change, not a one-time health check at deployment.

### Sedai Optimizes EC2 Costs For You.

See how Sedai autonomously reduces AWS EC2 costs and improves efficiency across your cloud infrastructure. Safely.

## **What This Looks Like When It Goes Wrong**

A mid-sized e-commerce platform runs Compute Optimizer weekly. One recommendation: downsize a fleet of m5.xlarge instances running a product recommendation service to m5.large. Average CPU: 18%. Confidence: high.

The team acts on it on Wednesday afternoon. By Thursday morning, p99 latency climbs from 120ms to 380ms. The service wasn't CPU-bound. It was memory-bound during recommendation model inference at peak catalog load. Compute Optimizer had no visibility into that. Rollback takes four hours. The incident review concludes: "The recommendation was correct based on available data." That's the problem in one sentence.

## **What Closes the Execution Gap**

The real constraint isn't knowing which instances to downsize. It's knowing whether this specific service, at this specific moment, can absorb that change without degrading latency or triggering cascading failures.

Conventional automation doesn't solve this. A script that downsizes instances when CPU drops below 30% is context-free: it treats a batch job & a latency-sensitive payment API the same way. That's not cost optimization. That's how an optimization tool creates an incident.

What closes the gap is **application-aware autonomy**: observing how a service actually behaves (latency, error rates, traffic patterns, saturation) before acting, & bounding every change within SLO constraints.[ See how AI-powered rightsizing works for EC2 VMs](https://sedai.io/blog/introducing-ai-powered-rightsizing-for-aws-ec2-vms).

This is the core distinction between *automation* & **autonomy**. Automation executes what you tell it. Autonomy decides what needs to be done, reasons about downstream risk, & acts incrementally: small, staged, reversible changes rather than one-shot right-sizings. When a system is making autonomous changes to production infrastructure, you cannot tolerate hallucinations. That's why the decision engine is deterministic, not probabilistic. **No LLMs in the loop**.

Sedai applies this across EC2 workloads: analyzing golden signals before any action, staging changes gradually, running continuous safety checks, & backing off automatically if behavior deviates. The same logic extends to autoscaling policy tuning.[ How to optimize Auto Scaling in EC2](https://sedai.io/blog/autoscaling-in-ec2).

[KnowBe4](https://sedai.io/blog/knowbe4#:~:text=How%20KnowBe4%20Saved%20%241.2M%20on%20AWS%20Despite%20Rapid%20Growth) cut AWS costs by 27% & saved over $1.2M using this approach, while the platform was still scaling.

## **The Tips Are Fine. The Problem Is Execution & the Data Behind It**

EC2 cost optimization best practices are well-documented & widely understood. But there's a problem that comes before execution: most of them are built on the wrong signal.

Rightsizing EC2 based on average CPU ignores how instances actually behave in production. A workload that looks underutilized on paper can behave very differently under real conditions. Safe optimization requires application context: latency, errors, traffic patterns, & saturation, not just a utilization percentage sampled over time.

This is why engineers who follow the standard playbook still end up with reliability incidents, unexpected costs, or both. The information isn't wrong; it's incomplete.

When rightsizing, scheduling, and autonomous optimization operate together, EC2 becomes a continuously self-correcting system. [Book a demo](https://sedai.io/demo) to see how Sedai combines all three across your EC2 fleet.

The second problem is execution. Even teams that understand this nuance can't act on it continuously and at scale. Every change requires human judgment, risk assessment, & sign-off. The backlog of optimization opportunities grows faster than any team can clear it.

The shift, then, is twofold: from incomplete signals to application-aware context, & from knowing what to do but not doing it, to a system that acts on it, bounded by your reliability constraints, at a scale no team can match manually.

That's not a better checklist. That's a[ self-driving cloud™](https://sedai.io/).

---

# ECS Autoscaling Mistakes to Avoid in 2026
Source: https://sedai.io/blog/ecs-autoscaling-mistakes-to-avoid-in-2026

Avoid the most common ECS autoscaling mistakes that lead to instability, cost overruns, and performance regressions. A deep technical guide for SREs and cloud architects.

ECS autoscaling should handle traffic changes without you thinking about it. In practice, most teams end up babysitting their scaling policies, overprovisioning to avoid incidents, or discovering cost spikes weeks after they started. The root cause is rarely a misconfiguration — more often, teams are applying static scaling rules to workloads that change behavior with every release.

This guide covers the most common [ECS](https://sedai.io/platform/ecs) autoscaling mistakes seen in production & how to fix them before they become expensive.

**In this article:**

- [How Does ECS Autoscaling Work?](https://sedai.io/blog/ecs-autoscaling-mistakes-to-avoid-in-2026#:~:text=FAQs-,How%20Does%20ECS%20Autoscaling%20Work%3F,-ECS%20autoscaling%20operates)
- [Common ECS Autoscaling Mistakes](https://sedai.io/blog/ecs-autoscaling-mistakes-to-avoid-in-2026#:~:text=real%2Dtime%20demand.-,Common%20ECS%20Autoscaling%20Mistakes,-Scaling%20on%20the)
- [Why ECS Autoscaling Fails Under Real-World Conditions](https://sedai.io/blog/ecs-autoscaling-mistakes-to-avoid-in-2026#:~:text=Book%20a%20Demo-,Why%20ECS%20Autoscaling%20Fails%20Under%20Real%2DWorld%20Conditions,-The%20mistakes%20above)
- [How to Fix Your ECS Autoscaling Configuration](https://sedai.io/blog/ecs-autoscaling-mistakes-to-avoid-in-2026#:~:text=hundreds%20of%20services.-,How%20to%20Fix%20Your%20ECS%20Autoscaling%20Configuration,-If%20your%20ECS)
- [How Can Sedai Help You With ECS Autoscaling?](https://sedai.io/blog/ecs-autoscaling-mistakes-to-avoid-in-2026#:~:text=quarterly%20review%20cycle.-,How%20Can%20Sedai%20Help%20You%20With%20ECS%20Autoscaling%3F,-The%20core%20challenge)
- [FAQs](https://sedai.io/blog/ecs-autoscaling-mistakes-to-avoid-in-2026#:~:text=handles%20it.-,FAQs,-What%20is%20the)

## **How Does ECS Autoscaling Work?**

ECS autoscaling operates at two layers, and most scaling problems come from misunderstanding how they interact.

**Service autoscaling** adjusts the desired task count for an ECS service based on CloudWatch metrics. You configure it through Application Auto Scaling with one of three policy types:

- **Target tracking** maintains a specific metric value (e.g., keep average CPU at 60%). ECS adds or removes tasks to hold that target.
- **Step scaling** triggers discrete task count changes at defined thresholds, giving you more control over how aggressively the service responds.
- **Scheduled scaling** adjusts min/max capacity on a fixed schedule for predictable traffic patterns like business hours or seasonal peaks.

**Cluster autoscaling** manages EC2 instances through capacity providers. When ECS can't place new tasks because the cluster is full, the capacity provider signals the underlying Auto Scaling Group to add instances. This layer only reacts to placement failures — it does not increase your service's desired count.

One detail that catches teams off guard: ECS publishes metrics to CloudWatch at one-minute intervals. The autoscaler then needs additional time to evaluate the alarm, calculate the response, & provision new tasks. That end-to-end lag means scaling decisions always trail real-time demand.

ECS cost optimization requires more than scaling policies. [Book a demo](https://sedai.io/demo) to see how Sedai manages task rightsizing, spot usage, and idle resource cleanup in one platform.

## **Common ECS Autoscaling Mistakes**

### **Scaling on the Wrong Metric**

The most frequent mistake is scaling on CPU when the workload is memory-bound, or vice versa. CPU and memory utilization are the default CloudWatch metrics, and most teams pick one without load testing first to determine which resource actually saturates under pressure.

Consider a service that processes file uploads. Memory grows with each concurrent upload, but CPU stays relatively flat. A CPU-based scaling policy would never trigger, even as the service runs out of memory and starts killing tasks. The fix is straightforward but often skipped: run a load test, identify which resource depletes first, & scale on that metric.

For services that consume both resources unevenly, configure dual target tracking policies. ECS handles the interaction correctly. It scales out when either metric breaches the target but only scales in when both metrics agree it's safe.

### **Ignoring Cooldown Periods**

Short cooldown periods cause scaling oscillation. The service scales out, metrics drop, it immediately scales back in, metrics spike again, & the cycle repeats. We see this most often with scale-in cooldowns set below 300 seconds: short enough that the service sheds capacity before the traffic lull is confirmed.

A good starting point: keep scale-out cooldowns short (60 seconds) so you respond quickly to traffic increases, and keep scale-in cooldowns longer (300 to 600 seconds) so you don't prematurely shed capacity during temporary lulls. These values should be tuned based on your traffic patterns, not left at defaults.

### **Setting Capacity Limits Too Tight**

Teams frequently set max capacity too low to "control costs" and end up throttling their own services during traffic spikes. The better cost control is a well-tuned scaling policy combined with reasonable maximums,  not an artificially low ceiling that causes outages.

On the other end, setting min capacity too low (especially to 1) means a single task failure takes down the service entirely. For anything production-critical, a minimum of 2 tasks across availability zones provides basic fault tolerance.

### **Confusing Service Scaling With Cluster Scaling**

This is one of the most common misunderstandings. Capacity providers manage EC2 instances. Service autoscaling manages task counts. They serve different purposes, and one does not replace the other.

We see teams configure capacity providers and assume their services will scale with demand. They won't. Capacity providers only respond when ECS can't place tasks. If your service desired count never changes, the cluster never needs to grow.

You need both layers configured: service autoscaling to adjust task counts based on load, and capacity providers to ensure the cluster has room for those tasks.

### Understand ECS Autoscaling Mistakes

See how Sedai explains ECS autoscaling mistakes in 2026 for scale, control & operational efficiency.

## **Why ECS Autoscaling Fails Under Real-World Conditions**

The mistakes above are configuration issues. The deeper problem is that ECS autoscaling was designed around static thresholds, and real workloads aren't static.

### **Releases Change Resource Profiles**

Every deployment can shift how your service consumes resources. A code change that introduces a new caching layer might cut CPU usage by 30%, making your existing scaling thresholds too aggressive. A dependency upgrade might increase memory consumption per request, causing memory-based policies to trigger earlier than expected.

Most teams don't re-evaluate their scaling policies after every release. The result is a gradual drift between what the autoscaler expects and how the application actually behaves. Over weeks and months, this drift compounds into either chronic overprovisioning (wasting budget) or underprovisioning (degrading performance).

### **CloudWatch Lag Creates Blind Spots**

ECS reports metrics to CloudWatch once per minute. The autoscaler evaluates those metrics, waits for alarm thresholds to breach, applies cooldown logic, & then initiates task changes. End to end, you're looking at 2 to 5 minutes — an eternity for services with sub-second latency requirements.

The standard workaround is to overprovision (run more baseline capacity than you need), which keeps the service safe but quietly inflates costs.[ Mis-sized containers & oversized resources account for roughly 8 to 12% of unnecessary cloud spend](https://sedai.io/blog/autoscaling-in-ecs) across most organizations running ECS.

### **Deployments Disrupt Scaling Behavior**

AWS suspends scale-in during ECS deployments but allows scale-out to continue. That asymmetry creates a predictable cost spike with every release.

During a rolling deployment with the default **minimumHealthyPercent: 100** and **maximumPercent: 200**, ECS temporarily runs both old & new task sets simultaneously. As a result, the task count can effectively double during the switchover. That spike in running tasks can trigger capacity provider scale-out at the cluster level — launching EC2 instances you don't actually need for steady-state traffic.

After deployment completes, the excess capacity lingers until scale-in cooldowns expire. If you deploy multiple times per day, the overprovisioning window never fully closes. Blue/green deployments amplify this further because the entire replacement task set runs alongside the original until traffic is switched.

The practitioner fix is to lower your capacity provider target capacity to 80-90% so the cluster has headroom to absorb deployment spikes without launching new instances. You can also extend scale-in cooldowns during deployment windows or use deployment circuit breakers to limit how long the overlap persists. But these are manual guardrails — they don't adapt to how each service's resource profile changes from release to release.

### **Single-Metric Scaling Misses Compound Problems**

Real performance degradation is rarely caused by one metric crossing a threshold. A latency-sensitive payment service might show normal CPU & memory utilization while response times climb because of I/O contention, connection pool exhaustion, or downstream dependency slowdowns. Scaling on CPU or memory in that scenario adds tasks that don't address the bottleneck — increasing cost without improving performance.

The native ECS autoscaling framework supports custom CloudWatch metrics, which partially addresses this. But building, publishing, & maintaining custom metrics for every service is operational overhead that scales poorly across dozens or hundreds of services.

ECS autoscaling mistakes compound quietly into higher bills and destabilized services.[ Book a demo](https://sedai.io/demo) to see how Sedai prevents misconfigured scaling thresholds, missing cooldowns, and stateful scale-in events before they cause problems.

## **How to Fix Your ECS Autoscaling Configuration**

If your ECS autoscaling is underperforming, start with these steps:

- **Load test every service individually.** Identify which resource (CPU, memory, or a custom metric) saturates first under realistic traffic. Scale on that metric.
- **Audit cooldown periods.** Scale-out cooldowns should be 60 seconds or less. Scale-in cooldowns should be 300 seconds or more. Adjust based on your service's startup time and traffic volatility.
- **Set capacity limits that match reality.** Max capacity should accommodate your highest realistic traffic peak with headroom. Min capacity should keep services fault-tolerant across availability zones.
- **Use dual target tracking for mixed workloads.** If your service consumes both CPU and memory unevenly, configure separate target tracking policies for each. ECS coordinates them so scale-out is aggressive (either metric can trigger it) &scale-in is conservative (both must agree).
- **Combine scheduled scaling with target tracking.** If your traffic follows predictable patterns (business hours, weekly cycles, seasonal peaks), use scheduled scaling to pre-position capacity. Let target tracking handle the fine-tuning within those windows. For a deeper look at how autonomous optimization handles this continuously, see our guide on[ mastering autonomous optimization for Amazon ECS](https://www.sedai.io/blog/mastering-autonomous-optimization-for-amazon-ecs).
- **Revisit scaling policies after every major release.** Resource consumption profiles change with code changes. A scaling policy that was optimal three months ago may be overprovisioning or underprotecting today.

These steps will improve your autoscaling significantly. But they're still manual, periodic adjustments in an environment that changes continuously. The services that stay optimized are the ones where scaling adapts to workload behavior in real time, not on a quarterly review cycle.

## **How Can Sedai Help You With ECS Autoscaling?**

The core challenge with ECS autoscaling is that it reacts to symptoms, not workload behavior. Thresholds drift as traffic, releases, and infrastructure change, and keeping them tuned manually across services becomes unsustainable.

Sedai takes a different approach. It continuously learns how each service behaves and autonomously adjusts scaling, rightsizing, and policies based on real application data, balancing cost, performance, and availability as conditions evolve.

[Palo Alto Networks](https://sedai.io/customers/palo-alto-networks), running thousands of ECS services at scale, used this approach to execute over 89,000 production changes autonomously and save $3.5M in cloud costs. Their scaling configurations stay current without engineers manually re-tuning after each deployment.

If your team is spending time tuning ECS autoscaling policies that keep drifting, or overprovisioning because you can't afford the risk of getting it wrong,[ see how Sedai's platform handles it](https://sedai.io/platform).

## **FAQs**

### **What is the best ECS autoscaling metric for web services?**

For most HTTP-based services, ALB request count per target is more responsive than CPU utilization because it scales on actual demand rather than resource consumption. Combine it with a CPU target tracking policy as a safety net so the service also scales when compute-bound processing drives up utilization independent of request volume.

### **How do I prevent ECS scaling oscillation?**

Scaling oscillation happens when scale-in cooldowns are too short. Set scale-in cooldowns to at least 300 seconds & scale-out cooldowns to 60 seconds or less. If you use dual target tracking policies for CPU &memory, ECS will only scale in when both metrics agree, which naturally reduces oscillation without additional configuration.

### **Should I use ECS Fargate or EC2 for autoscaling?**

Fargate simplifies autoscaling because you skip cluster capacity management entirely. You only configure service autoscaling, and AWS handles the compute. The tradeoff is cost: Fargate is more expensive for steady, predictable workloads. A common pattern is to run baseline capacity on EC2 (or EC2 with Spot) & burst to Fargate for unpredictable spikes using[ mixed capacity provider strategies](https://sedai.io/blog/7-strategies-to-optimize-ecs-costs).

### **How does ECS autoscaling behave during deployments?**

AWS suspends scale-in during ECS deployments but allows scale-out to continue. Rolling deployments with default settings can temporarily double your running task count, triggering unnecessary cluster scale-out. To limit this, lower your capacity provider target capacity to 80-90% & consider extending scale-in cooldowns during deployment windows so you have a controlled window for scale-in rather than oscillation