# Sedai: Customers Full content of 2 pages from https://sedai.io/customers, published 2026-04-13 to 2026-07-06. Each entry starts with the page title and its source URL. Every full-content file is listed in https://sedai.io/llms.txt. --- # How Relay Network Saved 3,868 Engineering Hours with Autonomous Cloud Optimization Source: https://sedai.io/customers/relay-network Relay Network cut AWS ECS costs 40%, saved 3,868 engineering hours, and reduced S3 storage 66% using Sedai autonomous cloud optimization. ## Story Highlights ### The Company  Relay Network is a customer engagement platform with a lean, security-conscious engineering team and no dedicated CloudOps headcount. ### The Challenge  Brendan Putek, Director of DevOps, was managing cloud infrastructure himself on top of platform, security, and DevOps responsibilities. Cloud optimization kept getting deprioritized. ### The Solution  Relay Network adopted Sedai's autonomous optimization, starting in dev, QA, and staging environments and expanding to full Autopilot across production. ### The Results - **40% reduction** of ECS container costs - **$12,051 saved** on a single AWS ECS container - **3,868 engineering hours** saved in 2026 alone - **4,699 days of latency** improvement across AWS Lambda - **900TB** **cut to 300TB** on AWS S3 --- ## The Challenge: Running Cloud Infrastructure Without a Dedicated Ops Team Brendan Putek doesn't have a simple job title. As Director of DevOps at Relay Network, he owns platform, security, FinOps, & DevOps as a single function. Developer enablement is his primary focus. Everything else, including cloud infrastructure, competes for whatever time is left. When Relay Network came to Sedai, the team was deep in the middle of a major disaster recovery upgrade alongside a full slate of contractual and compliance milestones. Because of this, right-sizing infrastructure kept getting deferred. "We have such an ambitious roadmap," said Brendan. "We want to spend as much time as possible innovating, and optimizing infrastructure was the piece that kept getting pushed to the next quarter." ### Why Reactive Autoscaling Fails Reactive autoscaling fails because it responds to a burst only after the burst starts, and by the time new capacity comes online, spikes are often over.  Relay Network felt this directly: Their EC2 services handled core messaging traffic that spiked predictably during peak hours. But without a dedicated ops team, Brendan's solution was implementing metric-based triggers to scale them up and down manually. That cost the team $1,000/month, and didn't actually solve the problem. "By the time the VMs stood up, the burst was over," said Brendan. "And now we're just processing the backlog versus handling it more in real time." The automatic scaling was an indication of a deeper limitation: reactive scaling. Conventional autoscalers poll metrics every 15 to 30 seconds, and new instances can take another minute or more to come online. For a burst that spikes and drops in under 60 seconds, the capacity arrives after the damage is done. [Predictive autoscaling works differently](https://sedai.io/blog/predictive-autoscaling-in-kubernetes). But building that capability in-house wasn't an option for a small team that was already stretched too thin. ### Why Cloud Automation Tools Can’t Be Trusted Most cloud automation tools can't be trusted because they can't answer a basic question: How are decisions being made, and can they be audited? The market is full of these tools claiming to make autonomous infrastructure decisions with no proof behind them. For a team that owns both DevOps and security, using tools like that was a dealbreaker. Brendan was direct about the problem, "Stuff goes into a black box, and then stuff comes out," he said. A system making autonomous changes in production needed to be explainable at every step, from:  - The initial opportunity identified - The validation process - The action taken - The record left behind  Without that, handing over production to an outside tool wasn't a risk any security-conscious team could justify. Ultimately, Brendan needed an approach to cloud optimization that would completely remove the manual work without the fear of breakage. Most tools could offer one; finding both in the same platform was the harder problem. As he put it, “My goal is to not have to think about auto-scaling, and not have to have my team think about auto-scaling." --- ## The Solution: Replacing Manual CloudOps with Autonomous Cloud Optimization When Relay Network partnered with Sedai in 2023, Brendan was focused on one thing: He needed autonomous scaling so his team could stop managing CloudOps and focus on shipping product instead. "I didn't care about the cost savings implications. With autonomous scaling and not having to do that work ourselves, that's awesome,” he said. That framing stuck throughout the entire engagement. Brendan has never described Sedai as a FinOps tool. To him, it's a cognitive load removal platform that allows his team to operate at scale, without needing specialized skills in load testing, right-sizing, or capacity planning. The question wasn't whether to use Sedai. It was how to get to autonomy safely. ### How Relay Brought Autonomous Optimization to Production Safely Sedai has three distinct operating modes, which teams can move between at their own pace: - **Datapilot** surfaces optimization opportunities without taking action - **Copilot** lets engineers review and approve potential optimizations, before Sedai safely implements them - **Autopilot** is fully autonomous: Sedai identifies, validates, and executes optimizations without any human review required For Brendan, Autopilot was always the destination, but he needed confidence he could trust it in production. To do that, Sedai configured environment-level groupings so Autopilot could run in Dev, QA, and Staging first. Brendan was able to see the data in non-critical environments, and verify that changes were safe and incremental. "Sedai’s approach really gives us that confidence at scale that, yes, we can allow this external tool to manage optimization for us,” he said. “And we're not going to suddenly find ourselves with a massive outage." ### How Application-Aware Optimization Works Application-aware optimization works by learning how each application actually behaves instead of applying static rules or wrapping LLMs around infrastructure metrics and calling it intelligence. But Sedai’s patented machine learning (ML) model learns application behavior by monitoring traffic patterns, seasonality, dependencies, and performance. Every optimization decision flows from that deterministic knowledge. For Brendan, that distinction was everything:  "You're not built on LLMs. Everything is deterministic. Every decision is backed by hard data and you can walk the entire chain back to determine why it was made. It's not a black box. That's a huge, huge selling point." That determinism lets Sedai reach conclusions a human engineer wouldn't. "In some cases, we've actually increased capacity to run faster and save money. Which, quite frankly, is not something any of my engineers would have ever been able to come up with," Brendan said. ### Expanding Autonomous Optimization Across 16 AWS Accounts By May 2025, \~55% of production resources were running on Autopilot. The results were so impressive that Brendan held an internal meeting with Relay’s engineering leadership to show what the platform could do. Their reaction was immediate: they wanted to expand further. "I had provided ample proof the product worked. It was safe. So I just turned it on. There weren't any issues," he said. By August 2025, all of Relay's 16+ AWS accounts were fully configured. Brendan's message to the team: Sedai would be enabled across production services by year-end. ### Making Autopilot the Default for Every New Service By October 2025, Relay Network shipped every new service on Sedai’s Autopilot by default.  "Autopilot is the most helpful to our engineers because then they don't have to think about it." Engineering leads stopped making right-sizing decisions on every new deployment, and the team's focus shifted entirely to shipping product. By February 2026, Brendan confirmed what the team had been working toward since September 2023: all eligible workloads were on full Autopilot. --- ## The Results: How Relay’s Eng Team Stopped Managing Infrastructure Eighteen months after Sedai's initial deployment, the impact shows up in three places: the **AWS bill**, the **engineering team's workload**, and **how fast Relay ships**. By the numbers: - 40% reduction of ECS container costs - $12,051 saved on a single AWS ECS container - 3,868 engineering hours saved in 2026 alone - 4,699 days of latency improvement across AWS Lambda - 900TB cut to 300TB on AWS S3 - No cloud specialist hire required ### AWS ECS: Optimization at the Container Level Across Relay Network's ECS workloads, Sedai has autonomously reduced container costs by an average of 40%, with $12,051 saved on a single container alone. "No matter what we put in for configuration, right or wrong, we're going to be right-sized. It's just one less thing we have to consider when we're building a new service," said Brendan. ### AWS S3: Visibility That Drove Action Relay Network's S3 environment had years of accumulated data with no cleanup process. Sedai was able to surface which buckets were using the wrong storage classes for their actual access patterns.  Armed with that visibility, Brendan spent two to three months working through the data — classifying what was still needed, what could move to lower-cost tiers, and what could be deleted entirely. "We just stored data in S3 forever," he said. "Sedai pointed me to the older data that should have been in a cheaper storage class but never was." The result: Relay Network's S3 storage **dropped from just over 900 terabytes to about 300 terabytes.** ### Cutting Engineering Toil by 3,868 Hours  In 2026 alone, **Relay Network's engineers reclaimed 3,868 hours** that would have gone to manual infrastructure management. With that time back, Brendan rebuilt the team's entire deployment process. **Relay now ships 800-plus deployments a week across four environments with a team of 25**. "Sedai is a third, maybe a quarter of what we'd pay a specialist engineer when you count the benefits." — Brendan Putek, Director of DevOps, Relay Network And nobody on that team thinks about infrastructure sizing to make it happen. "It doesn't even occur to them,” Brendan said. “We've just taken that cognitive load and totally removed it from their consideration"  But these results extend beyond just hours saved and increased shipping. Right-sizing and load testing requires a depth of infrastructure expertise that most engineering teams don't have in-house.  As Brendan put it, most SREs today are focused on incident management, not the kind of deep infrastructure tuning that was common a decade ago. "Sedai is a third, maybe a quarter of what we'd pay a specialist engineer when you count the benefits. If we didn't have this, we'd need to hire one, if not two people who specialize in that functionality — and I don't have the capacity to pick that up myself." ## Why Relay Chose ML-Based Optimization Over Rule-Based Autoscaling Brendan came to Sedai looking for one thing: a way to remove infrastructure management from his team's cognitive load entirely. Eighteen months later, when asked about the most noticeable change in his environment, his answer wasn't a metric. "The fact that the product runs in the background, makes changes to our services, scales them up and down as required, and we don't notice. That's exactly what we want. We want the product to be infrastructure. We don't want to have to think about it." For Relay Network, CloudOps is no longer a backlog item. Sedai handles it. --- *Ready to optimize your cloud with zero toil for your team? [Book a Sedai demo.](https://sedai.io/demo) --- # How Informed Cut 900+ Hours of Engineering Toil with Sedai Source: https://sedai.io/customers/informed See how Informed eliminated 900 hours of engineering toil and 26k+ days of latency across its Lambda environment — without a single production incident. ## Story Highlights #### The Company Informed is an AI-powered loan verification & fraud detection platform trusted by seven of the top ten U.S. auto lenders. #### The Challenge The company's engineering team didn't have time to manage & optimize its complex Lambda environment. The result was wasted cloud costs, degraded performance, & mounting engineering toil. #### The Solution Informed adopted Sedai's autonomous optimization to manage their complex serverless architecture and dramatically cut engineering toil.  #### The Results - 900+ hours of toil saved in 3 months - 26k+ days of latency eliminated across all Lambda workloads - 100% production compute runs on autopilot - $7K saved on a single Lambda function - Zero production incidents caused by Sedai ## Building a Cutting-Edge Cloud at Startup Speed Seven of the top ten U.S. auto lenders trust Informed’s AI platform to verify their loans. Its platform has processed over $350 billion in loan originations and counting. To meet Informed's complex infrastructure requirements, its engineering team runs everything on AWS Lambda functions. For Rob Berger, Chief Architect, that last part was both a point of pride and a problem waiting to happen. Because Informed’s customers submit loan documents in bursts, Rob made a deliberate call early on to go serverless: "Going serverless was pretty perfect, since we cycle between usage and no usage throughout a 24-hour period." Rob rebuilt the company's infrastructure from the ground up on AWS. Each environment runs 306 Lambda functions, many dedicated to running ML inference models, and 37 Step Functions as orchestrators — all replicated across multiple environments.  For a lean engineering team, managing complex infrastructure at that scale is no small feat. ### The Lambda Optimization Problem Lambda optimization is one of those tasks that's easy to deprioritize, especially when you’re working at startup speeds. But when cost becomes impossible to ignore, teams like Rob’s find themselves spending an excessive amount of time on manual optimization. "In the past, there were no tools to optimize Lambdas for cost & performance without a lot of manual work," Rob said. The ongoing manual toil of checking configurations, monitoring costs, & tuning functions quickly consumed engineering capacity. This problem was compounded by ML models running inside Lambdas and exploding their size. At the time, some functions were allocated as much as 10GB of memory — AWS Lambda's maximum limit.  While setting memory high guarantees functions won’t fail, it’s an expensive habit. The more Lambdas are provisioned, the costlier they become to run. But without a way to safely test and tune each function, Rob's team couldn't risk provisioning lower. ### The Cost of Manual Optimization Needing to drastically reduce costs, Rob planned to build an internal tool to handle optimization. But that would mean taking his engineers away from their day-to-day responsibilities just to build it, never mind spending time monitoring the tool continuously after it was implemented. "We originally planned to write our own performance analysis and do traditional serverless optimization,” he said. “But that takes a lot of work, and you have to do it periodically. My team just didn’t have time for that.” Building and maintaining that tool would have created a whole new source of toil for a team that was already stretched thin. Then, right before the team was about to launch that internal tool, Rob came across Sedai.  "When I found Sedai, there was nothing else on the market that I could find that optimized Lambdas the way Sedai does." ## The Autonomous Switch with Sedai With Sedai’s integrated ML that optimizes Lambdas based on real-time behavior, Rob was able to skip the internal tool entirely, and get continuous optimization without any of the work. However, finding Sedai was only the first step. Because Rob runs production infrastructure supporting hundreds of billions in loan originations, trusting an autonomous system to make changes in that environment wasn't something he took lightly. “We were definitely hesitant with using Sedai at the beginning,” Rob said. “We were concerned that it would just start flipping things during production and cause production glitches.” But Sedai's crawl-walk-run approach quickly dispelled these fears.  Before enabling any autonomous actions, Rob & his team began work in Sedai’s Datapilot mode. It provides full visibility into optimization opportunities & recommendations without executing any changes or risking production.  Rob’s engineers could see exactly what Sedai would do before it executed any optimizations. “Having the ability to see how Sedai optimizes while giving me the control to execute gave me a lot of confidence to move forward with it at startup scale." ### Trusting Sedai’s Impact in Production With the confidence Sedai could accurately identify Lambda functions needing optimization, the team moved to Copilot mode, which reviews and approves changes before Sedai executes them.  Rob’s team watched the recommendations, verified the behavior, & then gradually expanded autonomy within their Lambdas. "Sedai is completely transparent in what it optimizes. With that insight, we were able to confidently give it full autonomy. We just said, ‘Ok, let’s just turn it on.’" ### Give Your Team Time Back See how Sedai cut 900+ hours of engineering toil for yourself. Today, 100% of Informed's production compute runs on autopilot, with zero production incidents caused by Sedai. "We expected a full engineering effort to optimize Lambda functions. But Sedai just handles it for us, on autopilot." The most remarkable part of Rob’s implementation of Sedai is how unremarkable it was. "We pretty much got it set up and then ignored it," he said. ## Impact that Goes Beyond Budget By adopting Sedai, Informed has dramatically reduced their cloud costs. But beyond budget, Informed eliminated 900+ hours of engineering toil in three months and 26k+ days of accumulated latency — all without a single production incident. For an AI startup processing loan documents for the biggest lenders in the country, that performance impact is significant. Faster Lambda execution means faster loan verification, faster fraud detection, & faster decisions for the banks that rely on Informed's platform. Before Sedai, the team expected to spend engineering time building internal tooling, running test suites, & manually tuning functions on a recurring basis. Lambda optimization would have become a permanent drain on an engineering team that had no time for it. "Sedai just takes off a whole area of worry,” said Rob. “Normally, we would probably always be looking at our AWS spend and where costs were spiking. We just don't have to do that anymore." With Lambda optimization toil eliminated, Rob's engineers can focus on what they're actually there to do: building the product. "My engineers want to learn and build new things, not worry about delivering new features or spending time on optimizations. Sedai handles that with autonomy, so my team can just build." See how your team can stop toiling and start building. [Book a demo](https://sedai.io/demo).