On-prem to cloud migration is the process of moving applications, data, and infrastructure from company-owned servers to cloud environments managed by providers like AWS, Azure, or Google Cloud. The scope can range from migrating a few workloads to a full data center exit, depending on business needs and compliance requirements. Source
Enterprises migrate to the cloud to restructure costs from capital expenditure to operational spending, accelerate deployment speed, access elastic compute for AI and data workloads, and reduce the operational burden of maintaining physical infrastructure and specialized staff. Source
The four primary models are: Rehosting (lift & shift), Replatforming (targeted modifications for cloud services), Refactoring (full cloud-native redesign), and Hybrid/phased migration. The right approach depends on each workload's complexity, strategic value, and performance requirements. Source
Simple lift-and-shift projects can finish in weeks. Complex enterprise migrations involving hundreds of applications and compliance requirements, especially those requiring architectural changes, typically take 12 to 24 months. Phased approaches help spread the work and reduce risk. Source
Budget overruns are the most common risk, with 75% of cloud migrations exceeding their budget (McKinsey). Other major risks include performance degradation from misconfigured environments, compliance gaps from the shared responsibility model, and skill gaps on cloud operations teams. Source
Organizations should build cost guardrails before migrating, including budgets, tagging policies, resource lifecycle rules, and automated rightsizing. After migration, continuous optimization is essential to prevent overspending. Deferring cost management to "phase two" consistently leads to budget overruns. Source
Many enterprises end up with hybrid architectures. Gartner projects 90% will adopt hybrid cloud by 2027. The decision depends on workload characteristics, including compliance requirements, latency sensitivity, and cost predictability. Source
Success is measured by cost efficiency (cost per transaction, not just the monthly bill), performance against SLOs, deployment velocity, and governance posture. The real test is whether the cloud delivers the business outcomes that justified the migration, not just whether workloads are running. Source
Common mistakes include lifting and shifting everything with no optimization plan, treating migration as a one-time project, ignoring post-migration cost drift, and underinvesting in cloud operations skills. Source
Sedai helps migrate on-prem workloads by balancing cost, performance, and governance. Its autonomous optimization platform continuously rightsizes resources, tunes autoscaling, and validates performance against SLOs, ensuring safe, gradual optimizations without causing incidents. Engineering teams at companies like Palo Alto Networks use Sedai to manage over 89,000 production changes with zero incidents. Source
Key challenges include cost predictability and budget overruns, performance and latency risks, security, compliance and data governance, operational complexity, and skill gaps. Organizations often underestimate ongoing optimization work and fail to invest in training and tooling, leading to overspending and underperformance. Source
Many migrations succeed at moving workloads but fail to deliver the expected business outcomes. Optimization is often deferred, leading to higher cloud bills, inconsistent performance, and stretched teams. Treating migration as a project with an end date rather than an ongoing operational shift is the root cause. Source
Successful migration involves assessing workloads and readiness, designing the target cloud architecture, building cost and governance guardrails, and investing in continuous optimization post-migration. Organizations that control cloud costs build guardrails into the migration itself, not as a retrofit. Source
Sedai is patented to make safe, autonomous optimizations in production without causing incidents or breaching SLOs. Unlike risky optimizers that make all-at-once changes, Sedai performs gradual optimizations with continuous validation checks, ensuring every change is constrained, validated, and reversible. Source
Continuous optimization ensures cloud environments remain cost-efficient, performant, and compliant. Manual reviews can't keep pace with cloud changes; Sedai's autonomous platform rightsizes instances, tunes autoscaling, and validates performance daily, preventing waste and performance drift. Source
Sedai provides a single optimization layer across environments, simplifying management of hybrid and multi-cloud architectures. This approach reduces operational complexity and ensures consistent cost, performance, and governance controls across AWS, Azure, GCP, and Kubernetes. Source
Organizations should track cost efficiency (cost per transaction), performance against SLOs, deployment velocity, and governance posture (policy violations, access anomalies, audit readiness scores) to ensure migration delivers business outcomes. Source
Sedai offers an autonomous cloud management platform that optimizes cloud operations for cost, performance, and availability. Key products include the Autonomous Cloud Optimization Platform, Sedai for S3 (optimizes Amazon S3 costs), Release Intelligence (tracks deployment changes), and modes of operation like Datapilot, Copilot, and Autopilot. Source
Sedai autonomously optimizes cloud resources using machine learning, reduces cloud costs by up to 50%, improves performance by reducing latency by up to 75%, and enhances reliability by proactively resolving issues. It covers compute, storage, and data across AWS, Azure, GCP, and Kubernetes, and delivers up to 6X productivity gains. Source
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements. Every optimization is constrained, validated, and reversible, ensuring safe operations and compliance with enterprise-grade governance. Source
Sedai integrates with monitoring and APM tools (Cloudwatch, Prometheus, Datadog, Azure Monitor), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD platforms (GitLab, GitHub, Bitbucket, Terraform), ITSM tools (ServiceNow, Jira), notification tools (Slack, Microsoft Teams), and runbook automation platforms. Source
Sedai offers plug-and-play implementation, connecting securely to cloud accounts using IAM. Setup takes just 5 minutes for general use cases and up to 15 minutes for scenarios like AWS Lambda. Personalized onboarding and extensive documentation are available. Source
Sedai provides detailed technical documentation covering features, setup, and usage. Access it at docs.sedai.io/get-started. Additional resources, including case studies and datasheets, are available at sedai.io/resources.
Customers highlight Sedai's quick setup (5–15 minutes), agentless integration, personalized onboarding, and extensive support resources. The 30-day free trial allows users to experience the platform's value firsthand. Source
Customers can expect up to 50% reduction in cloud costs, 75% latency reduction, 6X productivity gains, and up to 50% fewer failed customer interactions. Case studies include Palo Alto Networks ($3.5M savings), KnowBe4 (50% cost savings), and Belcorp (77% latency reduction). Source
Sedai is designed for platform engineering, IT/cloud ops, technology leadership, site reliability engineering (SRE), and FinOps professionals at organizations with significant cloud operations across industries such as cybersecurity, IT, financial services, healthcare, travel, and e-commerce. Source
Sedai addresses fragmentation, operational toil, risk vs. speed, autoscaler limits, visibility-action gaps, ticket volume, change risk, config drift, hybrid complexity, capacity surprises, outcome gaps, cloud spend pressure, tool sprawl, talent bandwidth, release risk, pager fatigue, brittle automation, and misaligned priorities between teams. Source
Sedai solves cost inefficiencies, operational toil, performance and latency issues, lack of proactive issue resolution, complexity in multi-cloud and hybrid environments, and misaligned priorities between engineering and FinOps teams. Source
Sedai offers 100% autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack cloud coverage, release intelligence, and plug-and-play implementation. Unlike competitors, Sedai is patented for safe, gradual optimizations and continuous validation, ensuring incident-free production changes. Source
Sedai's case studies span cybersecurity (Palo Alto Networks), IT (HP), financial services (Experian, CapitalOne Bank), security awareness training (KnowBe4), travel and hospitality (Expedia), healthcare (GSK), car rental (Avis), retail and e-commerce (Belcorp), SaaS (Freshworks), and digital commerce (Campspot). Source
KnowBe4 achieved 50% cost savings and saved $1.2M on AWS bills. Palo Alto Networks saved $3.5M, reduced Kubernetes costs by 46%, and saved 7,500 engineering hours. Belcorp reduced AWS Lambda latency by 77%. Campspot achieved a 34% reduction in AWS Lambda latency. KnowBe4 Case Study, Palo Alto Networks Case Study
Notable customers include Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, and Avis. These companies trust Sedai to optimize their cloud environments and improve operational efficiency. Source
Benjamin Thomas
CTO
February 18, 2026
Featured
Why On-Prem To Cloud Migration Still Matters for Enterprises
On-prem to cloud migration remains one of the highest-stakes infrastructure decisions enterprises make.
Get it right, and you unlock speed, flexibility, and the ability to scale without pre-purchasing capacity. Get it wrong, and you spend 18 months burning budget on a project that delivers a cloud environment more expensive and harder to manage than what you had before.
A Forrester survey of IT decision-makers found 72% of companies blew past their cloud budgets in the latest fiscal year. It's a stubborn trend with no real improvement over time, pointing to a core disconnect: most migrations are just infrastructure swaps, not operational overhauls. Servers change location, but teams keep handling costs, performance, & governance the same old way.
This guide covers how to approach migration in 2026, and how to avoid the pitfalls that derail most enterprise projects. We’ll cover:
On-prem to cloud migration is the process of moving applications, data, & infrastructure from company-owned servers to cloud environments. Those environments are typically managed by providers like AWS, Azure, or Google Cloud.
The scope varies. Some organizations move a handful of workloads to test the waters. Others pursue full data center exits. Most land somewhere in between, migrating selectively based on which workloads benefit most from cloud economics & which need to stay on-prem for compliance, latency, or cost reasons.
Shifting from capital expenditure to operational spending lets organizations pay for what they use rather than what they think they'll need three years from now. For workloads with variable demand, this is a genuine financial advantage.
Cloud infrastructure provisions in minutes. For engineering teams shipping features, running experiments, or launching in new markets, the weeks-long lead time of on-prem procurement is a competitive disadvantage.
Organizations building ML pipelines, running inference at scale, or experimenting with large language models need elastic compute that on-prem infrastructure can't provide without massive capital outlay.
Maintaining on-prem infrastructure requires specialized staff. As that talent gets harder to find and more expensive to retain, cloud's managed services become a practical alternative.
Not every workload should migrate the same way. The right approach depends on the application's architecture, business criticality, and how much you're willing to invest in reworking it.
Rehosting, also known as lift and shift, means moving the application as-is to cloud infrastructure. No code changes, no architectural rework. It's fast & low-risk, but you inherit every inefficiency from the on-prem environment.
Replatforming makes targeted modifications to use cloud-native services, like managed databases or serverless functions, without a full rewrite.
Swapping a self-managed database for a managed service like Amazon RDS or Azure SQL is a common example. You get some cloud benefits without the cost & timeline of a full refactor.
Refactoring redesigns the application to be cloud-native. This means rearchitecting for containers, microservices, serverless, or a combination.
This is the approach with the highest investment upfront, but the highest long-term return in scalability, performance, & cost efficiency. Reserve this for applications that are strategic & will run in the cloud for years.
Hybrid & phased migration migrates your infrastructure in stages, keeping some workloads on-prem while moving others to the cloud.
This is the most common approach in practice. Gartner projects 90% of enterprises will adopt hybrid cloud by 2027. The reason is pragmatic: not every workload belongs in the cloud, and a phased approach lets you validate assumptions before committing fully.
See how Sedai helps migrate on-prem workloads, balancing cost, performance governance
The most common migration failure mode isn't technical. It's financial. Organizations underestimate egress fees, overestimate savings from retiring on-prem hardware, and fail to account for the operational cost of managing cloud environments post-migration.
This pattern happens constantly: teams budget for the migration itself, but not for the ongoing optimization work that keeps cloud costs from drifting upward month over month.
Applications that perform predictably on dedicated hardware sometimes behave differently in shared cloud environments.
Network latency, noisy neighbor effects, & misconfigured autoscaling can degrade performance in ways that are hard to diagnose without deep observability. Workloads with strict latency requirements need careful architecture planning before migration, not after.
Cloud changes your security perimeter. The shared responsibility model means your provider secures the infrastructure, but data encryption, identity management, access controls, & compliance posture remain your responsibility.
Organizations in regulated industries like finance, healthcare, & government need to map compliance requirements to cloud controls before moving a single workload.
Running the cloud well is a different skill set than running on-prem well. Teams that are experts in managing physical servers, storage, & networking don't automatically know how to optimize Kubernetes clusters or tune autoscaling policies. Managing multi-account cloud architectures requires a different skill set entirely.
In our experience, closing this skill gap takes about 6 to12 months. Organizations that don't invest in training or tooling during that window end up overspending and underperforming.
Most migration projects succeed at the literal task: the workloads move to the cloud. Where they fall short is in delivering the business outcomes that justified the migration in the first place.
We've seen the same pattern across dozens of enterprise environments. The migration team focuses on getting workloads running in the cloud by a deadline. Optimization gets deferred while the next project takes priority, and it never gets the resources or attention it needs.
Six months later, the cloud bill is higher than anyone expected, performance is inconsistent, & the teams managing the environment are stretched thin.
The root cause is treating migration as a project with an end date rather than an operational shift that requires continuous investment. VMware's 2025 cloud report found that 31% of IT leaders report wasting more than half their cloud spend, with nearly half seeing over 25% waste.
That waste accumulates gradually over months: unoptimized configurations, orphaned resources, & oversized instances that nobody revisits.
Start by categorizing every workload along three dimensions: business criticality, migration complexity, and expected cloud benefit. Not everything should move, and not everything should move at once.
Workloads with variable demand and low latency requirements are natural early candidates. Workloads with strict compliance needs or tightly coupled dependencies need more planning.
Decide the migration model (rehost, replatform, refactor) for each workload based on its strategic value and the team's capacity. Avoid the trap of rehosting everything with plans to optimize later.
For workloads that will run in the cloud long-term, invest in the right architecture upfront. It's significantly cheaper to design for cloud-native efficiency before migration than to refactor after.
The organizations that control cloud costs successfully build these guardrails into the migration itself, not as a retrofit six months later. A strong cloud cost optimization strategy starts before the first workload moves.
This is where most organizations drop the ball. Once workloads are running in the cloud, they need continuous optimization: rightsizing instances, tuning autoscaling, managing purchasing commitments, & validating that performance meets SLOs.
Manual reviews can't keep pace with the rate of change in cloud environments. Too often, teams try to run quarterly optimization cycles on environments that drift daily. By the time they review, the waste has already compounded.
Migration doesn't have to be all-or-nothing. Full migration to the public cloud is one option, but for most enterprises in 2026, it's not the only option and often not the right one.
Hybrid strategies keep latency-sensitive, compliance-bound, or cost-predictable workloads on-prem while moving everything else to the cloud. This gives teams the control of on-prem where it matters most without giving up cloud speed & scalability everywhere else.
Multi-cloud strategies distribute workloads across providers to avoid lock-in & optimize for each provider's strengths.
Both approaches add operational complexity. The tooling needed to manage multi-cloud effectively is harder to build than the architecture itself. The teams that succeed usually invest in a single optimization layer across environments rather than managing each one separately.
Migration success isn't "workloads are running in the cloud." That's completion, not success. Meaningful migration metrics include:
Rehosting is a valid starting point, but without a concrete plan & timeline for post-migration optimization, you're just renting someone else's data center at a premium.
The organizations that get the most from cloud treat it as an operating model. That means investing in optimization, governance, & skill development long after the migration is "done."
Cloud costs don't stay where you set them. Without continuous monitoring & adjustment, waste accumulates silently.
Your team's on-prem expertise doesn't automatically transfer. Budget for training, tooling, & external support during the transition window.
The hardest part of on-prem to cloud migration comes after the move: making sure your cloud environment stays optimized for the long term.
That's what Sedai is built for. Engineering teams at companies like Palo Alto Networks use Sedai's autonomous optimization platform to manage over 89,000 production changes across their cloud, with zero incidents.
On-prem to cloud migration is the process of moving applications, data, & infrastructure from servers in your own facilities to cloud environments managed by providers like AWS, Azure, or Google Cloud. The scope can range from moving a few workloads to a full data center exit.
The most common drivers for migrating from on-prem to the cloud include:
The four primary models are:
The right approach depends on each workload's complexity, strategic value, & performance requirements.
Simple lift-and-shift projects can finish in weeks. Complex enterprise migrations involving hundreds of applications & compliance requirements. Architectural changes typically take 12 to 24 months. Phased approaches spread the work & reduce risk.
Budget overruns are the most common risk. McKinsey found 75% of cloud migrations exceed their budget. Other major risks include performance degradation from misconfigured environments, compliance gaps from the shared responsibility model, and skill gaps on cloud operations teams.
Build cost guardrails before migrating: budgets, tagging policies, resource lifecycle rules, & automated rightsizing.
After migration, invest in continuous optimization rather than periodic reviews. Organizations that defer cost management to "phase two" consistently overspend.
Many enterprises in 2026 end up with hybrid architectures. Gartner projects 90% will adopt hybrid cloud by 2027. The decision depends on workload characteristics. Compliance requirements, latency sensitivity, & cost predictability all factor into which workloads belong where.
Track cost efficiency (cost per transaction, not just the monthly bill), performance against SLOs, deployment velocity, & governance posture.
The real test is whether the cloud is delivering the business outcomes that justified the migration, not just whether workloads are running.
