Kubernetes vs. Managed Container Services: ECS, Cloud Run & Beyond

Kubernetes has become the de facto standard for container orchestration in modern IT, but it’s not the only way to run containerized applications. Cloud providers offer container as a service platforms – like Amazon’s Elastic Container Service (ECS), AWS Fargate, Google Cloud Run, Azure Container Apps, and others – that promise to simplify container deployments by abstracting away the complexity of managing clusters. These fully managed container services are enticing for organizations looking to quickly deploy applications without diving into the deep end of Kubernetes configuration. They offer convenience, speed, and lower upfront skill requirements. But are they always the best fit, especially for large enterprises pursuing long-term modernization? In this article, we’ll compare Kubernetes with these managed container services and explore why the “easy button” approach can sometimes become a crutch that does more harm than good in the long run. We’ll highlight what types of applications are good candidates for container SaaS platforms, where these platforms fall short for enterprises, and how over-reliance on them can hamper skill development and future agility. Real-world cases from tech companies will illustrate how early shortcuts can lead to later roadblocks. The goal isn’t to declare one option universally better, but to ensure you choose the right path for your needs – balancing short-term simplicity against long-term flexibility and growth.

The Allure of Managed Container Services (ECS, Cloud Run, etc.)

For many teams, the initial draw of managed container services is powerful. These platforms handle the heavy lifting of provisioning and operating the container environment, allowing developers to focus on writing code and packaging it into containers. For example, AWS ECS tightly integrates with other AWS services and lets you deploy Docker containers without running your own Kubernetes control plane. Similarly, Google Cloud Run is a fully serverless container platform – you just provide a container image, and Google handles scaling it up or down to meet requests. The promise is that you can get your applications running in the cloud quickly, with minimal infrastructure management.

This convenience makes managed container services well-suited for certain scenarios and application types:

Small, stateless microservices or APIs

Cloud Run, for instance, excels at running stateless web services or REST APIs that can scale out on demand and even scale to zero when idle. If your application fits into a request/response model and doesn’t maintain stateful connections, such serverless containers can be extremely cost-efficient and simple to use.

Event-driven and batch jobs

Managed services are great for intermittent workloads. Cloud Run can spin up containers to handle events (e.g. processing messages from a queue) or run short-lived data processing jobs without needing an always-on cluster. For occasional or spiky tasks, paying only for runtime (as Cloud Run’s billing model does) is attractive.

Teams with limited container expertise or resources

A small startup or a departmental project might not have a dedicated DevOps team to run Kubernetes. Platforms like ECS or Azure’s Container Instances let developers deploy containers with a few clicks or simple CLI commands, avoiding a steep learning curve. As one guide notes, ECS is “great for teams looking to deploy containers fast without complex configurations” (1). For organizations fully invested in a single cloud ecosystem, using the cloud provider’s native service can also streamline integration (for example, ECS works seamlessly with AWS networking, load balancers, and IAM security out of the box).

Cost-conscious or low-traffic applications

Because many container services use pay-as-you-go pricing, they can be cost-effective at small scale. Cloud Run only charges you when your code is actually handling requests, which can result in almost no cost for low-traffic sites or dev/test environments. A small static website or internal API on Cloud Run might incur negligible expense while avoiding the overhead of running idle servers.

In summary, managed container services shine for simple, cloud-native applications that need to get off the ground quickly and don’t require deep customization. They lower the barrier to entry for containerization. As long as your needs align with what these platforms provide – e.g. stateless workloads, moderate scale, standard integrations – they can save you time and operational effort in the short run. It’s no surprise many organizations begin their cloud journeys here, enjoying the “quick win” of container deployments without the complexity of Kubernetes.

Where These Platforms Fall Short for Enterprises

Despite their advantages, container services like ECS and Cloud Run are not one-size-fits-all – especially when it comes to large or fast-growing enterprises. As applications and teams scale up, the very simplicity that made these platforms attractive can become a limiting factor. Here are key areas where fully managed container services often fall short for enterprise needs:

🔒 Vendor Lock-In and Limited Portability

Managed services tend to be closely tied to their cloud provider’s ecosystem. For example, Amazon ECS is designed to run exclusively on AWS infrastructure and uses AWS-specific concepts (task definitions, ECS APIs, etc.). This proprietary model can limit your future choices and portability. If you ever need to run your containers in another cloud or on-premises, you’ll face a steep migration – everything from your deployment definitions to your operational tooling is specific to AWS ECS. As one analysis bluntly put it, “when you use ECS, you are not really learning how to work with containers. You are learning how to work with ECS,” meaning those skills and configurations “do not travel well” outside AWS (2). The same goes for other services: Cloud Run is currently only on Google Cloud, and while it simplifies deployment, it abstracts away the Kubernetes API entirely. That means if you outgrow Cloud Run and need to move to a Kubernetes cluster (Google’s or otherwise), you’ll have to introduce your team to a whole new API and set of concepts. In short, these services can create tight coupling to a vendor’s stack, which is risky for enterprises that value multi-cloud flexibility or fear being “locked in.” Even efforts by providers to extend their services beyond the cloud (like “ECS Anywhere”) don’t truly eliminate the dependency – they often just stretch the tether a bit longer while keeping you tied to the provider’s control plane.

📦 Feature Limitations and Lack of Flexibility

Managed container platforms are intentionally opinionated and may not support advanced deployment scenarios. This is fine for basic microservices, but enterprises often run into needs that these platforms can’t easily accommodate. A notable example is stateful services. Cloud Run only supports stateless containers – you can’t, for instance, run a database or any service that requires persistent storage or sticky identity per instance. AWS ECS can run stateful containers with effort, but it lacks higher-level constructs like Kubernetes StatefulSets. The design decision to keep ECS simpler means features like maintaining persistent identities for pods or automatic rescheduling of stateful apps aren’t available out-of-the-box. Companies have hit painful walls due to this; for example, Figma (the collaborative design platform) initially ran all their services on ECS and encountered difficulties deploying a stateful system like etcd (a distributed key-value store) because ECS had no equivalent to Kubernetes StatefulSets. They resorted to brittle custom scripting to manage etcd’s cluster membership on ECS – a workaround that consumed engineering time and was fragile (3). Such limitations pushed them to reconsider Kubernetes, which handles these needs natively (in Kubernetes, etcd is commonly run as a StatefulSet with stable network IDs for each pod). Additionally, Kubernetes’ flexibility allows running complex off-the-shelf tools via the rich ecosystem of Helm charts and operators. Figma found that adopting open-source software like the workflow engine Temporal was “hard to install and maintain on ECS” because there was no straightforward way to apply the pre-existing Helm chart. Instead, engineers would have to manually translate everything into AWS formats like CloudFormation/Terraform. In contrast, on Kubernetes, they could deploy such tools much more easily using standard manifests or Helm. ECS’s limited orchestration features (as compared to Kubernetes) are noted elsewhere too. For instance, ECS imposes default service quotas and has fairly “complex networking” setup for certain modes. These constraints can become pain points as your workloads grow in number and complexity beyond what the “simple” service was designed for. In short, enterprises often need the richer feature set and extensibility of Kubernetes (custom resource definitions, fine-grained scheduling, pluggable networking, service mesh, etc.) once their architecture evolves past a certain point.

🔍 Observability and Debugging Challenges

One often overlooked downside of highly abstracted platforms is the loss of visibility into the underlying operation of your containers. Managed services hide much of the complexity – which is great until something goes wrong. When a container in Cloud Run or ECS fails or behaves oddly, you have fewer tools to diagnose why, compared to a Kubernetes environment where you can inspect pod logs, events, and even dive into the network namespace if needed. As Portainer’s Neil Cresswell describes it, “what ECS does is hide the complexity, not eliminate it. And when things break (and they inevitably do) you are left with very little context to debug.” Your team hasn’t been exposed to how containers actually run under the hood or how networking and storage are configured, so troubleshooting is like flying blind. Many ECS users discover that to perform certain fixes or updates, they have to drop to AWS CLI or write scripts because the managed UI doesn’t expose all the controls. This can be frustrating: a supposedly “easy” service reveals sharp edges when you try to go outside its narrow paved path. In contrast, Kubernetes, while complex, is very transparent – everything is declarative and observable (pods, events, metrics, etc.), which can actually make debugging and tuning easier for those who learn it. Enterprises with complex apps often require that transparency for effective monitoring, performance tuning, and rapid problem resolution.

📉 Skill Development and Operational Maturity

Perhaps the most strategic concern is the impact on the organization’s skills and DevOps maturity. Relying heavily on a plug-and-play service can lead to a false sense of security. Your team may become proficient in using ECS or Cloud Run, but not in understanding container orchestration. As one commentary noted, “ECS doesn’t teach you containers, it teaches you ECS… these are AWS-native patterns, not container-native concepts. And they do not travel well.” In an enterprise setting, this can hurt you in the long term. If all your automation, deployment pipelines, and team knowledge revolve around a specific service’s quirks, you might struggle to adapt to new technologies or cloud strategies down the road. For example, many companies start with one cloud’s easy service and later decide to adopt a multi-cloud or hybrid approach (for regulatory, cost, or strategic reasons). If your people haven’t built up foundational skills in Kubernetes or at least in cloud-agnostic container tools, you face a steep learning curve at the worst possible time. Additionally, part of modernization is cultural – embracing Infrastructure as Code, CI/CD, and DevOps practices. While it’s entirely possible to practice IaC and DevOps with ECS/Cloud Run, some organizations fall into a pattern of treating the platform as a black box (clicking around a web console) which can slow down the development of internal engineering best practices. By contrast, teams that invest in Kubernetes often end up improving their operational game: writing declarative manifests, automating processes, and managing infrastructure more like software. In essence, managed services can become a crutch – making it easy to get by without expanding your team’s skillset – and that crutch may hamper your broader cloud-native journey later. An AWS-focused solution might meet today’s needs, but what about tomorrow’s need to deploy on-premises, or use that cool new CNCF project everyone’s talking about? Enterprises must be wary of trading away future agility for present convenience.

💰 Cost and Scaling Surprises

At first glance, using a serverless or managed container platform seems cost-efficient – no control-plane to run, no clusters to keep online 24/7. And indeed, if you have a small deployment, it likely is cheaper. However, at enterprise scale, the cost equation can shift in unexpected ways. Managed services often come with many metered components: you’ll pay for CPU/memory usage, but also potentially for data transfer, load balancers, logging, monitoring, etc. Users of ECS, for example, have noted that CloudWatch log costs and other AWS service charges can pile up as your application scales, sometimes making the managed approach more expensive than running your own optimized Kubernetes clusters. Moreover, because you have less control, you can’t employ some advanced cost-saving techniques that an expert running Kubernetes might (such as bin-packing workloads to use all available capacity, customizing auto-scaling behavior, or using spot instances for non-critical workloads). One analysis pointed out that with ECS “you cannot tune the scheduler…you are managing workloads through abstraction, not through ownership,” which makes it harder to optimize resource usage. In addition, as your usage grows, you might hit service limits (like max tasks or services in ECS) and need to request quota increases or architectural workarounds. All of this is to say: for an enterprise at scale, managed services can carry hidden costs and scalability limits. Kubernetes, being open-source and self-manageable, lets you architect your infrastructure for efficiency (albeit with more effort). Many organizations eventually find that running a well-tuned Kubernetes cluster (especially with the help of a managed Kubernetes service for control plane) can be more cost-effective at a certain size than paying premiums for a fully hands-off platform.

In summary, container SaaS platforms are opinionated solutions that solve a lot of problems for you – but the flip side is they constrain what you can do and how you do it. For a large enterprise with diverse and evolving requirements, those constraints often become pain points. Whether it’s being stuck in one vendor’s lane, lacking critical features, struggling to troubleshoot black-box infrastructure, or not having the internal chops to advance your tech stack, the “easy” path can lead to dead-ends. As your organization pursues modernization, these limitations mean managed services might fail to support new initiatives – and by then, the cost of switching to a more flexible approach (like Kubernetes) is much higher, because you have to unlearn the old platform and retool a lot of systems.

Kubernetes to the Rescue (Mostly): Flexibility, Control, and Modernization

If the limitations above sound ominous, it’s because they underscore why so many enterprises eventually gravitate toward Kubernetes. Kubernetes is undoubtedly more complex to set up and operate initially – it’s a heavyweight tool built to handle complex, distributed systems across any environment. But with that complexity comes unmatched flexibility and control:

Open and Cloud-Agnostic

Kubernetes is open-source and can run virtually anywhere – on public clouds, on-premises data centers, hybrid clusters, at the edge, etc. This means you can adopt a single orchestration platform across all your environments, avoiding siloed solutions. Enterprises that want the option to switch cloud providers or support multi-cloud deployments gain that freedom with Kubernetes (e.g., you could run part of your workload on AWS EKS, part on Google GKE, and even on your own servers, with a consistent experience). This cloud-agnosticism is a major reason to choose K8s if “you need portability and want to deploy across multiple cloud providers or on-premises”. You’re investing in a standard that isn’t owned by any one vendor.

Rich Ecosystem and Features

Kubernetes comes with a robust set of features essential for modern, scalable applications – and a huge ecosystem of add-ons. Need to run stateful applications (databases, etc.)? Kubernetes has StatefulSets and Persistent Volumes to handle storage and stable identities. Want fine-grained network policies for security or custom network setups? Kubernetes allows custom CNI plugins and network policy resources. Complex release strategies like canary or blue-green deployments? Those can be implemented with native controllers or tools like Argo Rollouts. The list goes on: automated bin-packing, custom schedulers, horizontal and vertical pod autoscaling based on custom metrics, self-healing of failed containers, etc. And if something isn’t built-in, the community probably has an operator or plugin for it. This extensibility is key for enterprises – you’re far less likely to hit a wall where “Kubernetes can’t do that.” Instead, you might spend effort figuring out how to do it. For highly specialized needs, you can even write custom controllers or use third-party operators. In short, Kubernetes gives you the tools to tailor the platform to your workloads, rather than forcing your workloads to fit the platform.

Maturity and Community Support

Because Kubernetes is widely adopted across the industry (with a huge open-source community and vendor support), there’s a wealth of knowledge and constant innovation happening. You’re not limited to one company’s roadmap; you benefit from enhancements contributed by thousands of users and organizations. For example, if you want to integrate a new technology – say, a service mesh like Istio, or a new monitoring system – there’s likely first-class support for it on Kubernetes. Enterprises also appreciate that skills in Kubernetes are transferable; hiring or training for K8s expertise gives you talent that can work anywhere, not just in one cloud. Many enterprise IT leaders see adopting Kubernetes as a way to future-proof their operations, ensuring they’re in step with cloud-native best practices being adopted industry-wide.

Enabling DevOps and Self-Service

One interesting outcome of moving to Kubernetes is that it often accelerates an organization’s DevOps journey. Kubernetes’ declarative model encourages infrastructure-as-code and GitOps workflows. Teams can define their application deployments in YAML, store them in version control, and use CI/CD pipelines to deploy – fostering a culture of automation. Enterprises can even build internal developer platforms on top of Kubernetes that empower dev teams to deploy and manage services on their own (with guardrails). For instance, fintech company Plaid initially deployed on ECS but found that developers didn’t have direct control – the ops team had to get involved for every little change (4). After moving to Kubernetes, Plaid built a self-serve platform where each team could spin up and adjust their own services via simple config files, without always going through a central team. This shift not only removed bottlenecks but also aligned with DevOps principles by having developers operate their code. Kubernetes, in essence, provides the backbone for such internal platforms. It has robust APIs and object models that can be leveraged to create higher-level abstractions for developers (like templates or PaaS-like experiences) – something much harder to DIY on a less flexible service. For enterprises looking to modernize, this empowerment and cultural change is as important as the technology itself.

All that said, it’s important to acknowledge that Kubernetes is not a silver bullet. It comes with its own challenges. The learning curve is steep – you’ll likely need skilled site reliability engineers or extensive training for your team. Operating Kubernetes (even via managed services like EKS/AKS/GKE) means dealing with upgrades, cluster scaling, security patches, and potentially higher baseline costs (you might keep nodes running even at low utilization). For some organizations, these are significant hurdles. In fact, not every application needs Kubernetes, and using it for small-scale or very simple apps can indeed be overkill and lead to unnecessary complexity. It’s worth reiterating that the goal is to use the right tool for the job. If your entire system is a small monolith or a handful of services with predictable load, you might not need the heavyweight machinery of K8s – a simpler platform or even just VMs could suffice. The trade-off to consider is short-term ease vs. long-term agility. Kubernetes tilts towards the latter: it’s an investment upfront to gain adaptability and control for the future. Enterprises on a modernization path often decide that investment is worth it, to avoid hitting the ceilings imposed by more restrictive platforms.

Real-World Perspectives: Case Studies in Choosing (and Switching)

Seeing how real organizations navigate this choice can be illuminating. Two notable case studies come from Figma and Plaid – both high-growth tech companies that started with simpler container services and eventually transitioned to Kubernetes as their needs evolved.

Figma: From Quick Start to Hitting a Wall with ECS

Figma, a popular online design collaboration tool, initially ran their compute platform on Amazon ECS. This made sense at first: by containerizing their services and using ECS, they could “quickly spin up containerized workloads” without building a lot of orchestration infrastructure. As Figma grew, they even formed a team to manage their ECS-based platform, and for a while things ran smoothly. However, over time the limitations of ECS became more apparent – especially as Figma’s engineers wanted to implement more complex or stateful systems. We already mentioned the etcd fiasco (trying to run a key component of their system in ECS which lacked StatefulSets). The Figma team found themselves spending an increasing number of engineering hours on workarounds for ECS’s limitations. They described wondering if they were “iterating toward a local maximum instead of a global maximum” by sticking with ECS – in other words, would pushing ECS further yield diminishing returns compared to switching to a more powerful foundation? The ability to easily deploy standard tools (like via Helm charts) and manage stateful services were key capabilities they were missing. This led Figma to make the big decision to migrate their core services to Kubernetes. The transition took months of planning and effort (Kubernetes migrations are non-trivial), but by 2024 they had moved the majority of workloads onto Kubernetes. The payoff was significant: their platform became more feature-rich and “set them up for the long term”. They could now leverage Kubernetes primitives for things that were awkward in ECS, and they effectively “unlocked” the ability to introduce new services and open-source tools without fighting the old platform. Figma’s story underscores a common pattern – an easy start with a managed service eventually reaching a ceiling, prompting a move to Kubernetes for continued growth.

Plaid: Scaling Dev Teams with Kubernetes Self-Service

Plaid, a fintech company providing banking data APIs, offers another illustrative journey. Plaid began with a fairly basic deployment approach (AMIs on auto-scaling groups), then moved to AWS ECS around 2017 when containerization became the clear path forward. ECS worked for them in the early days, but as the number of microservices grew, they found ECS’s operational model cumbersome. Every new service meant setting up separate AWS CloudFormation stacks and CI pipelines, and the small infra team was getting paged for issues because developers lacked direct visibility or control. In Plaid’s words, “most engineers didn’t have direct control of their services… ECS also didn’t give us a lot of visibility into what was happening”, which led to frustration. The turning point was when Plaid realized that to keep shipping fast at a larger scale, they needed a deployment system where more automation and ownership could be given to the service teams. They experimented with Kubernetes and, especially after acquiring a startup that brought Kubernetes expertise, they decided to migrate everything to K8s. Crucially, they didn’t just lift-and-shift the old processes – they reimagined how deployments should work by building an internal platform on Kubernetes. Developers would define their service needs in a simple config (dubbed “Scaffold” at Plaid) and Kubernetes would handle the rest, providing features like load balancing, automatic canary deployments, and autoscaling “without much effort on their part”. This self-service Kubernetes platform removed the bottleneck of the infra team and let Plaid scale to hundreds of services and engineers while maintaining rapid deployment capabilities. The lesson from Plaid’s case: while ECS got them part of the way, Kubernetes enabled a higher level of scale and velocity by empowering developers and automating what was manual before. In a fast-moving enterprise, that can be a game-changer for productivity and reliability. (It wasn’t all smooth sailing – Plaid did hit some challenges in their K8s migration, like scaling Prometheus and adjusting to running many clusters – Kubernetes isn’t magic, after all. But they adapted and ultimately achieved a far more robust infrastructure than ECS alone could offer.)

These case studies highlight a reality seen in many organizations: managed services provide an on-ramp, but Kubernetes often becomes the highway for those who need to go farther. Companies like Figma and Plaid recognized that the short-term convenience of their initial approach could turn into a long-term handicap if they didn’t invest in a more scalable, flexible solution. By transitioning to Kubernetes (and importantly, doing so at a time when it was still manageable to switch), they avoided being stuck in a corner and instead set themselves up with a future-proof platform. Not every enterprise will have the same scale or needs as these tech companies, but the core principle applies broadly.

Industries with heavy regulatory or scaling demands especially lean towards Kubernetes for its consistency and control. For instance, in financial services and healthcare (where compliance and auditability are crucial), companies often prefer owning the full stack of their container platform, which Kubernetes allows, rather than relying on a third-party service that might abstract away details needed for audit or fine-grained security policies. Similarly, telecom or IoT enterprises that need to deploy workloads to edge locations or customer premises find Kubernetes a better fit, because it can be run in all those environments with the same toolset, whereas a cloud-only service simply won’t work off the cloud. One could argue that by not developing Kubernetes and cloud-native skills internally, an enterprise risks falling behind its industry peers who are building that muscle. Modernization is not just about migrating to cloud; it’s about continuously improving how you build and run software. If a managed container service delays that improvement, it might be giving you speed now at the cost of speed (and agility) later.

Conclusion: Weighing Short-Term Ease Against Long-Term Agility

Managed container services like ECS, Cloud Run, Azure Container Apps, etc., can be fantastic tools – in the right context. They cater to a level of complexity that is digestible for many teams and provide quick wins in deploying containerized applications. For small projects, early-stage products, or straightforward use cases, choosing these services can absolutely be the right decision. Kubernetes, on the other hand, is a bigger beast: it requires more upfront effort and know-how, which might be unnecessary for a modest workload. In fact, some experts caution not to use Kubernetes if you truly don’t need it, because of that added complexity and cost (5).

However, as we’ve explored, enterprises must think about the bigger picture. If your organization aims to embrace cloud-native practices, innovate rapidly, and operate at scale, you have to consider not just “What solves my problem today?” but “Will this solution support my growth and modernization tomorrow?”. The convenience of container SaaS platforms can mask the technical debt being built up – debt in the form of limited portability, missing features, and a workforce that hasn’t advanced its skillset. Those platforms might save you from having to hire a Kubernetes expert this year, but could leave you scrambling for one a couple of years down the road when you’ve outgrown the platform’s capabilities.

In many ways, it comes down to strategic trade-offs. By taking the easier path now, are you postponing an inevitable investment (and potentially making it harder by postponing)? Or are your needs narrow enough that the easy path is all you’ll ever need? There’s no one-size-fits-all answer, but generally:

• Use managed container services deliberately and with awareness of their constraints. They are excellent for certain applications (stateless web services, event handlers, low-traffic apps, quick prototypes) and can accelerate initial development. If you choose them, try to keep your architecture and deployment process as cloud-neutral as possible (for example, use standard Docker images and avoid proprietary config where feasible) so that migrating later is easier.

• Invest in people and skills. Even if you start on a simpler platform, consider training your team on container fundamentals and Kubernetes concepts. This way, the service is not a crutch but just a stepping stone. Some organizations run hybrid environments – a bit of Cloud Run or ECS for simple stuff, and Kubernetes for more complex systems – to gradually build competency.

• Reevaluate as you scale. The solution that fit at 50 containers might not fit at 500 or 5,000 containers. Periodically ask if the current platform is meeting your needs or if limitations are appearing. It’s better to proactively plan a transition (if needed) than to do it reactively in crisis mode. As Google’s cloud guidance suggests, you can even adopt a hybrid approach: use Cloud Run and GKE together, migrating workloads between them as complexity grows (6). Many organizations start with mostly managed services and incrementally adopt Kubernetes for the workloads that require more control – this can ease the learning curve and prevent a big bang switch.

  
  

Ultimately, Kubernetes vs. container managed services is not an either-or binary for all time. It’s a continuum of control vs. convenience. For enterprises focused on long-term modernization, Kubernetes (whether self-managed or via managed offerings like EKS/AKS/GKE) becomes an attractive backbone because of its flexibility, community, and ecosystem. It’s the platform you grow into, not out of. Managed services, while not “wrong” to use, should be chosen with clear understanding of their lifespan in your tech stack.

In the cloud era, success is often determined by how quickly and safely you can deliver value to customers. The tools you choose play a big role in that. A fully managed container service might let you deliver value now with less effort – but could slow you down later. Kubernetes might require more effort now, but pay dividends in agility later. Every enterprise must balance these timelines and make the choice that aligns with its goals and capacities.

In summary: beware the easy wins that become hard losses. Managed container services can jump-start your cloud journey, but don’t let them become a permanent crutch that holds back your organization’s evolution. With thoughtful planning, you can leverage their benefits while preparing for a Kubernetes-powered future – achieving both short-term success and long-term empowerment in your infrastructure strategy.

References

1. CLOUDZERO – Slingerland, C. “ECS Vs. Kubernetes: A Detailed Guide To Container Solutions”

2. Portainer.io - Cresswell, N. (05/14/2025). “When easy leads to expensive: Why ECS isn’t the best place to start with containers”

3. Figma - VonSeggern, I. (08/08/2024). “How we migrated onto K8s in less than 12 months”

4. PLAID - Worley III, G. (06/17/2022). “Migrating from ECS to Kubernetes”

5. erbis - Cherednychenko, M. (10/05/2023). “When Don’t You Need Kubernetes?”

6. Google Cloud - Documentation. (06/12/2025). “GKE and Cloud Run”