Introduction
Most Kubernetes clusters run at a small fraction of the capacity their owners pay for. Cast AI, a company that sells cluster optimisation software, studied tens of thousands of clusters for its 2026 State of Kubernetes Optimization Report and found average CPU use of 8%, down from 10% a year earlier. Because Cast AI sells a fix for this problem, treat the exact figure with some care. The direction is still clear.
Adoption keeps rising anyway. The Cloud Native Computing Foundation (CNCF) reported in January 2026 that 82% of container users run Kubernetes in production, up from 66% in 2023. So the tool is everywhere, and a lot of it is poorly tuned.
Kubernetes starts, stops, moves and restarts the small packages, called containers, that make up an application, spreading them across many machines. Getting a first deployment live is the easy part. Running it well means understanding scheduling, automation, networking, monitoring, security and cost, and explaining the trade-offs to people outside infrastructure.
That is why businesses look for DevOps developers for Kubernetes instead of general programmers. This guide covers where Kubernetes projects go wrong, which skills to look for, how 15 companies compare, which job title you actually need, and which questions expose weak candidates before you sign anything.
Kubernetes projects don't fail only because of bad code: where infrastructure decisions go wrong
An application can pass every test and still run badly on a cluster, usually because of a decision made before anyone wrote the deployment files.
Overcomplicating cluster architecture
Teams often build for the company they hope to become. They set up several clusters, a service mesh and custom controllers for an app with a few hundred users. Each extra piece needs upgrades, monitoring and someone who understands it. Seasoned container orchestration experts tend to start small and add parts only when a measured problem demands them.
A single cluster with separate namespaces, which are named sections inside a cluster, handles many early products fine. Cost adds up too. On Amazon EKS, each cluster carries a control plane fee of $0.10 per hour, about $73 a month, before a single workload runs (AWS EKS pricing page). Five environments with five clusters means five fees.
Treating every application as a microservices project
Microservices split an application into many small services that talk over a network. They help when several teams need to release on their own schedules. They hurt when one team of five maintains twelve services, because every call that used to happen inside one program now crosses a network that can be slow or fail.
A well-organised single application often runs fine in one container. A good engineer asks how many teams you have before suggesting a split.
Ignoring resource allocation
Every container can declare how much CPU and memory it asks for, called a request, and the most it may use, called a limit. Set requests too high and you pay for idle machines. Set them too low and the cluster packs too many containers onto one machine, which then slows down.
A CPU limit set too tight causes throttling even when the machine has spare power, so the app feels slow while the dashboard shows low CPU use. Cast AI's 2026 report put CPU over-provisioning at 69%, up from 40% a year earlier. In one cluster it described, memory kills hit 50 in one interval despite heavy over-provisioning, then fell to almost none after rightsizing halved the CPU allocation.
Delaying security until deployment
Retrofitting security is slow because it touches everything. Who may deploy? Where do passwords live? Can any container talk to any other? Kubernetes Secrets are only base64-encoded by default, which anyone can reverse, so encryption and tight access rules have to be switched on deliberately.
Automating without establishing clear deployment rules
Automation repeats whatever you give it, including mistakes. A pipeline that pushes every merged change straight to production spreads a bad release faster than a person could. Teams also hit drift, where staging and production stop matching because someone fixed production by hand.
Before automating, write down who approves a release, what a rollback looks like, and which environment is the source of truth.
KEY TAKEAWAY. Most early Kubernetes problems trace back to cluster size, service structure and release rules. A developer who asks about those first is worth listening to.
What makes a DevOps developer suitable for Kubernetes projects?
Look past the certificate list and ask what the person has actually run. If you plan to hire Kubernetes developers, these six areas separate people who have operated clusters from people who have read about them.
Kubernetes architecture and cluster management
A cluster is a group of machines called nodes. Pods are the smallest unit and hold one or more containers. Deployments tell Kubernetes how many copies of a pod to keep alive, and services give those pods a stable address.
The control plane is the cluster's brain. It includes the API server, the scheduler that picks a machine for each pod, and etcd, the database that stores cluster state. A capable Kubernetes DevOps engineer can explain what happens when etcd slows down, and why a cluster with a healthy control plane can still run unhealthy applications.
• Comfortable reading cluster events and node conditions.
• Able to explain why a pod stays "Pending".
• Aware of the control plane limits that appear as clusters grow.
Containerisation and image management
Docker packages an application and everything it needs into an image. Smaller images start faster and give vulnerabilities fewer places to hide. Look for experience with multi-stage builds, private registries (storage for images) and scanners that flag known flaws before an image ships.
One edge case: an image tagged "latest" can change without warning. Experienced engineers pin exact versions so a rollback returns to the same thing.
Infrastructure as code
Terraform and Ansible describe servers, networks and settings in files instead of clicks. The payoff is repeatability, because a whole environment can be rebuilt from the repository. Ask what happens when someone changes a setting by hand in the cloud console. Strong candidates talk about drift detection and locked, backed-up state files.
CI/CD pipeline development
CI/CD means code is tested and released automatically after each change. GitHub Actions, Jenkins and GitLab CI/CD are the common tools. The tool matters less than the design: automatic tests, staged releases and a fast path back to the last good version.
Good CI/CD and Kubernetes services include rolling updates, canary releases that send a small share of traffic to the new version first, and rollbacks that take minutes.
Kubernetes networking and storage
Ingress controllers route outside traffic to the right service. One current edge case: the Kubernetes project retired the Ingress NGINX controller in March 2026, ending releases and security fixes, and recommends moving to Gateway API or another controller (Kubernetes SIG Network, November 2025). A candidate who still proposes it for a new build deserves a follow-up question.
Storage has its own trap. A disk attached to a pod usually lives in one availability zone, so a pod that restarts in another zone cannot reach it. Network policies and service discovery, which lets services find each other by name, complete the picture.
Monitoring and observability
Prometheus collects numbers over time, and Grafana draws them as charts. Centralised logging keeps messages from short-lived pods that would otherwise vanish on restart. Tracing follows one request across many services. Ask what the candidate alerts on. Alerts on error rates and slow responses help. Alerts on raw CPU alone create noise.
PRO TIP. Ask the candidate to walk you through a real outage they handled, step by step. Specific details, such as which metric changed first, are hard to invent.
15 companies to consider for DevOps and Kubernetes development projects
The companies below range from specialist hiring firms to global consultancies. Some supply individual engineers, others run whole projects, and one sells software. A DevOps outsourcing company and a staffing model solve different problems, so read the engagement details as closely as the capabilities.
1. HireFullStackDeveloperIndia
HireFullStackDeveloperIndia is based in Ahmedabad and supplies full stack developers who work across client-side code, server-side code and databases. That profile suits small product teams that want one engineer to build a feature and look after how it ships. For Kubernetes work, ask for engineers who have containerised an application and written a deployment pipeline, because that is where full stack and DevOps skills overlap. Businesses can hire developers individually, outsource a full project, or set up an offshore team that they manage directly.
2. Rackspace Technology
Rackspace Technology sells managed Kubernetes through its Managed Platform for Kubernetes, built with Platform9 and available since April 2021. It covers Rackspace's own bare metal Kubernetes, Amazon EKS and Azure AKS, and includes monitoring, alerting and help with platform upgrades. Support comes from certified Kubernetes administrators. Companies that want engineers rather than a managed platform can use Rackspace Elastic Engineering, which supplies a flexible team for Kubernetes work.
3. WebClues Infotech
WebClues Infotech is a software development company based in Ahmedabad, with offices in the USA, UAE, UK, Canada and Australia. Its Cloud and DevOps practice covers CI/CD, deployment pipelines and managed infrastructure, and its engineers work across AWS, Azure and Google Cloud with Docker and Kubernetes. For teams that need CI/CD and Kubernetes services without building a department first, it offers full-time engineers at 160 hours a month, dedicated teams of one project manager, one QA specialist and four developers, hourly engagements from 24 hours, and offshore development centres of 20 people or more. Its hiring pages also mention a one-week free trial.
4. Thoughtworks
Thoughtworks is a technology consultancy known for its work on continuous delivery, the practice of releasing software in small, frequent, automated steps. The book Continuous Delivery was written by Jez Humble and David Farley while both worked there, and the company publishes the Technology Radar, a twice-yearly review of tools. For Kubernetes projects it fits organisations that need help with platform engineering and delivery habits as much as cluster setup. Work is usually organised as consulting projects with mixed teams.
5. HourlyDeveloper
HourlyDeveloper is an India-based provider that places developers on hourly, part-time or full-time terms. Its DevOps hiring page offers junior, advanced and senior engineers for CI/CD work and says the client always holds the source code. It also offers a free initial consultation. Kubernetes depth varies by engineer, so ask for profiles with cluster and Helm experience and hold a short technical call before committing. The hourly model works well for a proof of concept or a short audit, such as a cost review of an existing cluster.
6. EPAM Systems
EPAM Systems is a large engineering services company, founded in 1993 and headquartered in Newtown, Pennsylvania. It builds and runs software for enterprises, including cloud migration, DevOps and platform engineering work. Its scale suits multi-cluster programmes, regulated industries and projects that need dozens of engineers across time zones. Clients typically engage EPAM for project delivery or long-running dedicated teams.
7. DevOpsGroup
DevOpsGroup is a Cardiff-based consultancy that joined Sourced Group, an Amdocs company, in 2021. Its services have centred on Azure, DevOps engineering and containerisation with Kubernetes, with 24x7 managed services for critical applications and DevOps training for in-house teams. That mix suits companies on Microsoft's cloud that want pipelines built and then handed to their own staff. Engagements run as consulting projects, managed services or skills training.
8. HireAIDeveloper
HireAIDeveloper focuses on dedicated AI and machine learning developers. Kubernetes matters to these teams because 66% of organisations that host generative AI models use it for some or all of their inference work, according to the CNCF's January 2026 survey. A team building model-serving systems needs someone who can handle GPU scheduling, autoscaling and deployment pipelines alongside the data scientists. Businesses can add dedicated AI developers to an existing DevOps team or scope a defined project.
9. BairesDev
BairesDev was founded in 2009 and serves clients mainly in the United States from teams in Latin America, which gives good time zone overlap with US business hours. It provides software outsourcing, team augmentation and dedicated teams, with DevOps and cloud engineers among the roles. Kubernetes experience varies by engineer, so request profiles and test practical skills. Augmentation suits a company with a product team that needs two or three infrastructure specialists.
10. CloudBees
CloudBees supplies software, not engineers. Its CloudBees CI is an enterprise edition of Jenkins that can run on Kubernetes, and CloudBees Unify adds a control layer for governance and release management across Jenkins, GitHub Actions and other tools. Support for the Kubernetes Gateway API in CloudBees CI is in beta, which matters after the Ingress NGINX retirement. Companies buy it through subscriptions with vendor support. It makes sense when you already employ DevOps staff and want to standardise a sprawling set of pipelines.
11. N-iX
N-iX is a software engineering company with roots in Ukraine and delivery centres across Eastern Europe. Its services include DevOps, cloud engineering and software product development. Clients usually work with N-iX through dedicated development teams or fixed project scopes. European and UK clients get convenient working hours. Ask for named Kubernetes projects and the engineers who delivered them.
12. Kyndryl
Kyndryl, the infrastructure services company that separated from IBM in 2021, builds and runs container platforms mainly through Red Hat OpenShift. Its services cover advise, build, move and manage stages, and its 2023 OpenShift offerings added application modernisation and automation with Red Hat Ansible. This route fits large enterprises with mixed on-premises and cloud estates. Engagements are enterprise service contracts, including managed operation of the platform.
13. Andela
Andela is a talent marketplace that matches companies with pre-vetted remote engineers, including DevOps and cloud specialists, from several regions. It is a way to add people to a team you already manage. Platform design stays with you. Businesses can engage engineers on contract or hire them long term. Check how Kubernetes skills were assessed, since a general vetting process may not test cluster operations.
14. ScienceSoft
ScienceSoft is an IT consulting and software development company founded in 1989 and based in McKinney, Texas. It offers DevOps consulting and implementation that includes CI/CD pipelines, infrastructure as code, containerisation and monitoring. The company works on a project basis and also provides support after launch. It states that it holds ISO 27001 certification, which matters for clients in healthcare or finance that face security audits.
15. GlobalLogic
GlobalLogic is a digital engineering company that became part of the Hitachi group in 2021. It builds products and platforms for large clients in sectors such as automotive, healthcare and media, with cloud and DevOps engineering as part of that work. Teams are assigned per programme and can be large, which suits long, complex builds. A single cluster migration may start faster with a smaller provider.
KEY TAKEAWAY. Match the engagement model to your gap: staffing for extra hands, project delivery for an outcome, CloudBees for tooling.
Kubernetes developer vs DevOps engineer vs platform engineer: who should you actually hire?
Job boards use these titles loosely. The overlap is real, but the daily work differs, and hiring the wrong one stalls projects even when the best DevOps developers for Kubernetes are on the payroll.
Kubernetes developer
This person works closest to the application. They write the manifests and Helm charts, which are templates for Kubernetes settings, tune requests and limits, set up health checks and manage rollouts for specific workloads.
DevOps engineer
This role spans development and operations. It builds CI/CD pipelines, automates infrastructure, owns the release process and runs monitoring. A Kubernetes DevOps engineer is a DevOps engineer whose daily tool happens to be the cluster.
Platform engineer
This person builds the internal platform that other developers use to deploy without filing tickets: templates, guardrails and a self-service portal.
Which role fits your project?
One application moving to containers calls for a Kubernetes developer or a DevOps engineer. Ten teams shipping weekly call for a platform engineer. A startup with one product usually does better with one strong DevOps engineer who has real cluster experience than with three narrow specialists.
Managed Kubernetes vs self-managed Kubernetes: what should your development team recommend?
Settle this before the first engineer starts, because it shapes both budget and hiring.
Managed Kubernetes services
Amazon EKS, Azure Kubernetes Service (AKS) and Google Kubernetes Engine (GKE) run the control plane for you. The provider patches it, keeps it available and scales it. You still own your workloads, node settings, security rules and cost. EKS charges $0.10 per cluster per hour under standard support and $0.60 after a version passes its 14-month standard window, so skipped upgrades carry a price (AWS EKS pricing page).
Self-managed Kubernetes
You run the control plane yourself on virtual machines or your own hardware. That gives control over versions, networking plugins and placement, which some regulated industries and on-premises setups need. It also means handling etcd backups, certificate renewals, upgrades and security patches. Kubernetes ships about three minor releases a year, each patched for roughly 14 months, so upgrades keep coming.
How to decide between the two
A DevOps outsourcing company can run either option for you, which changes the question from who has the skills to who is accountable at 3 a.m.
Your Kubernetes cluster is running, but is it actually running efficiently?
A cluster that works on launch day can drift into waste within months. These five problems are the ones most often missed.
Overprovisioned infrastructure
Compare what workloads request with what they use. Cast AI's 2026 report found average CPU use of 8% and memory use of 20%. Engineers pad requests to avoid crashes, templates copy those numbers to every service, and autoscalers then add machines to satisfy the inflated requests.
Pods that keep restarting
Common causes are an app that crashes at startup because of a missing setting, memory use that passes the limit so the container is killed (OOMKilled), and a liveness probe, which is a health check, that is too strict and kills a healthy but slow app. After repeated failures Kubernetes waits longer between restarts, up to five minutes. That state is called CrashLoopBackOff.
Slow application scaling
The Horizontal Pod Autoscaler checks metrics about every 15 seconds by default, but new machines can take minutes to join the cluster. If requests are wrong, the autoscaler's maths is wrong too. And scaling on CPU does nothing when the real bottleneck is a database.
Deployment bottlenecks
Releases stay slow when pipelines wait on manual approvals, long test suites or large images that take minutes to pull. Measure each stage before buying faster tools.
Difficulties in identifying production issues
When a user reports that checkout is slow, which of eight services is at fault? Logs show events, metrics show trends, and distributed tracing shows the path of one request. Gaps are common. The metrics-server component keeps only recent readings, so last week's spike leaves no trace unless Prometheus stores history.
Experienced container orchestration experts start with measurement, change one setting at a time and compare before and after.
What should a Kubernetes project look like before it goes live?
These are the stages that matter for a production cluster.
1. Infrastructure planning. Pick the cloud, the region and the cluster layout, and decide how many environments you need.
2. Container preparation. Package each application into a small, scanned image with pinned versions.
3. Cluster configuration. Set up nodes, networking, storage and resource requests.
4. Deployment automation. Build pipelines with approvals, staged releases and a tested rollback, as part of your CI/CD and Kubernetes services.
5. Security implementation. Apply role-based access, secrets handling and network policies.
6. Production readiness. Run load tests above expected peak, delete pods and nodes on purpose to watch recovery, and confirm alerts fire.
Kubernetes security: what should businesses never leave to default settings?
Default settings favour getting started quickly, not locking things down. Experienced container orchestration experts change these early.
Role-based access control (RBAC)
Give each person and service only the permissions its job needs. Daily work should never run with cluster-admin rights.
Container image security
Scan images in the pipeline, use small base images and block images that failed scanning from running.
Secrets management
Keep passwords and API keys out of code and configuration files. Secrets are encoded, not encrypted, unless encryption at rest is enabled, so many teams add an external secrets manager.
Network policies
By default any pod can talk to any other pod. Network policies close that gap, but they only work if the cluster's network plugin supports them, a detail teams often find late.
Cluster updates and vulnerability management
Old versions stop receiving patches. On EKS they also cost six times more in control plane fees once the standard window ends.
The Kubernetes cost problem: why scaling applications can increase infrastructure bills
Cloud bills follow what you reserve, not what you use. Every pod's request reserves capacity, and every reservation can trigger another machine.
BY THE NUMBERS
• Average CPU use across Cast AI's 2026 dataset was 8%, with memory at 20%.
• GPU use for AI and machine learning workloads averaged 5%.
• Fewer than 2% of GPUs ran on cheaper Spot capacity through 2025.
• AWS raised H200 Capacity Block prices by 15% in January 2026.
• Find unused resources and oversized clusters first, since they are the cheapest wins.
• Review CPU and memory requests against real usage over several weeks, not a single day.
• Set a maximum on autoscaling. Scaling up happens automatically, but scaling down needs rules.
• Use the cloud provider's cost tools, and tag resources by team so bills have an owner.
• Keep GPU nodes separate and shut them down when idle.
A good engineer treats performance and spending as one problem. If you plan to hire Kubernetes developers, ask for a cost review in the first month.
Questions to ask a DevOps developer before trusting them with your Kubernetes infrastructure
Scenario questions show how someone thinks. Listen to the order of their steps.
1. How would you investigate a pod that repeatedly enters CrashLoopBackOff? Good answers start with the pod's events and previous logs, then check memory limits, health probes and missing configuration.
2. What would you do if a deployment caused unexpected downtime? Look for roll back first, investigate second, and a written review afterwards.
3. How would you manage secrets across development, staging and production? Look for separate secrets per environment, an external secrets manager or encryption at rest, and no passwords in the repository.
4. When would you use horizontal pod autoscaling instead of adding node capacity? A strong answer separates more copies of an app from more machines, and mentions that node startup is slower.
5. How would you migrate an application from a traditional server to Kubernetes? Listen for a staged plan, a parallel run, a data plan and a way back.
6. How would you reduce infrastructure costs without hurting performance? Expect measurement of real usage first, then rightsizing, then Spot capacity for workloads that tolerate interruption.
7. What is your approach to cluster upgrades and rollback planning? Good answers include testing in staging, checking removed APIs, upgrading one minor version at a time and having backups ready. A Kubernetes DevOps engineer with real upgrade experience will mention the deprecation notices without prompting.
When does your business need a dedicated Kubernetes developer instead of general DevOps support?
Specialised Kubernetes skill pays off in these situations:
• Moving several applications to containers.
• Running complex clusters, especially across regions or clouds.
• Building a microservices application.
• Improving deployment automation across many environments.
• Chasing recurring performance problems.
• Setting up an internal developer platform.
• Meeting security and compliance rules.
A general DevOps engineer can be enough when you run one or two applications on a managed cluster with a small team and steady traffic. In that case a DevOps outsourcing company or one part-time specialist may cover the work. When the cluster becomes the product's backbone, it is time to hire Kubernetes developers who work on it daily.
Conclusion
Kubernetes projects need application knowledge, infrastructure skill, automation and security thinking in one head or one team. The right hire depends on how complex the project is, what infrastructure you run and who will look after it in two years.
Judge candidates on what they have operated. Ask about outages they fixed, upgrades they planned and bills they reduced. Certificates and job titles say little about that. The best DevOps developers for Kubernetes are the ones who can explain a hard decision in words your whole team understands.
