How fast can the pod start?

Within 24 hours of greenlight after a 3-day free trial. The trial runs against a real scoped task, so you see the engineering depth before you sign anything. Replacement is free within 14 days if the fit is wrong.

Devlyn AI · Kubernetes

Kubernetes pods, owned by us. Embedded with you.

Senior Kubernetes engineers under one retainer, with AI-augmented workflows that compress 100 hours of typical work to 25. Deployed in 24 hours.

Where $Kubernetes fits

Kubernetes pods ship production-grade container orchestration including Helm chart authoring with reusable chart libraries, GitOps-driven deployment workflows with Argo CD or Flux for declarative cluster management, service-mesh implementation with Istio or Linkerd for traffic management, mutual TLS, and observability, policy controls with OPA Gatekeeper or Kyverno for admission-controller enforcement, full observability stacks (Prometheus, Grafana, OpenTelemetry Collector) for metrics, logs, and traces, and platform-engineering toolchains providing developer self-service portals. Devlyn engineers ship Kubernetes with security-first defaults including pod-security standards, network policies, and image-scanning pipelines, cost-aware autoscaling with HPA, VPA, and cluster-autoscaler configuration, and multi-tenant namespace isolation for shared-cluster environments.

AI-augmented Kubernetes workflows lean on Cursor and Claude Code for Helm chart scaffolding with values schema validation, Kubernetes manifest generation with proper resource limits, requests, and security contexts, custom operator patterns using the Operator SDK with reconciliation-loop boilerplate, and policy-test generation using conftest or chainsaw — all under senior validation that owns architecture decisions, security-posture review (pod security admission, network policies, RBAC configuration, secret management with External Secrets Operator), cost-optimisation strategy (right-sizing, spot-node pools, bin-packing configuration), and cluster-upgrade planning with proper PodDisruptionBudget and rolling-update configuration. Compression shows up strongest in manifest scaffolding, Helm chart boilerplate, and policy-test generation.

Kubernetes engagements at Devlyn typically run as one senior platform engineer plus shared backend for $6,000–$11,000/month, covering cluster architecture, GitOps pipeline design, and observability stack configuration. This scales to a two- or three-engineer pod when the roadmap splits into parallel lanes across platform infrastructure (networking, ingress, service mesh), security and compliance (RBAC, policy enforcement, image scanning, secret rotation), and developer-experience tooling (self-service portals, CI/CD integration, namespace provisioning). Pods share a single retainer with flexible allocation.

Book a discovery call →

Where Kubernetes pods land today

Six combinations that show up most often in the last few quarters of Kubernetes discovery calls — vertical, geography, and the named-risk pattern each engagement designed around.

Kubernetes · AI Startup · San Francisco

Kubernetes for AI Startup in San Francisco

The most common 2026 AI-startup engineering trap is shipping LLM-powered features without deterministic-test wrapping of stochastic systems, creating quality regressions that are invisible until users report hallucinations or incorrect outputs at scale. Kubernetes pods compress the work — kubernetes pods ship production-grade container orchestration including helm chart authoring with reusable chart libraries, gitops-driven deployment workflows with argo cd or flux for declarative cluster management, service-mesh implementation with istio or linkerd for traffic management, mutual tls, and observability, policy controls with opa gatekeeper or kyverno for admission-controller enforcement, full observability stacks (prometheus, grafana, opentelemetry collector) for metrics, logs, and traces, and platform-engineering toolchains providing developer self-service portals. On the Pacific (PT) calendar, fte hiring in sf has slowed structurally since 2024 layoffs but compensation expectations have not.

Read the full brief →

Kubernetes · Fintech · London

Kubernetes for Fintech in London

The most common 2026 fintech engineering trap is shipping a feature that depends on a partner-bank integration that has not been contractually signed or technically certified, creating a rollback scenario that wastes months of engineering effort. Kubernetes pods compress the work — kubernetes pods ship production-grade container orchestration including helm chart authoring with reusable chart libraries, gitops-driven deployment workflows with argo cd or flux for declarative cluster management, service-mesh implementation with istio or linkerd for traffic management, mutual tls, and observability, policy controls with opa gatekeeper or kyverno for admission-controller enforcement, full observability stacks (prometheus, grafana, opentelemetry collector) for metrics, logs, and traces, and platform-engineering toolchains providing developer self-service portals. On the GMT / BST calendar, london fte hiring runs 3–5 months for senior fintech and ai roles, with offers regularly contested by us tech giants opening uk offices.

Read the full brief →

Kubernetes · B2B SaaS · Seattle

Kubernetes for B2B SaaS in Seattle

The most common 2026 B2B SaaS engineering trap is integration-first roadmaps that fragment the codebase into per-customer hacks and one-off webhook handlers, creating a maintenance debt spiral that slows all future feature work. Kubernetes pods compress the work — kubernetes pods ship production-grade container orchestration including helm chart authoring with reusable chart libraries, gitops-driven deployment workflows with argo cd or flux for declarative cluster management, service-mesh implementation with istio or linkerd for traffic management, mutual tls, and observability, policy controls with opa gatekeeper or kyverno for admission-controller enforcement, full observability stacks (prometheus, grafana, opentelemetry collector) for metrics, logs, and traces, and platform-engineering toolchains providing developer self-service portals. On the Pacific (PT) calendar, seattle fte pipelines compete with faang-tier salaries that startup budgets cannot match.

Read the full brief →

Kubernetes · Healthtech · Boston

Kubernetes for Healthtech in Boston

The most common 2026 healthtech engineering trap is shipping a clinical feature that has not been reviewed against HIPAA BAA requirements or FDA SaMD classification boundaries, creating regulatory exposure that can halt the entire product. Kubernetes pods compress the work — kubernetes pods ship production-grade container orchestration including helm chart authoring with reusable chart libraries, gitops-driven deployment workflows with argo cd or flux for declarative cluster management, service-mesh implementation with istio or linkerd for traffic management, mutual tls, and observability, policy controls with opa gatekeeper or kyverno for admission-controller enforcement, full observability stacks (prometheus, grafana, opentelemetry collector) for metrics, logs, and traces, and platform-engineering toolchains providing developer self-service portals. On the Eastern (ET) calendar, boston fte pipelines run 4–6 months for senior backend roles.

Read the full brief →

Kubernetes · Logistics · Amsterdam

Kubernetes for Logistics in Amsterdam

The most common 2026 logistics engineering trap is shipping a routing-optimisation feature that fails under carrier-API outage or peak-season volume surge, creating delivery-promise violations at the worst possible time. Kubernetes pods compress the work — kubernetes pods ship production-grade container orchestration including helm chart authoring with reusable chart libraries, gitops-driven deployment workflows with argo cd or flux for declarative cluster management, service-mesh implementation with istio or linkerd for traffic management, mutual tls, and observability, policy controls with opa gatekeeper or kyverno for admission-controller enforcement, full observability stacks (prometheus, grafana, opentelemetry collector) for metrics, logs, and traces, and platform-engineering toolchains providing developer self-service portals. On the CET / CEST calendar, amsterdam fte pipelines run 2–4 months for senior backend roles.

Read the full brief →

Kubernetes · Govtech · Washington DC

Kubernetes for Govtech in Washington DC

The most common 2026 govtech engineering trap is shipping a feature that fails Section 508 accessibility testing or FISMA audit-trail requirements late in the procurement evaluation cycle, disqualifying the product from the award after months of engineering investment. Kubernetes pods compress the work — kubernetes pods ship production-grade container orchestration including helm chart authoring with reusable chart libraries, gitops-driven deployment workflows with argo cd or flux for declarative cluster management, service-mesh implementation with istio or linkerd for traffic management, mutual tls, and observability, policy controls with opa gatekeeper or kyverno for admission-controller enforcement, full observability stacks (prometheus, grafana, opentelemetry collector) for metrics, logs, and traces, and platform-engineering toolchains providing developer self-service portals. On the Eastern (ET) calendar, dc fte pipelines for cleared roles run 6–9 months.

Read the full brief →

What Kubernetes depth at Devlyn looks like

Common use cases

AI-augmented angle

Engagement shape & pricing

Ecosystem fluency

Kubernetes ecosystem depth covers the full modern CNCF surface: Helm for package management with chart repositories, Argo CD and Flux for GitOps-driven deployment, Istio and Linkerd for service mesh with traffic management and mTLS, OPA Gatekeeper and Kyverno for policy enforcement, Prometheus for metrics collection with AlertManager, Grafana for dashboarding and visualisation, OpenTelemetry Collector for trace and log aggregation, Cilium for eBPF-based networking and security, cert-manager for automated TLS certificate management, External Secrets Operator for secret synchronisation, Karpenter for intelligent node provisioning, and Crossplane for infrastructure composition. Devlyn engineers operate fluently across this entire surface with security-first, cost-aware production patterns.

Real outcomes

Calenso · Switzerland

4× productivity

5,000+ integrations on the platform after AI-augmented engineering replaced manual workflows.

Creator.ai

6 weeks → 1 week

6× faster delivery, 2× output per engineer, 50% leaner team.

Klaviss · USA

$4,800/mo pod

Two engineers + PM + shared DevOps. Real-estate platform overhaul shipped in 8 weeks.

Haxi.ai · Middle East

AI engagement at scale

Real-time, context-aware AI conversations across platforms — spec to production by one pod.

Continue browsing

Verticals where Kubernetes ships well

Kubernetes pods most often run engagements in the verticals below. Each links through to a vertical-level hub with named risks, compliance posture, and key metrics.

Metros where Kubernetes pods deploy

Hand-picked cities where Kubernetes engagements show up most. Each city has its own time-zone alignment and hiring-climate notes on the metro hub.

Common questions about Kubernetes engagements

What does a Kubernetes pod actually own end-to-end?

Architecture, security review, and the Kubernetes-specific patterns that production-grade work requires. Kubernetes pods ship production-grade container orchestration including Helm chart authoring with reusable chart libraries, GitOps-driven deployment workflows with Argo CD or Flux for declarative cluster management, service-mesh implementation with Istio or Linkerd for traffic management, mutual TLS, and observability, policy controls with OPA Gatekeeper or Kyverno for admission-controller enforcement, full observability stacks (Prometheus, Grafana, OpenTelemetry Collector) for metrics, logs, and traces, and platform-engineering toolchains providing developer self-service portals. Devlyn engineers ship Kubernetes with security-first defaults including pod-security standards, network policies, and image-scanning pipelines, cost-aware autoscaling with HPA, VPA, and cluster-autoscaler configuration, and multi-tenant namespace isolation for shared-cluster environments.
How does AI-augmented Kubernetes differ from a single contractor using AI tools?

AI-augmented Kubernetes workflows lean on Cursor and Claude Code for Helm chart scaffolding with values schema validation, Kubernetes manifest generation with proper resource limits, requests, and security contexts, custom operator patterns using the Operator SDK with reconciliation-loop boilerplate, and policy-test generation using conftest or chainsaw — all under senior validation that owns architecture decisions, security-posture review (pod security admission, network policies, RBAC configuration, secret management with External Secrets Operator), cost-optimisation strategy (right-sizing, spot-node pools, bin-packing configuration), and cluster-upgrade planning with proper PodDisruptionBudget and rolling-update configuration. Compression shows up strongest in manifest scaffolding, Helm chart boilerplate, and policy-test generation. The 4× compression comes from pod-level workflow design, not from individual tool adoption.
What does a Kubernetes engagement typically cost?

Kubernetes engagements at Devlyn typically run as one senior platform engineer plus shared backend for $6,000–$11,000/month, covering cluster architecture, GitOps pipeline design, and observability stack configuration. This scales to a two- or three-engineer pod when the roadmap splits into parallel lanes across platform infrastructure (networking, ingress, service mesh), security and compliance (RBAC, policy enforcement, image scanning, secret rotation), and developer-experience tooling (self-service portals, CI/CD integration, namespace provisioning). Pods share a single retainer with flexible allocation.
Which Kubernetes ecosystem libraries does Devlyn cover?

Kubernetes ecosystem depth covers the full modern CNCF surface: Helm for package management with chart repositories, Argo CD and Flux for GitOps-driven deployment, Istio and Linkerd for service mesh with traffic management and mTLS, OPA Gatekeeper and Kyverno for policy enforcement, Prometheus for metrics collection with AlertManager, Grafana for dashboarding and visualisation, OpenTelemetry Collector for trace and log aggregation, Cilium for eBPF-based networking and security, cert-manager for automated TLS certificate management, External Secrets Operator for secret synchronisation, Karpenter for intelligent node provisioning, and Crossplane for infrastructure composition. Devlyn engineers operate fluently across this entire surface with security-first, cost-aware production patterns.
How fast can the pod start?

Within 24 hours of greenlight after a 3-day free trial. The trial runs against a real scoped task, so you see the engineering depth before you sign anything. Replacement is free within 14 days if the fit is wrong.

When the next move is a conversation

Book a 30-minute discovery call. We will scope a Kubernetes pod against your roadmap and timeline. No contracts. No commitment. Or run the Pod ROI Calculator against your current vendor's burn first.