Kubernetes Migration Guide: Strategy, Networking, and Cutover

Introduction

A Kubernetes migration changes both applications and their operating model. It moves workloads from virtual machines, physical hosts, legacy container platforms, or older ingress systems into a Kubernetes-based environment. The work includes much more than packaging a process in a container: teams must redesign configuration, identity, networking, traffic entry, storage, observability, delivery, resilience, and ownership. The API Infrastructure Guide provides the wider context for these platform capabilities.

The safest migrations are incremental. They establish a target platform and reliability baseline, move a representative workload, shift a controlled share of traffic, verify business and technical outcomes, and retain a tested rollback path. A big-bang migration combines application, infrastructure, networking, and organizational change into one high-risk event. Use the API Observability Guide and API Security Guide to define the baseline before traffic moves.

This guide is for platform engineers, SREs, cloud architects, application teams, and infrastructure leaders. It covers readiness assessment, target architecture, containerization, Kubernetes networking, Ingress and Gateway API, security, observability, traffic cutover, rollback, and post-migration operations. For distributed application boundaries, connect it with the Microservices Architecture Guide and API Gateway Guide.

Define the Migration Outcome

Kubernetes is a means, not the objective. Define why the organization is migrating and how success will be measured. Typical goals include:

Standardizing application deployment and operations.
Improving release automation and rollback.
Scaling workloads more consistently.
Increasing portability across infrastructure environments.
Giving application teams safer self-service capabilities.
Consolidating traffic, policy, and observability practices.
Replacing an unsupported or fragmented ingress platform.

Translate goals into measurable outcomes such as deployment lead time, change failure rate, recovery time, availability, infrastructure utilization, platform adoption, and operational effort. Without this baseline, the migration may move workloads while preserving the same delivery bottlenecks.

Migration Readiness and Workload Assessment

Create an inventory before selecting a migration sequence. For each workload, record the operational, dependency, security, data, scaling, rollback, and regulatory details below. This inventory should become an input to the platform operating model, not a document that is discarded after the pilot.

Owner, criticality, consumers, and service-level expectations.
Runtime, operating system, dependencies, and startup behavior.
Network ports, protocols, domains, certificates, and firewall rules.
Configuration sources, secrets, and identity integrations.
Persistent data, file-system assumptions, and backup requirements.
Resource use, scaling behavior, scheduled work, and batch jobs.
Deployment frequency, maintenance windows, and rollback method.
Upstream and downstream dependencies.
Regulatory, residency, and isolation requirements.

Classify workloads by migration approach:

Rehost or containerize with minimal change: suitable for stateless applications with clear dependencies.
Replatform: adapt configuration, storage, health checks, and delivery to Kubernetes primitives.
Refactor: redesign tightly coupled or stateful components when the existing architecture prevents safe operation.
Retain: keep workloads outside Kubernetes when migration provides insufficient value.
Retire or replace: remove unused systems or adopt a managed capability.

Start with a representative but non-critical workload. An unrealistically simple pilot proves the cluster works but may not test identity, state, traffic, observability, and ownership needed by the broader estate.

Target Kubernetes Platform Architecture

A target architecture defines the shared capabilities every migrated workload can rely on. It should connect the API gateway runtime, Kubernetes Services, identity and policy, and the observability baseline without hiding ownership boundaries.

flowchart TD
  users[Users and API Consumers] --> edge[DNS, CDN, or Load Balancer]
  edge --> gateway[Ingress Controller or Gateway API Data Plane]
  gateway --> services[Kubernetes Services]
  services --> workloads[Application Pods]
  control[Platform Control Plane] --> gateway
  control --> workloads
  identity[Identity, Secrets, and Policy] --> gateway
  identity --> workloads
  workloads --> telemetry[Metrics, Logs, and Traces]
  gateway --> telemetry
  telemetry --> operations[Dashboards, Alerts, and Incident Response]

Decide early:

Cluster topology, regions, availability zones, and failure domains.
Namespace, tenancy, resource quota, and isolation model.
Container registry and software supply-chain controls.
Continuous delivery and configuration management approach.
Networking, DNS, service discovery, and egress policy.
Ingress controller or Gateway API implementation.
Identity, secrets, certificates, and policy enforcement.
Observability pipelines and operational ownership.
Backup, restore, disaster recovery, and upgrade strategy.

The platform should expose supported interfaces and defaults, not force every application team to assemble infrastructure independently. Continue with the Platform Engineering Guide for the product and operating-model perspective.

Containerization and Configuration

Build Predictable Images

Use minimal, maintained base images and reproducible builds. Run as a non-root user where possible, avoid unnecessary packages, and scan dependencies and images. Pin important versions and record provenance. A container should package the application, not environment-specific secrets or mutable runtime data.

Externalize Configuration

Move environment-specific configuration from local files and images into controlled configuration and secret systems. Define which changes require a new deployment and which can be updated dynamically. Protect secrets from source control, image layers, command output, and logs.

Define Health and Lifecycle Behavior

Kubernetes uses startup, readiness, and liveness probes for different decisions. Startup probes protect slow initialization. Readiness determines whether a Pod should receive traffic. Liveness determines whether the container should restart. A single generic endpoint for all three can cause unnecessary restarts or route traffic before dependencies are ready.

Handle termination signals, stop accepting new work, drain connections, and complete or safely abandon in-flight requests before the termination grace period ends.

Set Resource Requests and Limits

Measure CPU and memory behavior under representative load. Requests influence scheduling; limits constrain resource use. Defaults copied across all applications can create low utilization, throttling, or eviction. Revisit settings after production traffic is observed.

Networking, Ingress, and Gateway API

Kubernetes Pods are ephemeral, so traffic entry and service discovery need stable control points. Services provide stable discovery and load balancing across changing Pod endpoints. External traffic typically enters through a load balancer plus an Ingress controller or Gateway API implementation. The API Gateway Guide explains the additional policy and consumer controls that may sit beyond basic ingress.

The Kubernetes networking documentation explains Services, cluster networking, Gateway API, Ingress, and NetworkPolicy. Kubernetes notes that the Ingress API is frozen and recommends Gateway for new capabilities, while existing Ingress remains supported.

Ingress Migration

Inventory every host, path, rewrite, redirect, timeout, certificate, source restriction, authentication rule, and vendor-specific annotation. Many annotations do not map directly between controllers. Test semantics rather than translating configuration mechanically.

Read Comparing Kubernetes Gateway and Ingress APIs and A Quick Glance at Kubernetes Gateway API. The official Gateway API migration guide maps core Ingress concepts to Gateway API but does not replace a live traffic migration plan.

Gateway API and Role Boundaries

Gateway API separates infrastructure, gateway, and route responsibilities. This can let platform teams manage listeners and shared infrastructure while application teams own routes within policy. Verify that the selected implementation supports the required API version, protocol, policies, and operational features.

API Gateway Capabilities

An API gateway may add consumer authentication, rate limiting, transformations, canary traffic, protocol support, and API-level observability beyond basic cluster ingress. See Cloud-Native API Gateway on Kubernetes and Deploy an API Gateway on Kubernetes.

Security and Identity Baseline

Define security before production cutover:

Use workload and human identities with least privilege.
Apply RBAC to clusters, namespaces, APIs, and deployment automation.
Store secrets in a controlled system and rotate them.
Enforce image provenance and admission policy according to risk.
Restrict Pod privileges and host access.
Apply NetworkPolicy where the network implementation enforces it.
Encrypt external traffic and decide where internal TLS or mTLS is required.
Protect the Kubernetes API, gateway control plane, and administrative endpoints.
Record audit and policy events.

The API Security Guide covers identity, authorization, runtime protection, inventory, and response. Kubernetes controls do not replace application-level authorization or API security policy.

Observability and Reliability Baseline

Do not migrate production traffic until teams can detect and investigate failure. Establish:

Cluster, node, workload, gateway, and application metrics.
Structured application and access logs with ownership context.
Distributed trace propagation across the gateway and services.
Dashboards for traffic, errors, latency, saturation, and deployment changes.
SLOs and alerts based on consumer impact.
Runbooks for common platform and application failures.
Capacity, quota, and cost visibility.

The API Observability Guide provides the telemetry and SLO model. Test it by creating controlled failures and confirming that alerts, dashboards, logs, traces, and ownership information lead to a useful diagnosis.

Incremental Traffic Cutover

A migration seam lets the old and new environments run in parallel while traffic moves gradually. DNS, a load balancer, or an API gateway can route by hostname, path, header, consumer, or percentage.

flowchart LR
  clients[Clients] --> gateway[Migration Traffic Gateway]
  gateway -->|Stable traffic| legacy[Legacy Environment]
  gateway -->|Canary traffic| kubernetes[Kubernetes Environment]
  legacy --> data[(Existing Data Systems)]
  kubernetes --> data
  gateway --> observe[Shared Observability]
  legacy --> observe
  kubernetes --> observe

Use a staged process:

Route internal or synthetic traffic to Kubernetes.
Validate functionality, security, telemetry, and dependency behavior.
Enable a small production cohort or percentage.
Compare latency, errors, resource use, and business outcomes with the legacy path.
Increase traffic through explicit checkpoints.
Pause or reverse when thresholds are exceeded.
Complete cutover only after sustained validation.

Canary routing must preserve session, identity, data, and cache behavior. If both environments write data, define consistency and rollback semantics before the first request moves. Read Automating Canary Release Decisions in Kubernetes for an example of metric-driven progression.

Rollback, Disaster Recovery, and Validation

A rollback plan is part of the design, not an emergency improvisation.

Define:

The decision owner and rollback thresholds.
How traffic returns to the legacy environment.
Whether data written in Kubernetes remains compatible.
How configuration and schema changes are reversed.
How queued work and in-flight requests are handled.
Which evidence confirms rollback succeeded.
How long the legacy path remains operational.

Test backup restoration, regional or cluster failure, certificate replacement, control-plane unavailability, and dependency outage. Chaos testing can reveal assumptions before production incidents; see Building a More Robust APISIX Ingress Controller with Chaos Engineering.

Post-Migration Operations

After traffic moves, the migration is not finished. Teams must own upgrades, capacity, security patches, policy, cost, and incident response. Continue with platform engineering practices and API observability to turn the migrated cluster into a sustainable operating environment.

Remove unused legacy routes, credentials, infrastructure, and data copies.
Update architecture, ownership, runbooks, and support processes.
Review resource requests, autoscaling, and quotas using production evidence.
Track platform and application SLOs.
Exercise restore and disaster recovery regularly.
Plan Kubernetes, gateway, and add-on upgrades.
Measure whether delivery and reliability outcomes improved.
Feed repeated application needs into platform capabilities and golden paths.

Avoid leaving a permanent dual environment without clear ownership and retirement criteria. It increases cost and creates uncertainty about the system of record.

Kubernetes Migration Best Practices

Define measurable outcomes before selecting technology.
Inventory workloads, dependencies, traffic, state, and ownership.
Build the shared platform baseline before moving critical applications.
Start with a representative workload and migrate incrementally.
Treat configuration, identity, networking, and telemetry as first-class work.
Test Ingress or Gateway API semantics rather than only translating manifests.
Establish security, SLOs, alerts, and runbooks before production cutover.
Use controlled traffic shifts with explicit success and rollback thresholds.
Design data compatibility and rollback together.
Retire legacy infrastructure deliberately after sustained validation.

Kubernetes Migration Learning Paths

Platform and Workload Foundations

Networking and Traffic

Security and Operations

How API7 and Apache APISIX Fit

Apache APISIX and APISIX Ingress Controller can provide API traffic management for Kubernetes, including dynamic routing, authentication, rate limiting, canary traffic, service discovery, and telemetry integrations.

API7 Enterprise supports centralized API and gateway operations across Kubernetes and non-Kubernetes environments. This can provide a consistent traffic and policy layer while workloads move incrementally. The VM to Kubernetes solution describes related deployment and migration capabilities.

Use traffic management as the migration seam, then validate the broader application and platform operating model. A successful migration improves how teams deliver and operate software, not only where containers run.