Open Source Load Balancers

Layer 4 load balancers route connections using IP addresses and ports, so they work well for TCP and UDP services without understanding the application. Layer 7 load balancers inspect protocols such as HTTP and can route by host, path, header, cookie, or method. Layer 7 gives more control but adds parsing, retry, timeout, and protocol compatibility decisions that can affect application behavior.

Terminate TLS at the load balancer when you need centralized certificates, HTTP routing, header inspection, or request-level policy. Use pass-through when backends must see the original TLS session, client certificates, or application-specific encryption. A common compromise is TLS termination at the edge and re-encryption to upstreams. Make sure certificate rotation, SNI handling, and mTLS requirements are tested, not assumed.

Run at least two load balancer nodes and put a failover mechanism in front of them, such as floating IP, DNS-based failover, routing protocol integration, or a cloud network primitive. Then test node loss during real traffic. High availability is not just redundancy - config distribution, certificate availability, health check behavior, and stateful features such as stickiness all have to survive failover cleanly.

Benchmark the traffic shape you run, not a generic requests-per-second number. Test concurrent connections, keep-alive behavior, TLS handshakes, large uploads, slow clients, WebSocket or gRPC streams, and backend failure. Measure latency percentiles, CPU use, memory growth, and reload impact. A load balancer that looks fast on short HTTP requests can behave very differently with long-lived connections or expensive TLS settings.

Expect to rebuild behavior, not just translate syntax. Listener definitions, pools, health checks, TLS policies, redirects, header rewrites, and stickiness rules usually map conceptually, but proprietary defaults often hide timeout and retry behavior. Export existing config if possible, document every virtual service, and replay production-like traffic in a staging environment. Watch for subtle differences in status codes, headers, and connection closing.

They can, but the integration model matters. Some are designed to read Kubernetes objects through a controller, while others sit outside the cluster and treat nodes or ingress endpoints as backends. Check support for Ingress, Gateway API, service discovery, pod readiness, and graceful draining. Also decide whether the load balancer is part of the cluster lifecycle or a separate network tier owned by platform operations.

Health checks should reflect whether the backend can serve real traffic, not just whether a port is open. Look for configurable intervals, timeouts, thresholds, HTTP status expectations, and separate readiness versus liveness behavior. Flapping backends can cause bigger incidents than dead ones, so check slow-start, outlier detection, and connection draining. Good health checks reduce blast radius instead of amplifying instability.

Use sticky sessions only when the application cannot share session state or when cache locality is a proven requirement. Cookie-based affinity works well for HTTP, while source-IP affinity can break behind NAT or mobile networks. Sticky sessions complicate failover, scaling, and deployments because users can concentrate on unhealthy or old backends. If you use them, test what happens when a pinned backend disappears.

At minimum, you need metrics for request and connection rates, upstream latency, error rates, retries, rejected traffic, TLS failures, and backend health. Logs should include enough request context to debug routing decisions without capturing sensitive payloads. Trace propagation is useful for Layer 7 traffic. Make sure dashboards distinguish client errors, load balancer policy decisions, and upstream failures, or incidents will turn into guesswork.

Keep configuration, certificates, policy files, and deployment manifests in version control or another reproducible system. Backing up only the running host is not enough because the real asset is the routing and security policy. Store secrets separately with controlled access and rotation. Practice restoring a new node from scratch, including certificates and health checks, so disaster recovery does not depend on undocumented local state.

The license may reduce purchase cost, but the real cost is operation. You still need people who understand networking, TLS, Linux tuning, monitoring, capacity planning, and incident response. Commercial products may include support, UI workflows, or compliance paperwork that your team must replace. The strongest economic case usually appears when you can standardize the configuration model and automate deployments across many services.

Review the release history, vulnerability handling, dependency exposure, and hardening guidance. Run it with least privilege, restrict the admin API, and separate data-plane traffic from management access. Confirm support for modern TLS settings, safe defaults for headers, request size limits, and logging controls. If it parses HTTP at the edge, fuzzing history and independent security review are worth more than a long feature list.

They need protocol-aware timeouts and connection handling. WebSocket and gRPC streams can stay open far longer than normal HTTP requests, so idle timeouts, max connection age, backend draining, and retry behavior matter. A retry that is safe for a short idempotent request may be wrong for a stream. Test deployments and backend restarts with long-lived clients connected, not just fresh requests.

Your risk depends on how portable your configuration and operating model are. Favor plain deployment patterns, standard protocols, and config that can be transformed into another tool without reverse engineering. Keep documented tests for routing behavior, TLS policy, health checks, and failover. If you must rely on unusual extensions, isolate them so replacing the load balancer does not require redesigning every service at once.