4 posts tagged with "reliability"

Last Updated: May 2026

The LiteLLM proxy container does 2 very different things. It's an LLM data plane, /chat/completions, /v1/messages, embeddings, passthroughs, where latency is measured in single-digit milliseconds of overhead and traffic is high-volume and bursty. It's also a management control plane — keys, teams, SSO, audit logs, and the spend/usage analytics that power the dashboard, where a single request can scan millions of rows.

Run both on the same event loop, and the slowest thing the control plane does sets the reliability floor for the fastest thing the data plane does. This post is about why that coupling can cause outages, and the componentized deployment model that can help improve reliability at scale.

Last Updated: April 2026

Enterprise AI Gateway deployments put Redis in the hot path for nearly every request: rate limiting, cache lookups, spend tracking. When Redis is healthy, the latency contribution is single-digit milliseconds — invisible to end users. When it degrades, a production AI Gateway needs to stay up regardless.

Running LiteLLM at scale across 100+ pods means designing for failure modes before they appear. The easy case is Redis going fully down: fail fast, fall through to the database, continue serving requests. The hard case — the one that takes down gateways — is a slow Redis: still accepting connections, still responding, but timing out after 20-30 seconds per operation.

Thank you to everyone who joined our April town hall.

We used the session to share our CI/CD v2 improvements, product stability work, and what we are prioritizing next across reliability and product roadmap.

As LiteLLM adoption has grown, so have expectations around reliability, performance, and operational safety. Meeting those expectations requires more than correctness-focused tests, it requires validating how the system behaves over time, under real-world conditions.

This post introduces LiteLLM Observatory, a long-running release-validation system we built to catch regressions before they reach users.