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OpenProxyAI vs. LiteLLM: the engine vs. the car

· 3 min read
Rayyan Oumlil
Founder, OpenProxyAI

LiteLLM comes up in almost every conversation about self-hosted LLM gateways, and it should — it's the library a large share of this category, OpenProxyAI included, is built on top of for provider abstraction. So the honest framing isn't "OpenProxyAI vs. LiteLLM" as competitors. It's closer to: LiteLLM is the engine, and OpenProxyAI is what you get when you build the rest of the car around it.

What LiteLLM actually is​

LiteLLM is an open-source proxy and Python SDK that gives you a single OpenAI-compatible interface across 100+ providers, with routing, spend tracking, and caching. You self-host it, configure it via a proxy_config.yaml, and it does the provider-abstraction job extremely well — it's a dependable, widely-adopted piece of infrastructure, and if all you need is "one API, many providers, budget tracking," it's a completely reasonable place to stop.

Where the two diverge​

The gap shows up once "route to the right model" stops being the whole requirement.

  • Policy enforcement. OpenProxyAI's pipeline runs PII redaction, secrets scanning, topic guarding, and prompt-injection detection on every request as a core stage — not a separate service you wire in. LiteLLM's core proxy is routing- and budget-focused; equivalent guardrail behavior generally means integrating something else in front of or alongside it.
  • Compliance starting points. OpenProxyAI ships Healthcare, Finance, and Government policy templates you apply and customize. LiteLLM doesn't have an equivalent — you're building your policy configuration from scratch.
  • Admin surface. LiteLLM is primarily config-file and CLI driven, with enterprise features like chargeback attribution gated behind a separate licensed tier. OpenProxyAI ships a full admin console — teams, budgets, policies, audit log — as the default way to operate it, not an add-on.
  • Audit trail. OpenProxyAI's logging is immutable by design and built for handing to an auditor. LiteLLM's logging is flexible and integrates with a wide range of observability backends, but "audit-ready by default" isn't the framing it's built around.

Where LiteLLM is the better call​

If you're a single team that needs multi-provider routing and cost tracking, and you're comfortable owning the guardrail and compliance layer yourself (or you don't need one yet), LiteLLM's simplicity is a real advantage — it's less software to run, and its provider coverage is excellent. Plenty of OpenProxyAI's own architecture leans on the same library for exactly that reason.

The honest split: pick LiteLLM if the org chart approving your AI usage is small. Pick OpenProxyAI if there's a compliance or security team in that approval chain who needs redaction, an audit trail, and policy enforcement to already be there — not bolted on later.