AISIX vs LiteLLM: Which Open-Source AI Gateway in 2026?

AISIX vs LiteLLM at a glance

AISIX leads on routing intelligence (semantic routing and ensemble) and a fully Apache-2.0 data plane; LiteLLM leads on raw provider breadth and open-source budgets and virtual keys.

What is LiteLLM?

LiteLLM is an open-source Python SDK and proxy that exposes 100+ LLM providers through one OpenAI-compatible API.

What is AISIX?

AISIX is a Rust-native, Apache-2.0 AI gateway shipped as a single static binary, from the original creators of Apache APISIX.

AISIX vs LiteLLM: detailed feature comparison

The two gateways converge on the routing and reliability basics, then diverge: AISIX adds semantic routing and ensemble and keeps the whole data plane open; LiteLLM adds the widest provider list and OSS budgets.

Unified API and developer experience

Both unify providers behind an OpenAI-compatible surface. AISIX additionally treats the Anthropic Messages API as a first-class route, translating both ways, so an OpenAI or Claude SDK can hit one base_url and switch providers with no client changes.

# Point any OpenAI SDK at the gateway. Swap providers by changing the
# model alias on the gateway — the client code never changes.
curl http://localhost:3000/v1/chat/completions \
  -H "Authorization: Bearer $AISIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"my-model","messages":[{"role":"user","content":"hello"}]}'

LiteLLM offers the same OpenAI-format unification plus a Python SDK you can import directly — a strong fit when you want a library, not just a proxy. AISIX's edge is two-way OpenAI/Anthropic fidelity at the gateway. See the AISIX AI Gateway overview.

Routing intelligence: semantic routing and ensemble

Both offer weighted load balancing, failover, retries, cooldowns, and cost-, latency- and load-aware strategies. The difference is intelligence: AISIX can route by the meaning of a request and combine multiple models into one answer; LiteLLM, per its docs, does neither.

LiteLLM offers a flexible set of load-balancing strategies — latency-based, least-busy, rate-limit-aware, and cost-based. AISIX ships the same family — cost-aware (least_cost), latency-aware (least_latency), and load-aware (least_busy) selection, plus sticky weighted canary/A-B releases, tag-based conditional routing, and wildcard model aliases — and adds semantic routing (embed the prompt, score it against per-route examples, dispatch by meaning) and ensemble models (fan a request out to a panel of models and synthesize one answer). Per LiteLLM's official routing documentation, it has neither — its cost-based routing is price-driven, not meaning-driven. AISIX also publishes its performance baseline and sizing methodology.

Governance: what is free vs paid

LiteLLM keeps virtual keys and budgets in its open-source proxy and reserves SSO, SCIM, RBAC, and audit for Enterprise. AISIX keeps every gateway traffic capability Apache-2.0; AISIX Cloud adds organization roles, budgets, audit, SCIM directory sync, and a dashboard.

If you need OSS budgets and virtual keys without a managed service, LiteLLM is ahead there today. If you want the entire data plane — routing, semantic routing, ensemble, guardrails, caching, rate limiting, observability — under Apache-2.0, plus a managed control plane for budgets, roles, and audit, that is the AISIX shape. SCIM directory sync ships with AISIX Cloud.

Which should you choose?

Choose AISIX for a low-overhead, fully open gateway with routing intelligence; choose LiteLLM for the widest provider list, a Python SDK, and OSS budgets.

Migrating from LiteLLM to AISIX

Migration is mostly a config mapping — your client code keeps calling an OpenAI-shaped endpoint. A LiteLLM model_list entry becomes an AISIX model alias declared in resources.yaml.

model_list:
  - model_name: my-model
    litellm_params:
      model: openai/gpt-4o
      api_key: os.environ/OPENAI_API_KEY

models:
  - display_name: my-model
    provider: openai
    model_name: gpt-4o
    provider_key: openai-main

After the alias exists, point your SDK's base_url at the AISIX proxy and keep using the same model name. See the quickstart for provider-key setup.

The bottom line

There is no single winner — there is a winner for your constraints.

Frequently asked questions