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, semantic caching, 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

Both gate advanced governance, but differently. LiteLLM keeps budgets and virtual keys in open source and paywalls identity (SSO/SCIM/RBAC/audit). AISIX keeps every traffic control Apache-2.0 and offers org-level governance through the managed AISIX Cloud.

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. SSO and SCIM are on the AISIX roadmap rather than shipping today.

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 created through the Admin API.

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

curl -X POST http://localhost:3001/admin/v1/models \
  -H "Authorization: Bearer $AISIX_ADMIN_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "display_name": "my-model",
    "provider": "openai",
    "model_name": "gpt-4o",
    "provider_key_id": "<your-provider-key-id>"
  }'

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