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

AISIX vs LiteLLM at a glance

AISIX offers a Rust gateway with semantic routing and ensemble in its Apache-2.0 data plane. LiteLLM offers a Python SDK and proxy, broad provider integrations, open-source budgets, and beta Auto Routing. Choose against the required workload and operating model.

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

Compare the runtime, configured routing behavior, client contract, and management boundary separately. Both document semantic routing options; their implementation, release status, and configuration differ.

Unified API and developer experience

Both unify supported provider traffic behind familiar client APIs. AISIX supports OpenAI-compatible endpoints and Anthropic Messages, but endpoint, tool, streaming, and continuation behavior must be verified for each upstream.

# Point a compatible client at a configured gateway alias.
# Verify endpoint, tool, and streaming behavior before changing its provider.
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. For AISIX, verify the supported endpoint matrix and test your client workflow before switching an alias to a different provider. See the AISIX AI Gateway overview.

Routing intelligence: semantic routing and ensemble

Both document routing beyond basic load balancing. AISIX includes semantic routing and ensemble models. LiteLLM documents a beta Auto Router with heuristic, classifier, and semantic keyword routing; compare the selected release and configuration.

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). LiteLLM's beta Auto Router documentation also describes semantic keyword matching and classifier-based selection; its earlier semantic Auto Router is deprecated. Evaluate quality, classifier cost, latency, and failure behavior on the same requests. 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; its Enterprise documentation allows free SSO for up to 5 users and requires a license beyond that. AISIX keeps its gateway traffic capabilities Apache-2.0; AISIX Enterprise and AISIX Cloud add 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 Enterprise and AISIX Cloud and does not establish dashboard SSO. Check the current LiteLLM Enterprise terms for the free SSO user limit and licensed features.

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

Start by mapping a LiteLLM model_list entry to an AISIX model alias in resources.yaml. Then separately migrate caller access, budgets, telemetry, and policy, and test the client's endpoints, tools, streaming, continuation, and failure behavior. A configuration mapping alone does not establish compatibility.

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 after validating the required workflow. OSS rate limits do not replace spend budgets; compare your management and cost-control requirements before rollout. 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