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GoModel vs LiteLLM

Both put one OpenAI-compatible API in front of many AI providers. GoModel is a small Go binary built to stay out of the way. LiteLLM is a Python proxy with a bigger provider catalog.

Get started with GoModel See the benchmark

25×

faster responses

2.2 ms vs 55.8 ms

20×

more throughput

4,498 req/s vs 220 req/s

20×

less memory

74 MB vs 1,488 MB

33×

faster cold start

0.8 s vs 26.8 s

The benchmark

A gateway sits on every request, so its overhead adds up. Same machine, same mock backend, one gateway at a time.

Median latency

p50, chat completions · lower is better

25× faster

GoModel2.2 ms

LiteLLM55.8 ms

Tail latency

p99, chat completions · lower is better

9.7× lower

GoModel6.9 ms

LiteLLM66.6 ms

Time to first token

p50, streaming chat · lower is better

21× faster

GoModel5.0 ms

LiteLLM105.3 ms

Throughput

peak sustained · higher is better

20× more

GoModel4,498 req/s

LiteLLM220 req/s

Memory

peak RAM · lower is better

20× less

GoModel74 MB

LiteLLM1,488 MB

Cold start

container start to first request · lower is better

33× faster

GoModel0.8 s

LiteLLM26.8 s

Docker image

compressed pull size · lower is better

18× smaller

GoModel21 MB

LiteLLM365 MB

GoModel 0.1.98 vs LiteLLM 1.103.0 on an AWS c7i.large (2 vCPU, 4 GiB), run 20260928-043817 (28 September 2026). 20,000 requests per workload at concurrency 10, five randomized trials. API workloads served: GoModel 6/6, LiteLLM 6/6. LiteLLM ran its recommended two workers; retries, GoModel's circuit breaker, and per-request logging were off. LiteLLM hit the 60-second cap per workload before 20,000 requests, so its figures cover the requests it finished. Rerun it with one command.

Beyond speed

Both are open core with a paid tier. LiteLLM covers more providers; GoModel keeps its admin audit trail free and has no SSO seat cap.

	GoModel	LiteLLM
Runtime	One Go binary	Python, one worker per vCPU
Providers	34	100+
Config	Env vars alone, YAML optional	YAML file, plus DB and admin UI
Budgets, rate limits, per-user API keys	Free	Free
Audit trail of admin changes	Free, opt-in	Enterprise license
SSO	OIDC for the dashboard in Pro, no seat cap	OIDC and SAML, free up to 5 users, then Enterprise
License	MIT core + commercial Pro	MIT core + commercial Enterprise

Which one fits?

An honest split.

Pick GoModel if

Latency or request volume matters.
You want an audit trail of admin changes without an enterprise license.
You want one small binary on a laptop, edge box, or cluster.
You want to configure it with env vars alone, no config file.

Pick LiteLLM if

You need a provider outside GoModel's 34.
Your stack already builds on the LiteLLM Python SDK.
You need SAML SSO today. (GoModel Pro has OIDC SSO for the dashboard.)

Try it against your own workload

Benchmarks are an argument; your traffic is the proof. GoModel speaks the OpenAI and Anthropic APIs natively, so pointing an existing app at it is a base-URL change.

1 · Run GoModel

docker run --rm -p 8080:8080 \
  -e LOG_FORMAT=text \
  -v gomodel_data:/app/data \
  enterpilot/gomodel

2 · Point your SDK at it

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="your-gomodel-key",
)

Full setup, providers, and configuration live in the documentation. Questions? Ask on Discord or book a 30-minute call.