LiteLLM vs Gateway-LLM: A Performance & Architecture Comparison

TL;DR

Both LiteLLM and Gateway-LLM are open-source LLM gateways. They solve the same headline problem (one API in front of every provider) but differ on architecture, performance, and where smart routing lives in the product.

| Dimension | LiteLLM | Gateway-LLM | |---|---|---| | Language | Python | Go | | Deployment shape | Library + Python proxy server | Single Go binary | | Routing decision latency | ~5–15 ms | ~11 µs | | Memory footprint | ~150–250 MB | ~25–35 MB | | Smart routing | Plugin / external | First-class core feature | | Provider coverage | 100+ via plugins | 8 built-in, custom via OpenAI-compatible | | OpenAI-compatible API | Yes | Yes | | License | MIT | Apache 2.0 | | Hosted plan | Yes (LiteLLM Cloud) | Yes (Custom plan) |

The right choice depends on how you want to deploy more than which features are checked. This post walks through the differences in detail and tells you when each one is the right pick.

Where they overlap

If you're new to the category, both products do the same thing in broad strokes:

• Sit between your application and one or more LLM providers.
• Expose an OpenAI-compatible API surface so your existing OpenAI SDK calls work without rewrite.
• Translate request and response shapes for non-OpenAI providers (Anthropic, Google, Bedrock, etc.).
• Issue virtual API keys with rate limiting and budgets.
• Track per-request cost and emit metrics.

If you stop reading here, you can pick either one and you'll be roughly fine — the average cost of a wrong gateway choice is a half-day migration when you grow into the limits of the wrong one. Both teams are friendly and competent; nothing below is a knock on LiteLLM, just an honest comparison.

Where they diverge

1. Language and deployment shape

LiteLLM is Python-first. It ships as both:

• A Python library (pip install litellm) you import and call from inside an existing Python service. This is its origin story — it started as a unifying abstraction for OpenAI/Anthropic/Cohere and grew the proxy on top.
• A proxy server (litellm --port 4000), which is a Python (FastAPI) process you run alongside your apps.

If you have an existing Python service and want to add provider-agnostic routing without standing up a new server, the library form is genuinely the easiest path. LiteLLM wins this comparison if that's your shape.

Gateway-LLM is a standalone server. There is no in-process library form; you run it as a Docker container or a systemd service, and your apps talk to it over HTTP. The binary is ~12 MB and starts in well under a second. There's no Python interpreter in the runtime path.

This affects two practical things:

• Cold starts in serverless / autoscale environments. The Go binary is in steady state in 200 ms; the Python proxy takes 2–5 seconds to be responsive.
• Resource isolation. The Go binary doesn't compete with your application Python interpreter for CPU and memory. On a t3.small worker that's the difference between "fine" and "swap thrash".

2. Performance and overhead

This is the most concrete difference and the easiest to measure.

| Metric | LiteLLM (Python proxy) | Gateway-LLM (Go) | |---|---|---| | Routing decision (no upstream call) | 5–15 ms p50 | ~11 µs | | Memory at idle | ~150–250 MB | ~25–35 MB | | Memory under 1k RPS | ~600 MB – 1.2 GB | ~80–150 MB | | CPU per request | ~3–5 ms (Python) | ~30 µs (Go) | | Throughput on 4-core node | ~600 RPS sustained | ~12k RPS sustained |

Numbers above come from a homegrown bench harness running the same prompt mix through each gateway in isolation, with mocked upstreams. They're not a knock on Python — they're the realities of GIL-bound interpretation versus a Go server with goroutines.

Does the latency difference matter? Honestly, often not. If your upstream OpenAI call takes 2,000 ms anyway, the gateway's 5 ms vs 11 µs is rounding error. Two situations where it does matter:

• Caching hits. A cache hit on LiteLLM's Python proxy is a 5–15 ms response. On Gateway-LLM it's ~50 µs. If you cache aggressively (semantic cache turned on, real-world hit rates 30–50%), this is a meaningfully better user experience.
• High-RPS workloads. At 5k RPS, LiteLLM's Python proxy needs ~8 cores; Gateway-LLM's Go binary handles it on 1.

3. Smart routing

Both gateways can route a request to one of N deployments. The difference is how core that feature is to the product.

LiteLLM ships several routing modes (simple_shuffle, least_busy, usage_based_routing) plus a plugin interface for custom logic. They work; they're not the marquee feature. There isn't a built-in prompt-complexity classifier — you can build one as a plugin, but you're on your own for the actual classification logic.

Gateway-LLM has smart routing as section 1 of its docs. The prompt classifier (deterministic feature scorer, sub-microsecond) is built in, the bucket-to-deployment mapping is config.yaml syntax, and the savings number is a first-class metric (gatewayllm_smart_route_decisions_total). The whole demo on the homepage is built around it.

If smart routing is the reason you want a gateway at all, Gateway-LLM is the more direct fit. If you want a router-shaped object you can drop a custom plugin into, LiteLLM gives you that.

4. Provider coverage

LiteLLM: 100+ providers via its plugin system. Every long-tail provider you've heard of and several you haven't.

Gateway-LLM: 8 built-in providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, Mistral, Together, Groq, Ollama). For anything else, the openai_compatible provider type forwards to any OpenAI-shaped endpoint — covers most of the long tail with one-line config.

If you specifically need Replicate, Fireworks, OctoAI, or some niche regional provider with a custom auth scheme, LiteLLM's plugin will save you implementation time. For the common big-five providers, both work fine and Gateway-LLM is faster.

5. License

LiteLLM: MIT. Some advanced features (admin UI, certain enterprise integrations) live behind LiteLLM Enterprise, a paid offering.

Gateway-LLM: Apache 2.0. The whole product is in the OSS repo — same binary the hosted plan runs. The hosted plan adds operational convenience (we run it, we patch it, we observe it) and procurement-friendly paperwork (SOC 2, MSAs); it doesn't unlock features.

For procurement-sensitive shops, the lack of an "open core / paid features" split sometimes matters. For lone developers, both licenses are fine.

6. Documentation and community

This is harder to score honestly because we built one of them.

LiteLLM has more years of accumulated documentation, more StackOverflow answers, more YouTube videos. If you're in a hurry and want the most discoverable answers, you'll find them faster.

Gateway-LLM has fewer years of search results but tighter, more architecturally-coherent docs. The product is young enough that the docs cover it end-to-end without legacy quirks.

If you're picking a gateway to live with for two years, "tighter docs" matters more than "more search results" — but in week 1, the second matters more.

Concrete decision tree

Use this if you want a single-shot answer:

• Existing Python service, want to add routing in-process → LiteLLM library.
• Standalone server, smart routing is the headline feature → Gateway-LLM.
• Need a long-tail provider not in either built-in list → LiteLLM (more plugins).
• High-RPS / caching-heavy / sub-millisecond latency budget → Gateway-LLM.
• Procurement requires Apache 2.0, no open-core → Gateway-LLM.
• You want the most-trodden path with the most StackOverflow → LiteLLM.

If two or three of those tip toward Gateway-LLM, you're in our wheelhouse. If two or three tip toward LiteLLM, that's the right call — the cost of running the wrong gateway is a half-day migration, and we'd rather have you on the right one for your shape.

Migrating from LiteLLM to Gateway-LLM

If you decide to move, the migration is roughly:

1. Stand up Gateway-LLM alongside LiteLLM. Same provider keys; both running.
2. Translate litellm_config.yaml → config.yaml. The structure is similar; we have a config translation reference. Most teams do this in an hour.
3. Issue virtual keys in Gateway-LLM that mirror your LiteLLM key configuration.
4. Switch a single low-risk service. Compare outputs and metrics for 24 hours.
5. Cut over the rest. Application code doesn't change; only the base_url (and the virtual key) does.
6. Decommission LiteLLM when traffic is fully on Gateway-LLM.

A typical Pro-tier LiteLLM deployment migrates in a half-day; an Enterprise-tier deployment with custom plugins takes longer because the plugins need re-implementing as Gateway-LLM policy / classifier extensions.

Try Gateway-LLM

If you want to compare empirically: spin up Gateway-LLM next to whatever you're running now, point a low-stakes service at both for a day, and look at the latency, memory, and savings numbers side by side.

Quickstart → /docs/quickstart Smart routing → /docs/smart-routing Talk to the founders → mitshawtechnologies@gmail.com

Related reading

• What is an LLM gateway? — the category primer.
• Smart routing for cost savings — the worked-example for the feature where these two diverge most.
• Multi-provider failover patterns — practical resilience tactics for either gateway.

FAQ

What is the main difference between LiteLLM and Gateway-LLM?
LiteLLM is Python (library + proxy). Gateway-LLM is a Go binary with smart routing as a first-class feature. The architectural difference shows up most in latency, memory, and deployment shape.

When should I pick LiteLLM over Gateway-LLM?
When you want a Python library you can import in-process, or when you need a niche provider only LiteLLM supports.

Is Gateway-LLM open source?
Yes — Apache 2.0, full product in the public repo. The hosted plan uses the same binary.

Can I migrate from LiteLLM to Gateway-LLM?
Yes. Both speak OpenAI-compatible APIs; application code doesn't change. Config and virtual keys translate cleanly.