LLM Gateway vs LiteLLM: An Honest Comparison

LLM Gateway and LiteLLM solve the same core problem: give developers a single API to access many LLM providers. But they approach it from opposite ends. LiteLLM is a Python library and self-hosted proxy you run yourself. LLM Gateway is a managed (or self-hosted) platform with the proxy, dashboard, billing, caching, routing, and audit logs already wired together.

We built LLM Gateway, so we're biased — but we'll tell you where LiteLLM is the right call too.

The Quick Version

Where LLM Gateway Wins

No Infrastructure to Babysit

LiteLLM's proxy is a Python service. You deploy it, you scale it, you monitor it, you update it. Add Redis for caching. Add a database for spend tracking. Add a second instance for high availability. Add a load balancer. Add CI to test config changes. It's not hard — but it's a project, and it never shrinks.

LLM Gateway's managed tier takes that entire stack off your plate:

1import OpenAI from "openai";
2
3const client = new OpenAI({
4  baseURL: "https://api.llmgateway.io/v1",
5  apiKey: process.env.LLM_GATEWAY_API_KEY,
6});

1import OpenAI from "openai";
2
3const client = new OpenAI({
4  baseURL: "https://api.llmgateway.io/v1",
5  apiKey: process.env.LLM_GATEWAY_API_KEY,
6});

That's the whole "deployment." Uptime, scaling, patches, and new-model support are someone else's job.

Prefer to self-host? One Docker command gets you the same features on your own infrastructure:

1docker run -d \
2  --name llmgateway \
3  -p 3002:3002 -p 4001:4001 -p 4002:4002 \
4  -e AUTH_SECRET="your-secret" \
5  -e GATEWAY_API_KEY_HASH_SECRET="your-hash-secret" \
6  ghcr.io/theopenco/llmgateway-unified:latest

1docker run -d \
2  --name llmgateway \
3  -p 3002:3002 -p 4001:4001 -p 4002:4002 \
4  -e AUTH_SECRET="your-secret" \
5  -e GATEWAY_API_KEY_HASH_SECRET="your-hash-secret" \
6  ghcr.io/theopenco/llmgateway-unified:latest

A Dashboard That Comes With the Proxy

LiteLLM tracks spend and usage, but the observability story is "bring your own." Hook up LangSmith, Langfuse, Datadog, or write your own callback. It works — after you build and maintain the integration.

LLM Gateway ships with:

• Per-request logs — prompt, response, model, provider, latency, tokens, cost, cache status
• Real-time cost breakdown — by model, project, API key, and user
• Cache hit rate — see exactly how much you're saving
• Provider health — uptime, throughput, and error rate per provider over time
• Security events — guardrail violations with category and action breakdowns

Nothing to wire up. Open the dashboard and it's there.

Smart Routing That Adapts

LiteLLM's fallback is a static list: "if this fails, try that." You write it in config. You update it when providers change.

LLM Gateway's router scores every available provider for a model using the last 5 minutes of real metrics:

• Uptime (50%) — exponential penalty below 95%
• Throughput (20%) — tokens per second
• Price (20%) — weighted toward cheaper providers
• Latency (10%) — time to first token on streaming

Epsilon-greedy exploration (1% of requests) probes underused providers so the scoring stays honest as conditions change. Every decision is logged with the scores and the winner, so nothing is a black box.

Caching That's One Toggle Away

Caching in LiteLLM is configurable but manual — pick a backend, configure keys, set TTLs, manage the Redis yourself.

In LLM Gateway, caching is a project-level switch. TTL from 10 seconds to 1 year. Works with streaming and non-streaming. Cached responses cost nothing. For workloads with repeat prompts (FAQ bots, classification, CI tests, batch jobs), teams routinely see 30–90% hit rates.

Enterprise Features Out of the Box

• Guardrails — prompt injection, jailbreak, PII, and secrets detection with block/redact/warn rules
• Audit logs — every org action tracked with who, what, when, which resource (90-day retention)
• Team management — roles and permissions across organizations and projects
• Data retention controls — full payload retention, metadata-only, or zero retention per project

LiteLLM covers some of this through plugins or external services. LLM Gateway bundles it.

Where LiteLLM Wins

Pure Self-Hosting Flexibility

If your requirement is "run it entirely on my hardware with zero external dependencies and total control over the Python stack," LiteLLM is purpose-built for that. It's a library first, a proxy second. You can embed it in your own service, patch it, fork it, and bend it to any shape.

LLM Gateway self-hosts too (AGPLv3, one Docker image), but it's an opinionated platform — dashboard, database, worker, gateway. If you only want a thin routing layer inside your own app, LiteLLM is leaner.

Python-First Codebases

LiteLLM's native interface is the litellm Python library. If your stack is Python and you want provider routing inside your process — no network hop, no proxy — calling litellm.completion() is the shortest path.

LLM Gateway is accessed over HTTP (OpenAI-compatible). That's fine for every language, but it is a network call.

Community and Longevity

LiteLLM has been around longer and has a deep community. More StackOverflow answers, more blog posts, more "I ran into this exact error" threads. For an existing LiteLLM team, that familiarity has real value.

Migration Is Two Lines

If you're running a LiteLLM proxy and want to try LLM Gateway:

1- const baseURL = "http://localhost:4000/v1";  // LiteLLM proxy
2- const apiKey = process.env.LITELLM_API_KEY;
3+ const baseURL = "https://api.llmgateway.io/v1";
4+ const apiKey = process.env.LLM_GATEWAY_API_KEY;

1- const baseURL = "http://localhost:4000/v1";  // LiteLLM proxy
2- const apiKey = process.env.LITELLM_API_KEY;
3+ const baseURL = "https://api.llmgateway.io/v1";
4+ const apiKey = process.env.LLM_GATEWAY_API_KEY;

Most model names are compatible. LLM Gateway accepts both bare model IDs (routed automatically) and provider-prefixed ones:

Using the LiteLLM library directly? Point it at LLM Gateway:

1import litellm
2
3response = litellm.completion(
4    model="gpt-4",
5    messages=[{"role": "user", "content": "Hello!"}],
6    api_base="https://api.llmgateway.io/v1",
7    api_key=os.environ["LLM_GATEWAY_API_KEY"],
8)

1import litellm
2
3response = litellm.completion(
4    model="gpt-4",
5    messages=[{"role": "user", "content": "Hello!"}],
6    api_base="https://api.llmgateway.io/v1",
7    api_key=os.environ["LLM_GATEWAY_API_KEY"],
8)

Full guide: docs.llmgateway.io/migrations/litellm.

Who Should Use What

Choose LLM Gateway if:

• You want a managed gateway without standing up Python infrastructure
• You need a dashboard, audit logs, and team management without assembling them
• Caching, guardrails, and smart routing should be toggles, not projects
• You work across Python, Node, Go, Rust — any language — over a single HTTP API
• You're running AI in production and want observability that's already wired up

Choose LiteLLM if:

• Your stack is Python-only and you want routing inside your process
• You need maximum self-hosting flexibility with zero external dependencies
• You want a thin library rather than a platform
• Your team already runs LiteLLM and the operational load is acceptable

The Bottom Line

LiteLLM gives you a proxy and a library. LLM Gateway gives you the proxy, plus the dashboard, caching, routing, guardrails, audit logs, team management, and cloud operations that production AI eventually needs.

If you enjoy owning infrastructure, LiteLLM is excellent at being what it is. If you'd rather ship features than babysit a proxy, LLM Gateway takes that operational work and makes it a checkbox.

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