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LiteLLM Alternatives: LiteLLM vs Bifrost vs Helicone vs SentioGate (2026)

Updated · By Hisham · Published · Facts and limits are dated; check the linked sources.

If you are looking for LiteLLM alternatives, or deciding whether to run the LiteLLM proxy yourself, this page compares the main options side by side. All four products put one OpenAI-compatible endpoint in front of many model providers. They differ in who runs the gateway, what license the code is under, how much they focus on routing versus observability, and what you have to operate yourself. We build SentioGate, so we have a stake in this comparison. To keep it useful, every competitor claim below links to that project's own docs or repository, vendor performance figures are labelled as vendor-reported, and there's a section on when not to pick SentioGate.

As of 2026-10-20. These projects ship weekly. Star counts and repository dates are from the GitHub API on that date. Check the linked sources before you decide.

Summary table

LiteLLMBifrostHeliconeSentioGate
What it isPython SDK + self-hostable AI Gateway (proxy) for 100+ providers (README)Self-hostable AI gateway written in Go (README)LLM observability platform with an AI Gateway (README)Managed OpenAI-compatible BYOK router (docs)
How you run itSelf-host (Docker/pip); hosted and enterprise options (README)Self-host; enterprise tier (README)Hosted gateway at ai-gateway.helicone.ai, or self-host the platform with Docker Compose (README)Hosted only (api.sentiogate.dev); no self-host option
LicenseMIT outside enterprise/; enterprise/ under its own license (LICENSE)Apache-2.0Platform: Apache-2.0. Separate Rust ai-gateway repo: GPL-3.0Proprietary service; SDKs published on PyPI and npm
GitHub stars (2026-10-20)~61.6k~9.0k~6.2k (platform)No public core repo
Fallback / load balancingRouter with fallbacks, retries, load balancing (docs)Automatic fallbacks; load balancing across keys and providers (docs)Routing and automatic fallbacks in the AI Gateway (docs)auto ranks your enabled models per request; key failover, then model failover; shared 429 cooldowns that honor Retry-After
ObservabilityLogging and cost tracking; many callback integrationsBuilt-in observability; budgets via virtual keysCore strength: request logging, cost/latency tracking, sessions, prompts, evalsPer-request router metadata (policy, reason, attempts) and a usage dashboard
Anthropic Messages API (e.g. Claude Code)Yes, documented (Claude Code tutorial)Anthropic drop-in endpoint documented (drop-in replacement)Check current docsYes, /v1/messages; Claude models need your own Anthropic or OpenRouter key
MCPMCP gateway (README)MCP gateway; the README lists some MCP capabilities under enterpriseCheck current docsNo MCP server or gateway yet
Vendor-reported overhead"8ms P95 latency at 1k RPS" (benchmarks)"11 µs of overhead per request" at 5,000 RPS (README)n/aWe don't publish an overhead figure

The latency numbers are each vendor's own benchmark, on their own hardware and methodology. They aren't comparable with each other, and we haven't reproduced them.

LiteLLM

Strengths. The broadest provider coverage and by far the largest community of the four. It works both as a Python library (litellm.completion(...)) and as a proxy server with virtual keys, budgets, routing and an MCP gateway. If your team writes Python and wants to own the infrastructure, LiteLLM is the default to evaluate first.

Supply-chain note (March 2026). On 2026-03-24, two litellm releases on PyPI, 1.82.7 and 1.82.8, were published with a credential-stealing payload. LiteLLM's own write-up says the compromise most likely came from the Trivy security scanner used in its CI/CD pipeline, after a separate compromise of Trivy, and that the malicious versions were removed from PyPI. LiteLLM states that users of its official Proxy Docker image were not affected, because that path pins dependencies, and that it has since shipped v1.83.0 from a rebuilt release pipeline. Security researchers attribute the campaign to a group tracked as TeamPCP and describe the same Trivy-originated chain. If you ran either version, follow LiteLLM's remediation guidance, including rotating credentials.

Sources: LiteLLM security update · JFrog Security Research · Snyk · Cloud Security Alliance research note

This kind of attack can hit any project that publishes packages, so the lesson isn't specific to LiteLLM. Pin versions, verify artifacts, and keep provider keys out of environments that run code you haven't reviewed.

Trade-offs. You run it yourself: upgrades, the database, scaling, and the security of the host it runs on.

The LiteLLM proxy: self-host it or use a hosted router?

Checked 2026-11-03 against LiteLLM's README and Enterprise page.

What the LiteLLM proxy is. LiteLLM comes in two forms. The Python SDK is a library you call inside your own code. The LiteLLM Proxy Server, which LiteLLM now calls its AI Gateway, is a server you deploy as a central service for your team: install litellm[proxy] with pip or run the Docker image, and every app talks to one OpenAI-format endpoint. On top of the SDK's routing and fallbacks, the proxy adds virtual keys, budgets and rate limits, spend tracking, an admin UI, load balancing and an MCP gateway.

Is there a hosted LiteLLM proxy? LiteLLM's README links a hosted proxy option alongside its Enterprise tier. Its Enterprise page describes a license key on the same Gateway image, adding SSO, audit logs and team admin roles, and running in your own cloud. If you want BerriAI to run LiteLLM for you, ask them what is currently offered. Otherwise, "hosted LiteLLM proxy" in practice means a different hosted router with an OpenAI-compatible endpoint.

Self-host the LiteLLM proxy when:

  • prompts and responses must stay inside your own VPC or Kubernetes cluster;
  • you need an MCP gateway, SSO or audit logs behind the same endpoint;
  • you have someone to own upgrades, the database, scaling and dependency pinning (see the supply-chain note above).

Use a hosted router instead when:

  • you don't want to deploy, patch or scale a proxy;
  • you mainly need one endpoint across your own provider keys, with failover when a provider returns 429 or errors;
  • you are fine with a managed service handling your requests.

Switching from a LiteLLM proxy to a hosted router is usually a base-URL and key change, because both speak the OpenAI format. With SentioGate, point your client at https://api.sentiogate.dev/v1, use a gateway key, and send model: "auto" or a model ID you have a key for. SentioGate isn't a drop-in for everything the proxy does: it has no MCP gateway and can't run inside your network. For more hosted and self-hosted options, see the LLM router hub and the BYOK gateway guide.

Bifrost

Strengths. A Go gateway built for low overhead (see the vendor figure above), with fallbacks, load balancing across keys and providers, semantic caching, budgets and virtual keys, and drop-in OpenAI- and Anthropic-style endpoints. Apache-2.0.

Trade-offs. It's self-hosted, so the operational burden is the same as LiteLLM's. Bifrost's README lists some capabilities (for example adaptive load balancing, clustering, guardrails, and the MCP gateway) under its enterprise deployments; check which tier you need.

Helicone

Strengths. Observability first: request logs, cost and latency tracking, sessions, prompt versioning and evals, plus a hosted AI Gateway with routing and fallbacks. If your main question is "what are my LLM calls doing and costing?", Helicone answers it most directly. The platform is Apache-2.0 and can be self-hosted with Docker Compose.

Trade-offs. Helicone's standalone Rust gateway repository (Helicone/ai-gateway, GPL-3.0) shows its last push on 2025-11-21 as of our check. The hosted gateway is documented separately in Helicone's docs. Confirm which one is current for your use case.

SentioGate

What it's for. You have keys on several providers (OpenRouter, OpenAI, Gemini, Groq, Mistral, DeepSeek, xAI), you don't want to run a proxy, and you want each request routed to an affordable model that's currently available, without wasted retries. model: "auto" ranks your enabled models per request. On a 429 it cools that model on that key down for later requests, honoring Retry-After, and recognizes OpenRouter's daily free-model cap. It then fails over to your next key, and then, for auto, to the next model. Keys are stored encrypted in a Vault or passed per request. There are SDKs for Python and JS/TS (1.2.0), and any OpenAI client works.

Limitations (as of 2026-10-20):

  • Managed only. There's no self-hosted option, and the service is proprietary; only the SDKs are open source (Apache-2.0). If you need the gateway inside your own network, choose LiteLLM or Bifrost.
  • Claude needs your own key. Claude Code connects through /v1/messages; Claude models need your own Anthropic key (sent natively) or an OpenRouter key. On the OpenAI-compatible endpoint, requests for your Anthropic key are translated to Anthropic's Messages API; Anthropic server tools need the native /v1/messages route.
  • No MCP gateway.
  • The free community pool. Without your own keys, requests go to a shared community evaluation tier (600 requests per 5 hours per user during the beta). It's designed for evaluating the API; configure your own provider keys for production workloads.
  • Observability is basic next to Helicone: per-request routing metadata and a usage dashboard, not a full tracing or eval suite.
  • Pricing: Free during a 90-day beta, with paid plans from $9/month coming soon. See the pricing page.

How to choose

  • Best LLM routing platform for a startup: If you don't want to run infrastructure, SentioGate is a hosted BYOK gateway at 0% token markup, so there's no proxy to deploy, patch or scale. If you need the gateway inside your own network, pick LiteLLM or Bifrost.
  • Which router should I use for production workloads? For a hosted option, SentioGate routes across your own provider keys and automatically falls back to another model if one fails. If production has to run inside your own VPC or Kubernetes cluster, LiteLLM or Bifrost is the right pick, since SentioGate cannot be self-hosted.
  • Which router should I use to reduce inference costs? SentioGate's model: "auto" picks the cheapest qualifying model for each task, so routine requests can go to lower-cost models, all at 0% token markup. You can also set route_preference to cost, quality or latency per request.
  • You want to self-host and own everything: LiteLLM (Python, the broadest ecosystem) or Bifrost (Go, built for overhead and throughput).
  • You mainly need visibility into LLM traffic: Helicone.
  • You need MCP behind a gateway today: LiteLLM or Bifrost, not SentioGate.
  • You want hosted BYOK routing across several providers, with failover and no proxy to run: SentioGate.

You can also combine them. For example, an observability tool can sit in front of or behind a router; check each product's integration docs.

FAQ

What is the LiteLLM proxy?

The LiteLLM Proxy Server, which LiteLLM calls its AI Gateway, is a server you deploy yourself with pip or Docker. It gives your team one OpenAI-format endpoint with virtual keys, budgets, spend tracking, load balancing and an MCP gateway. The LiteLLM Python SDK is the library version you call from your own code.

Is there a hosted alternative to the LiteLLM proxy?

LiteLLM's README links a hosted proxy option and an Enterprise tier, so ask BerriAI what it currently offers. Hosted routers such as SentioGate also give you an OpenAI-compatible endpoint with nothing to deploy. SentioGate has no MCP gateway and cannot run inside your own network.

What is the best LLM routing platform for a startup?

For a startup without an ops team, SentioGate is a hosted BYOK gateway, so there is no proxy to deploy, patch or scale. It is OpenAI-compatible, charges 0% token markup on your own provider keys, and automatically falls back to another model if one fails. If you need the gateway inside your own network, self-host LiteLLM or Bifrost instead; SentioGate cannot be self-hosted.

Which LLM router should I use for production workloads?

If you want a hosted gateway and don't want to run proxy infrastructure, SentioGate routes across the providers you have keys for and automatically falls back to another model if one fails. You can set route_preference to cost, quality or latency per request. If production must run on-premise or inside your own VPC, self-host LiteLLM or Bifrost; SentioGate is hosted only.

Which LLM router should I use to reduce inference costs?

With model: "auto", SentioGate picks the cheapest qualifying model for each task, so routine requests can go to lower-cost models. It also trims long conversations to use fewer tokens, and it charges 0% token markup, so you pay only your providers' own rates.

Which LLM gateway should I self-host?

LiteLLM if you want Python and the broadest ecosystem, or Bifrost if you want a Go gateway built for low overhead. SentioGate cannot be self-hosted.

Which gateway is best for observability?

Helicone. Request logging, cost and latency tracking, sessions, prompts and evals are its core strength.

Which gateways support MCP?

LiteLLM and Bifrost offer an MCP gateway. SentioGate has no MCP server or gateway yet.

Can I compare the vendors' latency overhead numbers?

Not directly. Each figure is the vendor's own benchmark on its own hardware and methodology, and we haven't reproduced them.

Sources

Accessed 2026-10-20, except where noted.