LiteLLM vs Bifrost vs Helicone vs SentioGate: Choosing an LLM Gateway (2026)
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
| LiteLLM | Bifrost | Helicone | SentioGate | |
|---|---|---|---|---|
| What it is | Python 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 it | Self-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 |
| License | MIT outside enterprise/; enterprise/ under its own license (LICENSE) | Apache-2.0 | Platform: Apache-2.0. Separate Rust ai-gateway repo: GPL-3.0 | Proprietary 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 balancing | Router 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 |
| Observability | Logging and cost tracking; many callback integrations | Built-in observability; budgets via virtual keys | Core strength: request logging, cost/latency tracking, sessions, prompts, evals | Per-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 docs | No. No /v1/messages endpoint yet |
| MCP | MCP gateway (README) | MCP gateway; the README lists some MCP capabilities under enterprise | Check current docs | No 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/a | We 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.
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 (capped at 2 minutes), 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.
- No Anthropic Messages endpoint, so it doesn't work with Claude Code. Claude models are reachable through an OpenRouter key; direct Anthropic keys aren't forwarded yet.
- No MCP gateway.
- The free community pool is one model. Without your own keys, requests go to a single shared model with shared rate limits (150 requests per 5 hours per user during the beta) and no failover. It's for trying the API, not for production.
- 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 (25,000 requests/month soft cap), with paid plans from $9. See the pricing page.
How to choose
- 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 Claude Code or 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.
Sources
All accessed 2026-10-20.
- LiteLLM: GitHub, LICENSE, Routing, Benchmarks, Security update, March 2026
- Bifrost: GitHub, Retries & fallbacks, Drop-in replacement
- Helicone: GitHub, AI Gateway docs, ai-gateway repo
- Incident reports: JFrog, Snyk, Cloud Security Alliance
- SentioGate: docs, pricing