Landscape · Open-source LLM proxy and SDK
LiteLLM alternatives: what to run instead, and when to run nothing
People search for LiteLLM alternatives for one of two reasons: they no longer want to operate a proxy themselves, or they have discovered their problem was never provider abstraction. Both are covered below, along with the five options that actually compete for the job.
What LiteLLM is genuinely good at
LiteLLM is the pragmatic default for provider abstraction and it is free. The provider coverage is broad, the OpenAI-compatible interface means minimal application change, and self-hosting keeps traffic and keys inside your infrastructure. For a team that wants a proxy without a vendor relationship it is genuinely hard to argue against, and most people who search this term end up staying.
Why people look for something else
- You do not want to operate it
A self-hosted proxy in the critical path of every model call is infrastructure with an on-call rotation attached. Teams frequently start with LiteLLM and then decide that hop should be somebody else's uptime problem.
- You want a support relationship
Open source with a community is not the same as a vendor you can escalate to during an incident, and some procurement processes require the second thing regardless of the first thing's quality.
- Your spend controls need to reach further than keys
Virtual keys and budgets cap spend by key and model. Once the question becomes 'which change made us more expensive', a per-key cap has no data to answer it — and that is a different tool, not a better proxy.
- Your AI is vendor agent tools, not application code
A proxy governs calls your code makes. Claude Code, Copilot, Cursor and Codex do not route through it, so if that is where your AI usage actually lives, the proxy question is answering a problem you do not have.
The open-source llm proxy and sdk landscape
A commercial AI gateway covering routing, fallbacks, caching, observability, guardrails and budget controls as a managed platform.
pick this ifYou want the gateway capability set without running the gateway, and a vendor to escalate to.
Model traffic proxied through Cloudflare's edge, adding analytics, caching, rate limiting and request logging for a base-URL change.
pick this ifIntegration effort is your binding constraint and you are already a Cloudflare customer.
A unified API across many model providers, positioned around routing and access breadth through a single credential.
pick this ifYou wanted the provider-abstraction half of LiteLLM and are happy for it to be a hosted service rather than a component you deploy.
AI capabilities on an established API gateway, so model traffic is governed by the platform that already governs your other APIs.
pick this ifYou have a platform team, you already run Kong, and adding an AI-specific proxy would mean two control points instead of one.
Open-source LLM observability with a proxy integration path, centred on logging, monitoring and cost visibility.
pick this ifYou deployed LiteLLM mostly to get logs and cost data, and never used the routing.
Provider SDKs called directly, instrumented with OpenTelemetry's GenAI semantic conventions so spans land in the observability stack you already operate.
pick this ifOne or two providers, an existing observability platform, and no genuine need for failover — in which case the proxy is a hop and a dependency buying you very little.
Why Vincosha is not on that list
the honest part
Vincosha is not a LiteLLM alternative and we say so on the comparison page too. We do not abstract model providers, do not hold provider credentials, and nothing in your application code needs to change to adopt us. If provider fragmentation is your problem, one of the six rows above is your answer.
The adjacent problem, and whether it is yours
The overlap that makes people ask is cost. LiteLLM's virtual keys and budgets are frequently the first AI cost control a company puts in place, and cost control is one of Vincosha's three jobs — so it is worth being exact about where the two differ rather than claiming a rivalry that does not exist.
A virtual key attributes spend to a key and a model. That answers 'did we exceed a cap'. Vincosha Ledger attributes spend to a user, a project, a session and the artifact versions that were active in that session, which answers a different and harder question: whether shipping version three of a skill made agents cheaper or more expensive. A proxy cannot make that join because it never sees the artifact.
The security boundary is the same one that applies to any proxy, and it is structural rather than a gap in the implementation. A component in the model request path cannot see a hook executing shell commands on a laptop, an MCP server that changed its tool definitions after approval, or a rule file that arrived through a pull request nobody read. None of those produce a request.
Teams that run both split cleanly: LiteLLM in the request path, Vincosha in the artifact path, no shared mechanism and no conflict.
Deciding
- Is operating the proxy the thing you want to stop doing?
- Then you want a managed gateway — Portkey or Cloudflare AI Gateway — or a hosted routing layer like OpenRouter if abstraction was all you used.
- Did you only ever use the logging and cost pages?
- Then you bought a proxy to get observability. Helicone, or your existing platform via OpenTelemetry's GenAI conventions, is a smaller and more durable answer.
- Do you already run an API gateway?
- Extending it is usually cheaper than adding an AI-specific control point, and it keeps policy in one place. Kong's AI capabilities exist for exactly this reader.
- Are you trying to answer 'which configuration change caused this cost'?
- No proxy can. That attribution has to reach sessions and artifact versions, which is what Vincosha Ledger is for — and it is additive to whichever proxy you keep.
Frequently asked
- Is there a managed version of LiteLLM?
- LiteLLM offers hosted and enterprise options alongside the open-source project — check their own site for current details, since we do not publish other vendors' plan structures. If the goal is simply not operating a proxy, the managed gateways above cover the same job.
- What is the lightest possible alternative to running a proxy?
- Calling provider SDKs directly and emitting OpenTelemetry spans using the GenAI semantic conventions. You lose failover and centralised key management; you gain one less hop in the request path and no new service to operate. For small provider counts that trade is often correct.
- We use LiteLLM for budgets. What does artifact-level attribution add?
- Depth. Per-key limits answer whether you exceeded a cap. Per-session, per-artifact-version attribution answers which configuration change caused the increase and whether your fix worked — a question a proxy has no data to answer, because the artifact never passes through it.
- Do agent tools like Claude Code go through LiteLLM?
- Not by default. They talk to their vendor's API directly, so a proxy you deploy does not observe them. That is the usual moment a team realises the proxy covers their applications but not the majority of their actual AI usage.
Check every one of these yourself
This landscape was last checked against each vendor's own site on 2026-07-26. It asserts no feature, price, version or benchmark for any product on it, including ours — those change, and we cannot verify them from here.