Best LiteLLM Alternatives in 2026

LiteLLM earned its place. It is an open source Python library providing a simple unified API for interacting with multiple LLM providers, and its proxy server mode adds logging and token usage tracking with very little setup. For a team wiring up its first prototype, that is often all you need.

The reason people now search for LiteLLM alternatives is not that the library stopped working. It is that a recent supply-chain attack put trust and governance at the top of the buying checklist, and production teams are asking whether a lightweight proxy is the right foundation for real traffic. This guide compares the strongest options in 2026 on routing, observability, governance, security, and deployment, so you can match a tool to where you actually are.

Here is the short version before we go deep.

Tool	What it is	Standout strength	Best for
WSO2 AI Gateway	Open source AI gateway with unified API, AI, and MCP governance	Enterprise governance, guardrails, and hybrid or on-prem deployment with vendor support	Production teams that need control, audit, and data residency
LiteLLM	Open source Python library and proxy across providers	Fast, lightweight unified API for prototyping	Early experiments and small self-hosted setups
Portkey	AI gateway with routing, fallbacks, and observability	Managed control plane with request-level visibility	Teams wanting a hosted gateway with monitoring
Kong AI Gateway	AI gateway built on Kong's API gateway	Mature enterprise controls and rate limiting	Teams already standardized on Kong
Helicone	LLM observability and monitoring layer	Logging, usage tracking, and request-level analytics	Observability-first teams
OpenRouter	Unified access to many models through one API	Broad model access behind a single endpoint	Reaching many providers quickly
TrueFoundry	ML and LLM platform with a gateway	Platform approach with enterprise self-hosting	Teams wanting a full platform, not just a proxy

Why Look for a LiteLLM Alternative?​

The trigger for most searches right now is trust. A supply-chain attack against the LiteLLM ecosystem pushed teams to re-examine what sits in the critical path of every model call. When a component routes prompts, keys, and responses, its security posture is your security posture. That is why the buying conversation shifted from features to provenance, patching discipline, and who is accountable when something breaks. If you are weighing that risk the OWASP Top 10 for LLM Applications is a useful baseline for what a gateway should defend against.

Trust is the headline, but it is not the only reason teams move off LiteLLM. The practical gaps show up as traffic grows:

• Latency at scale. LiteLLM adds significant latency when proxying to external providers, which can become a bottleneck for real-time agentic apps that make many chained calls.
• Enterprise deployment friction. It is difficult to deploy in enterprise settings that need built-in service configuration for Kubernetes, VPC, or on-prem environments.
• Support and SLAs. As an open source project, it lacks the enterprise support and SLAs that regulated teams need in writing.
• Observability ceiling. The proxy is useful, but it lacks advanced observability once you need per-team attribution, deep audit logs, and cost governance.

None of these make LiteLLM a bad tool. They make it a tool that was designed for a different job than running governed production traffic.

What LiteLLM Does Well (and Where It Falls Short)​

Credit where it is due. LiteLLM abstracts away provider differences so developers can switch models without rewriting code, and it does this behind an OpenAI compatible API that most codebases already speak. Point your existing OpenAI client at it, and you can reach multiple LLM providers with a one-line change. It is free and open source, easy to run locally, and genuinely good for prototyping. If your goal is to validate an idea this week, reaching for LiteLLM is a reasonable call.

The gaps appear when the prototype becomes a product. Because the proxy sits inline on every request, its latency overhead compounds under load. Its usage tracking is fine for a dashboard glance but short for production finance, where you need audit logs tied to teams, projects, and cost centers. Self-hosted model deployment across a real network is manual work, and there is no vendor on the hook for uptime. The honest summary: LiteLLM is a strong developer library and a light gateway, and it starts to strain exactly where governance, security, and scale become non-negotiable.

The Best LiteLLM Alternatives in 2026​

The alternatives below span open source gateways, observability layers, and managed control planes. They solve different problems, so read the "best for" line as much as the strengths. Every tool here is described neutrally, and pricing is intentionally omitted because it changes often and should be confirmed with each vendor.

WSO2 AI Gateway​

WSO2 AI Gateway is the open source, enterprise-grade option on this list, and it is the most direct answer to the post-attack trust question. It is part of the WSO2 API Platform, a 100% open source platform for shipping, governing, and monetizing APIs, AI, and MCP traffic. That heritage matters here: you get transparency you can inspect and enterprise support you can call, rather than one or the other.

On the outbound side, the LLM Gateway handles multi-model routing across OpenAI, Azure AI, AWS Bedrock, Anthropic, and Mistral AI, with round-robin and failover so a single provider outage does not take you down. It applies AI guardrails including semantic prompt validation, PII masking, and URL and JSON Schema checks, and it can integrate Azure Content Safety and AWS Bedrock Guardrails or your own. Token-based rate limiting plus department-level chargeback gives finance the cost governance that a proxy cannot. On the inbound side, the MCP Gateway secures agent traffic, auto-generating MCP servers from existing REST APIs and enforcing authentication, authorization, and throttling on agent calls. WSO2 was named a Leader in the Forrester Wave for API Management Software, Q3 2024.

Best for: production teams that need governance, deep audit logs, guardrails, and the choice to run self-hosted, hybrid, or SaaS for data residency and air-gapped environments.

Portkey​

Portkey is a managed AI gateway focused on routing, fallbacks, and observability. It sits as a control plane in front of your providers and gives request-level visibility into calls, which appeals to teams that want a hosted layer without operating their own infrastructure. If your priority is getting a monitored gateway running quickly and you are comfortable with a SaaS control plane, it is a reasonable evaluation.

Best for: teams that want a managed control plane with observability and provider selection out of the box.

Kong AI Gateway​

Kong AI Gateway extends Kong's established API gateway into LLM traffic. Teams already standardized on Kong inherit its mature enterprise controls and rate limiting, and the AI capabilities layer routing and provider selection on top. The draw is consolidation: one gateway story for both conventional APIs and model calls.

Best for: organizations already invested in Kong that want AI traffic under the same enterprise controls.

Helicone​

Helicone is an observability and monitoring layer for LLM calls rather than a full gateway. It captures logging, usage tracking, and request-level analytics, which makes it strong where the primary need is visibility into what your models are doing and what they cost. Many teams run it alongside a gateway rather than instead of one.

Best for: observability-first teams that want detailed logging and usage tracking.

OpenRouter​

OpenRouter provides unified model access, exposing many providers and models behind a single OpenAI compatible endpoint. Its strength is breadth: reaching a wide catalog of models without integrating each one directly. It leans toward model access and aggregation rather than deep enterprise governance, so pair it with your own controls if you need them.

Best for: teams that want fast, broad access to many models through one API.

TrueFoundry​

TrueFoundry is a broader ML and LLM platform that includes gateway capabilities alongside deployment and hosting features. It suits teams that want a platform approach with enterprise self-hosting rather than a single-purpose proxy, and it targets organizations consolidating model operations in one place.

Best for: teams wanting a full platform with self-hosted control, not just routing.

How to Choose a LiteLLM Alternative​

Start from your stage, not from a feature checklist. A prototype and a governed production system have opposite priorities, and the right answer changes with them. Work through these dimensions in order:

1. Security and provenance. After the supply-chain attack, ask how each project handles patching, signing, and disclosure. An open source codebase you can audit plus a vendor accountable for fixes is the strongest combination.
2. Governance and RBAC. Do you need role-based access, per-team quotas, and audit logs that satisfy compliance? Proxies rarely provide this; governed gateways do.
3. Routing and load balancing. Confirm multi-provider model routing with automatic retries and failover so one provider's outage does not become your outage.
4. Observability and cost. Look for request-level tracing plus cost governance and chargeback, not just aggregate usage tracking.
5. Deployment model. Decide between self-hosted, hybrid, and SaaS based on data residency. Regulated and air-gapped teams need on-prem or hybrid, which rules out SaaS-only tools.

WSO2 AI Gateway as a LiteLLM Alternative​

If your reason for leaving LiteLLM is trust plus the need to run governed traffic at scale, WSO2 AI Gateway is built for exactly that transition. The open source foundation answers the transparency concern directly: you can read the code, run it entirely inside your own network, and still have a vendor to call when you need an SLA. That combination is hard to find, and it is the core reason to choose it over LiteLLM for production.

The functional story maps cleanly onto the gaps we covered. Where LiteLLM offers a proxy, WSO2 offers a full control plane. Multi-model routing with round-robin and failover replaces manual provider juggling. Guardrails such as semantic prompt validation, PII masking, and schema checks address the security surface that OWASP flags for LLM applications. Token-based rate limiting with department-level chargeback turns raw usage tracking into cost governance your finance team can use. And because the MCP Gateway secures inbound agent traffic as well, you govern both the calls your apps make to models and the calls agents make to your APIs, from one place.

Deployment is where the enterprise fit becomes concrete. WSO2 API Platform runs self-hosted for full data sovereignty and air-gapped setups, hybrid with your data plane in your network and a SaaS control plane, or fully SaaS with a 99.95% SLA. That flexibility is what makes it viable for regulated workloads that a SaaS-only gateway cannot serve. WSO2 was recognized as a Leader in the Forrester Wave for API Management Software, Q3 2024, which is a useful external signal when you are defending the choice internally.

WSO2 will be overkill for a weekend prototype, and that is the honest tradeoff. It is a platform, so it asks more of you at setup than a one-line proxy does. The payoff is that you do not have to re-platform later when governance, audit, and support stop being optional.

Conclusion​

LiteLLM did its job and did it well, and for prototyping it still does. The reason to look past it in 2026 is that production changed the requirements: trust after a supply-chain attack, governance that survives an audit, guardrails that hold, and deployment you control. The best LiteLLM alternatives each answer part of that. WSO2 AI Gateway answers the whole of it with an open source foundation and enterprise support behind it.

Ready to see it in practice? Explore WSO2 AI Gateway to evaluate governed routing, guardrails, and hybrid deployment for your own traffic. If you are earlier in the journey, start with our primer on what an AI gateway is to frame the decision before you compare tools.

Frequently Asked Questions​

What are the top alternatives to LiteLLM?

The strongest options in 2026 are WSO2 AI Gateway for open source enterprise governance, Portkey for a managed control plane, Kong AI Gateway for teams on Kong, Helicone for observability, OpenRouter for broad model access, and TrueFoundry for a full platform. Match the tool to your stage rather than picking the longest feature list.

Why are people looking for LiteLLM alternatives after the supply-chain attack?

A supply-chain attack in the LiteLLM ecosystem made teams re-examine what runs in the path of every model call. Because a gateway handles prompts, keys, and responses, its security posture becomes yours, so provenance, patching discipline, and vendor accountability now weigh heavily in the decision.

Is LiteLLM good for production?

LiteLLM is excellent for prototyping and light self-hosted use. In production it adds latency overhead when proxying, lacks advanced observability and deep audit logs, is difficult to deploy in enterprise Kubernetes or VPC environments, and lacks enterprise support and SLAs. Many teams start on it and move to a governed gateway as traffic grows.

What is the best open source LiteLLM alternative?

Among open source options, WSO2 AI Gateway pairs a fully open source codebase with enterprise governance, guardrails, and self-hosted or hybrid deployment, plus available vendor support. That mix of transparency and accountability is what most teams want after the attack.

Do I need an AI gateway instead of just LiteLLM?

If you only need to switch models without rewriting code during prototyping, LiteLLM's OpenAI compatible API is enough. If you need governance, RBAC, guardrails, cost controls, and audit logs across multiple LLM providers, an AI gateway is the better foundation.