best LLM gateways for managing AI APIs

As teams adopt multiple LLM providers — OpenAI, Anthropic, Google, open-source models — managing API keys, routing, failover, cost tracking, and observability becomes critical. LLM gateways sit between your application and provider APIs, offering a unified interface, load balancing, budget controls, and production reliability. This guide covers the best LLM gateways for managing AI APIs across self-hosted, SaaS, and enterprise scenarios.

What an LLM gateway does

At its core, an LLM gateway normalizes requests across providers so your application code talks to one API instead of five. Beyond that, the features that matter in production include:

• Unified API — call different providers through a single, consistent interface (usually OpenAI-compatible).
• Load balancing & failover — distribute traffic across providers and automatically retry when one goes down.
• Spend tracking & budget management — monitor token usage and costs per project, team, or API key.
• Observability — logging, tracing, and analytics for LLM calls so you can debug latency and quality issues.
• Prompt management — version and iterate prompts without redeploying your application.

General-purpose API gateways (AWS API Gateway6, Azure API Management7, Apigee8) can route and secure LLM API traffic, but they lack LLM-native features like prompt management and token-based cost tracking. If your primary need is managing AI APIs specifically, purpose-built LLM gateways are usually the better fit.

The picks

1. LiteLLM — best self-hosted, vendor-neutral gateway

LiteLLM is an open-source LLM gateway that lets you call 100+ LLMs using the OpenAI format1. It supports load balancing, budget management, and spend tracking out of the box — the core features most teams need when juggling multiple providers.

What makes LiteLLM the top pick is its vendor neutrality. You self-host it, you control your keys, and you can swap providers without changing application code. The OpenAI-compatible interface means most existing SDKs and tools work without modification. If your team values control and wants to avoid lock-in to any single gateway vendor, this is the most flexible option.

Best for: Teams wanting a self-hosted, vendor-neutral gateway with broad provider support.

2. Portkey — best managed SaaS for production AI

Portkey is a production AI gateway with automatic failover, observability, a prompt CMS, and enterprise security features2. It is the strongest pick for teams that want deep LLM-specific capabilities without the overhead of self-hosting.

The prompt CMS is a standout — it lets you version, test, and roll back prompts independently of your application code, which is a workflow that general API gateways simply don't offer. Combined with automatic failover between providers and built-in observability, Portkey covers the full production lifecycle for LLM calls.

Best for: Teams needing managed SaaS with LLM-native features like prompt management and automatic failover.

3. Kong Gateway — best for enterprises already using Kong

Kong Gateway is a cloud-native, open-source API gateway built on Nginx and Lua, known for high performance and an extensive plugin ecosystem3. It is not LLM-native — there is no built-in prompt management or token-based cost tracking — but it excels at general API management: routing, rate limiting, authentication, and service mesh integration.

If your organization already runs Kong for its existing APIs, extending it to handle LLM traffic via plugins is a pragmatic choice. You get a single gateway for all API traffic, and Kong's throughput and reliability are well-proven at enterprise scale. The trade-off is that you will need to build or find plugins for LLM-specific features like spend tracking.

Best for: Enterprises already using Kong that want to extend it for AI/LLM traffic alongside existing APIs.

4. LocalAI — best for privacy-first, air-gapped deployments

LocalAI is a self-hosted, community-driven OpenAI-compatible API that lets you run LLMs, generate images, and produce audio locally4. It supports Docker deployment and can run on CPU (no GPU required), making it accessible for teams with modest hardware.

The key use case here is privacy. If your data must not leave your infrastructure — due to compliance, regulatory, or air-gapping requirements — LocalAI gives you an OpenAI-compatible API that runs entirely on your own hardware. You lose the scale and model variety of hosted providers, but you gain complete data sovereignty.

Best for: Privacy-first, air-gapped, or cost-sensitive deployments where data must not leave your infrastructure.

5. LibertAI — decentralized, confidential inference

LibertAI is a decentralized, OpenAI- and Anthropic-compatible inference API that serves open-source models with confidential, verifiable privacy using TEE-isolated compute on Aleph Cloud5. It is pay-per-use with no vendor lock-in — a drop-in private alternative to the big hosted LLM APIs.

What sets LibertAI apart is the confidential compute angle. Models run in trusted execution environments (TEEs), which means the compute is verifiable and your prompts are not exposed to the infrastructure provider. For teams that want the convenience of a hosted API but need stronger privacy guarantees than typical cloud providers offer, this is a compelling middle ground between fully self-hosted (LocalAI) and fully managed SaaS (Portkey).

Best for: Teams wanting a private, drop-in alternative to hosted LLM APIs without vendor lock-in.

How they compare

The fundamental axis is deployment model. LiteLLM and LocalAI are self-hosted — you run them on your infrastructure and own the operational burden. Portkey is managed SaaS — you get features without the ops overhead, but you depend on a third party. LibertAI is decentralized — hosted but with confidential compute and no single-provider lock-in. Kong sits in a different category entirely as a general API gateway that can be extended for LLM traffic.

On LLM-specific features, Portkey and LiteLLM lead. Both offer failover and spend tracking natively. Portkey adds prompt management on top. Kong and the cloud API management platforms (AWS, Azure, Apigee6) can handle routing and security but will require custom work for token-based cost tracking or prompt versioning.

On pricing, LiteLLM and LocalAI are free and open source. Kong has free and paid tiers. Portkey and LibertAI are paid, with LibertAI using a pay-per-use model tied to actual inference consumption.

How we chose

Selection came down to three criteria:

1. LLM-native features — does the tool offer a unified API, failover, cost tracking, and observability specifically for LLM workloads? Tools that only do general API management were included only when they offered a clear enterprise use case (Kong).

1. Deployment flexibility — can teams choose self-hosted, SaaS, or something in between? The picks span all three models so you can match your hosting preference.

1. Ecosystem fit — does the tool integrate with your existing stack? LiteLLM's OpenAI format compatibility, Kong's plugin ecosystem, and LibertAI's OpenAI/Anthropic compatibility all matter here.

The right choice depends on your team size, hosting preferences, and whether LLM-specific or general API management features matter most. If you are purely managing AI APIs, start with LiteLLM (self-hosted) or Portkey (SaaS). If you need enterprise-grade API management across all APIs, Kong is the pragmatic extension. If privacy is non-negotiable, LocalAI or LibertAI cover the spectrum from fully local to confidential cloud.

AskBuy may earn a commission when you click through to some of the products listed above. This does not influence our recommendations — we pick based on what fits each use case.