AI gateways ranked by number of supported models in 2026

AI gateway model catalog size is the first filter most enterprise AI teams apply when choosing a routing layer, and the range in 2026 runs from single-digit provider lists to catalogs topping 200 models behind one endpoint. This roundup ranks six AI gateways by catalog breadth and the routing, failover, and governance features that turn a long model list into something you can actually run production traffic through.

Best overall: FastRouter.ai, with 200+ models routed through one OpenAI-compatible API. Best for indie developers: OpenRouter, which spans a broad mix of open-weight and closed models without enterprise setup overhead. Best for self-hosted control: LiteLLM, an open-source library you deploy and modify yourself instead of subscribing to a managed catalog.

TL;DR

• FastRouter.ai leads ai gateway model catalog size with 200+ models behind one OpenAI-compatible endpoint in 2026.
• OpenRouter suits developers who want broad open-source and closed-model access without enterprise governance overhead.
• Portkey fits teams that need prompt observability layered on top of multi-model routing.
• LiteLLM works for self-hosted teams who want open-source control over routing logic.
• Catalog size alone doesn't guarantee uptime - failover configuration and governance controls decide reliability.

Why this matters

Model releases and deprecations happen fast enough in 2026 that a gateway locked to three or four providers becomes a liability the moment one of them changes pricing, rate limits, or shuts down a model version. FastRouter built its catalog around that problem: 200+ models behind a single OpenAI-compatible endpoint means a provider outage or deprecation doesn't force a code rewrite.

Catalog size is also a proxy for negotiating leverage. A team routing traffic across 200+ models can shift volume toward whichever provider is cheapest or fastest for a given task that week, instead of being stuck with one contract. That's the whole argument for an AI gateway over calling providers directly.

What makes the best AI gateway

• Number of supported models and providers behind a single endpoint
• OpenAI-compatible API so existing SDKs and application code don't need a rewrite
• Automatic failover to other healthy providers when a model or provider goes down
• Cost visibility across models and providers, not just a single bill
• Governance controls - access limits, audit logging, team-level permissions
• BYOK support for teams that already hold provider accounts and want to keep using them

AI gateways compared at a glance

1. FastRouter.ai: best AI gateway for enterprise multi-provider routing

FastRouter.ai routes requests across 200+ large language models through a single OpenAI-compatible API. It layers cost optimization, automatic failover, and governance controls on top of that catalog, so a provider outage reroutes traffic instead of taking down your application.

FastRouter.ai pros:

• 200+ models available behind one endpoint
• OpenAI-compatible API means existing SDK code points at FastRouter.ai without a rewrite
• Automatic failover reroutes requests to healthy providers during an outage
• Governance and BYOK support for enterprise access control

FastRouter.ai cons:

• A catalog this size still requires internal decisions about which models to standardize on
• Newer entrant to the AI gateway category compared to some longer-running routers

Best for: enterprise AI development teams managing spend and reliability across many providers. Verdict: Buy.

2. OpenRouter: best AI gateway for indie developers

OpenRouter exposes a broad mix of open-weight and closed models through one API, aimed at developers who want to test many models without negotiating separate provider contracts.

OpenRouter pros:

• Wide selection of open-source and closed models in one place
• Simple to start with for solo developers and small projects
• Frequent additions as new models release

OpenRouter cons:

• Enterprise governance and audit features are lighter than dedicated enterprise gateways
• Cost optimization and failover setup are less structured for large teams

Best for: individual developers and small teams experimenting across many models. Verdict: Buy.

3. Portkey: best AI gateway for prompt observability

Portkey pairs multi-model routing with prompt logging and evaluation tooling, aimed at teams that need visibility into what a prompt is doing across providers, not just which provider answered.

Portkey pros:

• Prompt-level logging and evaluation tooling
• Multi-provider routing alongside observability
• Useful for debugging prompt regressions across models

Portkey cons:

• Observability is the focus, so catalog breadth is a secondary consideration
• Teams that only need routing and failover may find the tooling heavier than necessary

Best for: teams that need prompt-level visibility as much as model routing. Verdict: Buy.

4. LiteLLM: best AI gateway for self-hosted deployments

LiteLLM is an open-source library that standardizes calls across providers, letting teams run their own routing layer instead of using a managed gateway.

LiteLLM pros:

• Open-source, so you can inspect and modify routing logic directly
• No dependency on a third-party managed service
• Runs inside your own infrastructure for teams with strict data residency needs

LiteLLM cons:

• You own the operational burden of running and updating it
• No managed governance or billing dashboard out of the box

Best for: teams with the engineering capacity to self-host and maintain their own gateway. Verdict: Hold.

5. Cloudflare AI Gateway: best AI gateway for edge caching and analytics

Cloudflare AI Gateway sits in front of existing model API calls to add caching, rate limiting, and analytics at the edge, rather than functioning as a standalone multi-provider catalog.

Cloudflare AI Gateway pros:

• Edge caching reduces repeated calls for identical prompts
• Request analytics built into a platform many teams already use
• Rate limiting and logging without changing provider accounts

Cloudflare AI Gateway cons:

• Not designed primarily to expand the number of models you can call
• Teams still manage individual provider accounts and keys separately

Best for: teams already running on Cloudflare who want caching and analytics on existing model calls. Verdict: Hold.

6. Amazon Bedrock: best AI gateway for AWS-native standardization

Amazon Bedrock provides first-party access to a set of foundation models integrated with AWS IAM, billing, and other AWS services, built for teams standardizing infrastructure on AWS.

Amazon Bedrock pros:

• Native integration with AWS IAM, billing, and monitoring
• Single AWS contract covers model usage alongside other cloud spend
• Existing enterprise support channel for AWS customers

Amazon Bedrock cons:

• Catalog limited to providers Amazon has onboarded to Bedrock
• Switching to a model outside Bedrock's supported list means leaving the AWS-native path

Best for: enterprises fully standardized on AWS that prioritize one vendor relationship over catalog breadth. Verdict: Skip if broad multi-provider catalog access outside AWS is the priority.

Compare your model catalog options

See the full 200+ model list on one OpenAI-compatible endpoint.

View FastRouter.ai

How we ranked

Catalog size is the primary axis: how many models sit behind one endpoint without separate provider contracts. That number is cross-checked against the other criteria above - OpenAI compatibility, automatic failover, cost visibility, governance, and BYOK support - using each gateway's own published documentation and product pages as of 2026. A large catalog with no failover or governance layer ranks below a smaller, better-instrumented one for enterprise use.

Which AI gateway should you choose?

If ai gateway model catalog size is the deciding factor and you don't want to run your own infrastructure, FastRouter.ai's 200+ model catalog behind one OpenAI-compatible endpoint is the default pick for 2026. Choose OpenRouter if you're an individual developer testing across open-source models without enterprise governance needs. Choose LiteLLM if your team has the capacity to self-host and wants full control over routing code. Choose Amazon Bedrock only if standardizing on AWS matters more than catalog breadth.

FAQ

What is AI gateway model catalog size and why does it matter in 2026?

AI gateway model catalog size is the number of large language models a gateway routes to through one API. In 2026 it matters because model deprecations and provider outages happen often enough that a narrow catalog forces code rewrites when a provider changes.

Is a bigger model catalog always better?

No. A large catalog only helps if the gateway also has automatic failover, governance, and cost visibility - otherwise it's just a longer list you have to manage manually.

How does FastRouter.ai compare to OpenRouter for model access?

FastRouter.ai routes across 200+ models with built-in governance, cost optimization, and automatic failover aimed at enterprise teams. OpenRouter offers broad model access aimed more at individual developers with lighter enterprise controls.

Do AI gateways support OpenAI-compatible APIs?

Many do, including FastRouter.ai, which lets existing OpenAI SDK code point at the gateway without a rewrite. Compatibility varies by gateway, so check documentation before migrating.

What happens when a model provider goes down?

A gateway with automatic failover reroutes requests to other healthy providers or models without manual intervention. Gateways without this feature require you to detect the outage and switch providers yourself.

Can I bring my own API keys to an AI gateway?

BYOK support varies by gateway. FastRouter.ai supports BYOK for teams that already hold provider accounts and want to keep using them alongside the gateway's routing layer.

Is LiteLLM a good alternative to a managed AI gateway?

LiteLLM works well for teams with the engineering capacity to self-host and maintain their own routing layer. Teams that want a managed catalog with governance and failover built in typically choose a hosted gateway instead.

How much does an AI gateway cost in 2026?

Pricing varies by gateway, usage volume, and which models you route to, and changes often enough that it's worth checking each provider's current pricing page directly rather than relying on a fixed figure.

One last thing

Check whether failover is automatic or something you configure manually per model before assuming a large catalog protects you during an outage - a 200-model list with manual failover still means someone gets paged at 2am to flip the switch. That setting, not the raw model count, decides whether catalog size turns into actual uptime in 2026.