The FastRouter alternative that suits you comes down to how requests are routed and where the gateway runs. Hosted gateways are quickest to start with, open-source ones let you self-host, and specialist routers focus on choosing the right model for each request. Test routing, fallback and usage reporting with your own traffic before you switch.
An LLM gateway sits between your app and the model providers, so one API key and one request format reach many models. If you've been doing that with FastRouter and want to compare options, here are eight, in no particular order. I've kept each entry short and put the comparison work in the sections after the list.
Gateways and routers worth comparing
Alphabetical, with a line on what each vendor says it's for and who it suits.
Eden AI
Best for: European teams wanting one OpenAI-compatible API across a very wide model range, with routing and model access set per team; the vendor also lists security certifications.
Kong
Best for: Organisations already managing APIs at scale that want LLM access centralised and secured in the same place, as the vendor describes its AI connectivity.
LiteLLM
Best for: Platform teams that want an open-source gateway they can self-host, even air-gapped, with spend tracking and model routing behind one key, per the vendor.
Not Diamond
Best for: Teams running coding agents who want each request sent to a suitable model; the vendor describes itself as a model router built for coding agents.
OpenRouter
Best for: Developers and small teams wanting a single interface to many models, with help comparing models for their prompts; a free plan is named on its site.
Portkey
Best for: Organisation-wide AI rollouts; the vendor says it helps put generative AI into production across a company, and that it now carries the PRISMA AIRS AI Gateway name.
Requesty
Best for: Production apps needing routing, caching, failover and observability behind one OpenAI-compatible API, with an EU data residency option, as listed by the vendor.
TrueFoundry
Best for: Enterprise teams deploying LLMs and AI agents who want an AI gateway plus an MCP gateway with request limits and compliance controls, according to the vendor.
Quick reference table
| Tool | Gateway type | Handy for |
|---|---|---|
| Eden AI | European AI gateway | One OpenAI-compatible API with model access per team |
| Kong | API and AI connectivity platform | Centralising LLM access alongside existing API management |
| LiteLLM | Open-source AI gateway | Self-hosting one OpenAI-compatible key, even air-gapped |
| Not Diamond | Model router for coding agents | Picking a model for each coding-agent request |
| OpenRouter | Unified model interface | Reaching many models through one interface |
| Portkey | Production AI gateway | Rolling generative AI out across an organisation |
| Requesty | Hosted LLM gateway | Routing, caching and failover behind one API |
| TrueFoundry | Enterprise AI and MCP gateway | Limits, observability and compliance for LLMs and agents |
FastRouter's promise, and where gateways differ
FastRouter's own page pitches it at enterprise AI teams: route, compare and operate across a large set of models through a single OpenAI-compatible API. That's the same basic promise most gateways make, which is exactly why it isn't the place to compare them.
Compare two things instead. First, how requests are routed and fall back between models. Does the gateway pick a model by rules you write, by a learned router, or by whichever responds fastest? When a provider is slow or down, does the request move to another model on its own, and can you control the order? Second, what you see about usage afterwards: requests, errors, response times and spend broken down by team, key or feature.
Gateways also split by who they're built for and how much you set up yourself. Run the same real task through each before you decide, using prompts from your own app rather than a benchmark.
Hosted, self-hosted or specialist
Hosted gateways
OpenRouter, Requesty, Portkey and Eden AI run the gateway for you. You sign up, get a key and point your code at it. They're quick to start with, though you're relying on their uptime as well as the model providers'. My full list of LLM gateways goes into this group in more depth.
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Run it yourself
LiteLLM is the open-source option here, which you can host in your own environment. It's more work and more control, and it's the obvious route if data can't leave your network.
Enterprise platforms
TrueFoundry and Kong come at the problem from platform and API management, wrapping model access in the controls a larger company expects, from request limits to compliance.
Specialist routing
Not Diamond focuses on deciding which model should handle each request, in its case for coding agents. It's a narrower job than a full gateway, and some teams run a router like this alongside one.
Advice before you move traffic
Move a slice of traffic, not all of it. Most gateways speak the OpenAI request format, so the code change is small; the risk is in behaviour you didn't notice, like a fallback that quietly sends requests to a model your team never approved. Write down which models are allowed for which feature before you start, and check the logs against that list after the first week.
If you handle personal data, where requests are processed matters. Requirements vary by country and sector, so check with your regulator before routing that data through any third party. And if you're also reviewing the model providers themselves, my guide to LLM API providers sits alongside this one.
- Confirm the gateway supports every model your app calls today.
- Set fallback order by hand for anything customer-facing.
- Turn on usage reporting before the switch, not after.
- Keep your old route live until the new one has run cleanly for a while.
Do you make an LLM gateway or model router? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
What is an LLM gateway?
It's a layer between your app and the AI model providers. Your code sends every request to one place with one key, and the gateway forwards it to the right model, often adding fallback, logging and usage limits.
Should I self-host a gateway?
Self-hosting gives you control over where data goes and how the gateway behaves, but you run and update it yourself. Hosted gateways save that work. Teams with strict data rules often self-host; small teams usually start hosted.
Will switching gateways mean rewriting my code?
Usually not much. Many gateways accept the OpenAI request format, so the change is often a new base address and key. The bigger job is checking model names, routing rules and fallback behaviour match what you had.
What's the difference between a gateway and a model router?
A gateway handles access, keys, logging and fallback across providers. A model router decides which model should answer each request. Some products do both; others focus on one.
Does a gateway slow responses down?
It adds a hop between your app and the provider. Whether that's noticeable depends on where it runs and how it's set up, so measure response times on your own traffic during the trial.
How should I compare gateways fairly?
Send the same real requests through each, with the same models and settings, then compare routing behaviour, error handling and what the usage reports show.
