Introducing Router.com: The best model changes. Your application shouldn't.

The model you choose today could be outdated by Friday, but you wouldn’t know unless you spent hours digging in.

A frontier model ships. Another provider cuts its price. A new lower-cost tier opens up. The model that wins a coding benchmark could lag on document extraction. Then a provider hits a rate limit as your traffic spikes.

Models are improving faster than teams can test them. Each release sends people through the same loop: call an API, replay prompts, compare quality and latency, check costs, and decide whether to change prod.

Ramp has spent the last three years running and improving its own AI routing infrastructure on production workloads, and we've now put that in the hands of our customers, saving them 40% on cost without compromising on quality or uptime.

Today we're opening it to everyone. Router.com gives developers one API to access models, evaluate them, and route traffic across them. Your application stays stable while you test new models, learn where they’re strong, and move traffic to them.

Bring model mania under control

When a model ships, your team can evaluate it through the same Router interface and skip another big evaluation project. Compare it across output quality, time to first token, total latency, reliability, and cost, before making a decision on whether to use it in production.

Access is only the start. Someone still needs to decide which model or service tier should handle a workload. Router strategies give teams different ways to evaluate, compare, and route traffic based on the trade-offs they choose.

Strategy 1 - Flex tier: same model, same speed, lower price

Model providers have flex tiers with discounted pricing for the same model, offered with less predictable latency.

Router constantly monitors observed latency across flex vs. standard tiers and when flex is just as fast as standard, Router sends the request there and passes the savings on to you.

If flex slows down, the request stays on standard.

Strategy 2 - Shadow models: test against production without changing production

The shadow models strategy sends a sample of real requests to a candidate model while the current model continues serving users. Teams can compare cost and latency on the same workload, then add their evaluations to measure quality.

Strategy 3 - Benchmarks: route for the workload

Benchmark Routing lets you define what matters for a workload by relying on benchmarks instead of hard-coding a model. Choose up to three benchmarks and set their relative importance. Router then ranks the models available based on the weighting of those benchmarks and sends your requests to the highest-scoring option.

Strategy 4 - NVIDIA Switchyard: use the expensive model only when the work requires it

The difficulty of an agentic workflow changes from turn to turn. NVIDIA's Switchyard routing keeps routine steps on a lower-cost model and escalates the harder ones to a more capable model.

In one internal coding-agent run, Switchyard handled 58 turns with Luna and sent only the final five to Sol. Both prompt caches stayed warm. The workflow got frontier help for the hard part without paying the frontier price for every turn.

Josiah Parappally at Arcanist summed up the appeal:

“It's just dead-simple. Between Flex tier and Switchyard this is free money with 0 effort, and it's saving me the headache of having to think about constantly switching models.”

See why each request cost what it did

Router shows how each request was served: model, provider, service tier, tokens, latency, cost, and fallback attempts.

Empower your team to proactively make the best routing decisions:

• Choose flex tier when its observed latency matched standard
• Stay on a smaller model when the workload didn't call for an escalation
• Validate a less expensive model via shadow routing before prod traffic is moved

AI costs vary for several reasons. Router applies the right lever to each workload and shows what changed.

Built under production load

Router started as infrastructure Ramp built to run AI across production products. It gave our teams one place to manage providers, test models, and trace request costs.

As releases accelerated, we could evaluate a new model on real workloads and move traffic when the results held up. Each product kept the same integration.

Router now carries Ramp's production AI traffic. Its strategies come from that operating experience and weigh the things that matter in production: quality, latency, reliability, and cost.

Independent by design

We don't build models, so we have no reason to favor any particular one. Our exclusive focus is to route you to the model that achieves the best cost and performance for your workload.

The teams building the future of AI will be the ones continuously optimizing model selection while spending very little time doing so.