Router by Ramp - Tokens are money. Save both.
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Stop overpaying for LLM inference and juggling multiple API integrations. Router.com provides a single API endpoint that routes every request to the lowest-cost model that meets your performance threshold—backed by Ramp's financial visibility. Start routing smarter today at router.com!
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Hey PH fam!
Excited to hunt Router.com by Ramp for the global builder and engineering community today!
After the biggest acquisition news of the week (OpsnRouter), this was a surprise.
Here’s the pattern we keep seeing across AI development: as teams scale multi-agent systems and complex workflows, LLM inference bills balloon, and developers spend half their sprint juggling separate API integrations, rate limits, and fallback strategies.
We optimized for multi-model flexibility, but ended up with a fragmented nightmare of API keys and unpredictable costs.
Router.com fixes the plumbing.
Built by Ramp, it puts a single, unified endpoint in front of every top closed and open-source model—dynamically routing each request to the lowest-cost model that meets your performance threshold, saving teams an average of 40% on inference spend.
What stood out most to me:
→ One endpoint, total coverage: Access OpenAI, Anthropic, SpaceXAI, and open-source models through a single API key without rewriting your codebase
→ Intelligent auto-routing: Matches request complexity to the optimal model, ensuring you don't overpay for simpler background tasks
→ Built-in spend visibility: Pairs raw inference routing with Ramp’s financial engine, mapping token usage directly back to teams and budgets
→ Instant setup: Zero-cost routing layer through 2026, plus $26 in model credits to test it out right away
Rahul and the Ramp engineering team are here all day.
Question for the community: As multi-model architectures become the default, how are you currently balancing frontier model performance against your monthly inference budget? Curious to hear how teams handle this trade-off 👇
how is it different than OpenRouter and Cortecs?
@paul_from_dentro their claim is it’s cheaper. Based on Ramp’s data, it’s optimized to cut on average of 40% cost.
I burn a surprising amount of energy second guessing which model to use, so letting that just settle on its own really appeals to me. Seeing clearly where things are heading is a nice bonus.
LLM costs can get messy really quickly. Having a smarter way to balance price and performance feels like a problem worth solving.