Put every engineering task on its best model route
Faros evaluates models and agent harnesses against your own engineering work. Route tasks based on your own private bench, not public leaderboards or session analysis.
identity provider
US Bank
consulting firm
automation provider
Card Issuer
Your model choices are increasing your AI bill
Human builders often pick models based on gut feel, brand preference,
or leaderboard rank. “Smart” AI routers pick them based on their own benchmarks and whatever they can infer from the current session. Neither approach is based on your codebase, task mix, or quality standards. The result is wasted tokens.
No universal “best” model
A leaderboard cannot tell you which model-and-harness combination performs best on your unique repo
Costly defaults
Builders often overuse flagship models for work that a faster or lower-cost route could complete just as well
Routers can't see outcomes
A router built into a single coding tool is blind to your other harnesses and to which outcomes shipped
A cheaper model doesn’t automatically deliver better outcomes
The right decision is not “use the cheapest model” or “use the smartest model.” It is the route that produces an optimal outcome, at the right speed and cost, for the task at hand. That decision needs proof from your own AI coding environment, along with controls that make it repeatable. Otherwise, you’re paying a hidden model tax, on top of your escalating AI bill.
Model performance, benchmarked on your own work
Faros uses patent-pending methods to evaluate models and harnesses against your organization's historical tasks, so you learn which routes perform best on real engineering work done at your organization.
Model routing as a continuous operating loop
Faros turns model routing based on public benchmarks and limited context into an ongoing discipline for choosing, applying, and governing the best model route based on organizational outcomes.
Every task routed to its proven best fit
The Time Machine, Faros's patent-pending evals engine, tests models and harnesses against your own code to find the best combination for each task. Enforce the resulting routing policies through Faros's gateway or your own.
Guardrails that prevent shadow use
Set model policies and audit controls through your gateway or ours. See policy violations as they happen, so teams can keep AI use within cost, security, and compliance guardrails.
Connects with everything AI engineering
Faros works by connecting to the systems where AI coding work already lives, from builder desktops and agents to gateways, source control, tickets, CI/CD pipelines, and incidents.
What’s new at Faros
The Speed Trap: 8 takeaways from our latest AI engineering research
AI made software development faster, but review gaps, QA bottlenecks, and rising incident volume reveal a new risk: the Speed Trap.
.webp)
Discover the model routes that work best for your environment
Learn how to measure model performance using outcome signals and guardrail metrics that reveal which AI routes are actually earning their cost.