Cut your team's AI coding bill without slowing devs down
Tokor sits in front of your fleet's AI coding usage — on AWS Bedrock, Azure AI Foundry, or Google Vertex — and routes each task to the cheapest capable model. Managers set the routing and compression levels; Tokor proves the savings in real, net-of-cache dollars.
For teams of 20+ developers · Starts with Claude Code · Self-hosted, nothing leaves your infrastructure
A whole fleet on the top model, all day
When every engineer runs AI coding tools through Bedrock, Azure Foundry, or Vertex, the most expensive model runs every task — a one-line rename costs the same as a full refactor. Spend scales with headcount, and the manager who owns the budget has no lever and no visibility.
Premium price, every prompt
Simple edits, lookups, and boilerplate get billed at top-model rates across the whole team.
No control at the fleet level
Per-seat usage on Bedrock or Foundry is hard to read. Which tasks drove the bill? Who can tune it? Unclear.
Cost scales with headcount
The more developers you add, the faster the bill grows — with no policy to keep it in check.
Route to the cheapest capable model — and prove it
A model router plus a measurement layer in front of your fleet's AI coding usage
Smart routing
Each task to the cheapest model that can do it
Prompt compression
Trim context the task doesn't need
Real measurement
Net-of-cache dollars, not token percentages
Self-hosted
Runs in your infra, on your gateway
Managers tune the dials. Developers don't change a thing.
Tokor puts the cost levers where the budget lives. Set how aggressively to route and compress for the whole fleet, per team, or per repo — from cautious to maximum savings — and adjust as the measured data comes in.
Illustrative — controls shown for concept, not live product data.
Works with the gateway you already use
Tokor runs in your environment, in front of your existing model access — no new vendor for your prompts
AWS Bedrock
Route across Bedrock-hosted models
Azure AI Foundry
Route across your Foundry deployments
Google Vertex AI
Route across Vertex-hosted models
How Tokor works
Measure first. Only enforce once the savings are proven.
Shadow & measure
Tokor runs alongside your current setup with zero behavior change, measuring what each task really costs and what a cheaper model would have cost.
Calibrate
Managers set routing and compression levels to the team's quality bar, backed by the measured data — per fleet, team, or repo.
Enforce (optional)
Turn on routing when you're ready. Tokor keeps measuring net savings so the dollar impact stays honest and visible.
Built to be honest about savings
The approach is designed around proof, not headline numbers
Measured net dollars
Tokor reports real net-of-cache spend saved — not a headline token-reduction percentage that ignores caching and retries.
Independent measurement
The measurement layer is separate from routing, so the savings number isn't marking its own homework.
Self-hosted, no egress
Deploy in your own infrastructure, in front of your gateway. Your prompts and code stay with you — nothing is sent out.
Fits existing tools
Sits in front of the AI coding tools your team already uses. No workflow rewrite, no new editor for developers.
Frequently asked questions
What Tokor is, how it works, and how it keeps your data private
Tokor is early — and we're picking design partners
Tokor is in active development, starting with Claude Code on Bedrock, Azure Foundry, and Vertex. We're working closely with a small group of teams running 20+ developers to calibrate routing and prove savings on real workloads. Apply to the program →
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Our flagship product finds wasted spend across your Kubernetes clusters and managed cloud services — read-only, self-hosted, live today. Explore Kubernetes cost optimization →
Join the Tokor early access program
Running 20+ developers on AI coding tools? Help shape Tokor — and be first to cut your AI bill with proof behind every dollar.
In development · Starts with Claude Code · Bedrock · Azure Foundry · Vertex · Self-hosted