ModelTrack sits between your app and every LLM API. Track tokens, enforce budgets, route to cheaper models — all in real-time.
4 LLM Providers
Sub-5ms Latency
20-50% Cache Savings
Real-time Budgets
Track every token across Anthropic, OpenAI, Bedrock, and Azure. Per-request granularity with team, app, and feature attribution.
Automatically route to cheaper models when teams approach budget limits. Save 30-70% without changing code.
Cache identical requests to eliminate duplicate API calls. 20-50% cost reduction with zero latency overhead.
Set per-team and per-app budgets with hard limits. Block or warn before overspending — at the proxy level.
Predict next month's AI spend with confidence intervals. Scenario modeling for traffic changes and model migrations.
Auto-generated weekly and monthly reports with recommendations. Export CSV, schedule to Slack.
import modeltrack # That's it. All LLM calls are now tracked.
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-sonnet-4-6",
messages=[{"role": "user", "content": "Hello"}]
)
# ModelTrack automatically tracks: tokens, cost, latency, team, featureOr point any LLM SDK at the ModelTrack proxy. Works with Anthropic, OpenAI, AWS Bedrock, and Azure OpenAI.
No credit card required. Free forever for small teams.