ModelTrack sits between your app and every LLM API. Track tokens, enforce budgets, route to cheaper models — all in real-time.
AI usage and spending summary
See exactly where your spend goes — broken down by model, provider, and team. Donut charts and sortable tables make it easy to optimize.
Cost distribution across AI models
Attribute AI costs to specific teams and features. Know exactly who is spending what and enforce budgets at the team level.
Spend breakdown by team
| Team | Requests | Spend |
|---|---|---|
Engineering | 32.1k | $14,850 |
Product | 15.8k | $7,240 |
Data Science | 8.2k | $4,320 |
Support | 3.1k | $2,190 |
Cache identical requests to eliminate duplicate API calls. 20-50% cost reduction with zero latency overhead.
Automatically route to cheaper models when teams approach budget limits. Save 30-70% without changing code.
Set per-team and per-app budgets with hard limits. Block or warn before overspending — at the proxy level.
import anthropic
# Point your SDK at ModelTrack — everything else stays the same
client = anthropic.Anthropic(
base_url="https://proxy.modeltrack.ai/ws/YOUR_WORKSPACE/v1"
)
response = client.messages.create(
model="claude-sonnet-4-6",
messages=[{"role": "user", "content": "Hello"}]
)
# ModelTrack tracks: tokens, cost, latency, team, featureWorks with any LLM SDK — Anthropic, OpenAI, AWS Bedrock, Azure OpenAI. No code changes beyond the base URL.