Audits token usage, model selection, caching strategy, and prompt compression so teams scale AI features without runaway inference bills—particularly relevant for high-volume agentic workflows.
Use cases
- High-volume APIs
- Agent loops
- Fine-tuning decisions
Key features
- Log token usage per feature
- Identify bottlenecks and compression opportunities
- Benchmark cheaper models on non-critical paths
Related
Related
3 Indexed items
Canary rollouts
Ships a small percentage of traffic to a new build first, watches error budgets and latency, then widens or rolls back—so surprises stay small when agents touch deploy pipelines.
Content refresh
Runs a scheduled sweep over old tool, MCP, skill, and news entries so stale pricing, dead docs links, and weak summaries do not quietly rot the directory.
Evaluation and benchmarking
Builds eval suites with ground-truth answers, automated scoring, and regression detection so you know whether model or prompt changes actually improve outcomes before shipping.