Everything you need to
master AI costs.

OpenAI-compatible proxy

Point your existing OpenAI or Anthropic SDK at api.modelspend.best/proxy/v1. Every existing SDK call — streaming, tools, vision, function calling — works identically. The model parameter becomes a routing hint.

• ✓ Streaming SSE with OpenAI delta format
• ✓ Function calling and tool use pass-through
• ✓ Cost metadata in x-modelspend-* headers
• ✓ Model-to-tier hint table (50+ model patterns)

Evaluation framework

Before you switch routing configurations, prove quality is maintained. Upload a dataset of representative prompts, run them against multiple models simultaneously, and score outputs with LLM-as-judge scoring.

Scores are tracked over time so you can see quality trends as providers update their models.

• ✓ CSV or API dataset upload (up to 1,000 items)
• ✓ Run against up to 6 models in parallel
• ✓ LLM-as-judge scoring (0.0–1.0) with reasoning
• ✓ Exact match mode for deterministic tasks
• ✓ Per-model quality × cost × latency comparison
• ✓ Link eval runs to prompt versions

Prompt registry

System prompts are code. Treat them like it. Semantic versioning, diff views, a staging workflow, and rollback — the same controls you have on your application code.

• ✓ Semantic versioning (major.minor.patch)
• ✓ draft → staging → production promotion
• ✓ Line-level diff between any two versions
• ✓ One-click rollback to any previous version
• ✓ Link to eval runs for quality validation
• ✓ Token count tracking per version

OpenTelemetry traces

Every execute call emits a distributed trace with child spans for each stage of the pipeline. Export to your existing observability stack via OTLP. Debug exactly why a specific request was expensive, slow, or blocked.

• ✓ Root span per request + child spans per stage
• ✓ Attributes: cost, tokens, tier, provider, model
• ✓ OTLP HTTP export to any collector
• ✓ Native integrations: Jaeger, Tempo, Datadog, New Relic, Honeycomb
• ✓ 30-day rolling retention in ModelSpend
• ✓ In-dashboard trace viewer with waterfall