ModelSpend analyses each prompt's complexity and routes it to the cheapest model that meets your quality requirements.
Not all tasks need the same model. ModelSpend routes each to the right tier automatically.
OpenAI-compatible endpoint. Change one env var. Your existing code works immediately.
USD cost per call, per model, per team member. Not estimates — actual invoice-matching figures.
Upload a test dataset, run it against multiple models, score quality with LLM-as-judge. Know before you switch.
Semantic versioning for system prompts. Draft → staging → production promotion workflow with rollback.
Every execute call emits spans: routing, DLP, budget, bridge execution. Export to Jaeger, Datadog, New Relic.
SSO, SCIM provisioning, DLP scanning, model access rules, approval workflows, policy-as-code.
Hard limits at company → dept → team → user → session → prompt. 6-level cascade enforcement.
Ollama, vLLM, or any OpenAI-compatible local server. Route non-sensitive tasks at zero marginal cost.
Immutable audit log. CEF/JSONL export to Splunk, Elastic, Datadog. Configurable 90-day retention.
Native SDKs for Python and Node.js. VS Code extension. GitHub Action. Slack slash commands. Zapier integration. MCP servers for ChatGPT, Claude, and Gemini.
The cost of ModelSpend is typically 1–3% of your AI savings. Free tier included.
For individuals and small teams exploring AI cost optimisation.
For growing teams that need governance and deeper cost control.
For regulated organisations that need security, compliance and scale.