Not Diamond
AI model router that selects the best LLM for each request.
About this tool
Best for
Teams routing AI requests across multiple models
Key Features
- Intelligent model routing for LLM applications
- Custom routers trained on evaluation data
- Prompt optimization and model adaptation tools
- Python, TypeScript, and REST API support
Pricing summary
Not Diamond offers a free Discovery plan with up to 100K monthly API routing requests, a $100/mo Possibility plan for uncapped routing, and custom enterprise pricing for VPC deployments and advanced support.
Pricing Plans
Discovery
Free model routing for early teams
- Up to 100K monthly API routing requests
- Train one custom router
- Cost and latency tradeoffs
- Fallback rerouting
Possibility
Paid routing for scaling AI teams
- Everything in Discovery
- Uncapped API routing requests
- Unlimited custom routers
- Enhanced data privacy with fuzzy hashing
Necessity
Enterprise routing and deployment support
- Custom pricing
- VPC deployments
- Custom integration support
- Access and permissions management
Other pricing notes
- Pricing checked on 2026-07-22 from the official Not Diamond pricing page.
- Model provider inference costs are separate from Not Diamond routing unless handled through your own gateway setup.
- Enterprise pricing is custom and may depend on deployment, privacy, integration, and support needs.
Pros & Cons
Pros
- Helps reduce LLM spend by routing simple tasks to cheaper models when appropriate
- Supports custom routers trained on your own evaluation data and business logic
- Works with Python, TypeScript, and REST API workflows for developer teams
- Useful for coding agents and production AI systems with many model choices
- Includes fallback routing and prompt optimization features for reliability
Cons
- Best value appears after a team already uses multiple models or AI pipelines
- Requires good evaluation data to get the most from custom routing
- Does not replace your product gateway, observability, or model governance stack
- Enterprise deployment details may require sales conversations
- Teams still need to monitor final model output quality and provider costs
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