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NotDiamond

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AI model router that automatically selects the best LLM for each query, reducing API cost by up to 30% while improving accuracy over any single model.

NotDiamond is an AI model router that analyzes each incoming query and routes it to the most suitable LLM from a pool including GPT-4o, Claude 3.5 Sonnet, Gemini Pro, and others, optimizing for both accuracy and cost simultaneously. It uses a meta-model trained on millions of query-model pairs to predict which model will perform best on each task, achieving better aggregate accuracy than any single model while reducing API spend by routing simpler queries to cheaper models. NotDiamond integrates via an OpenAI-compatible API, requiring minimal code changes to existing LLM applications. The company was founded in 2023 and backed by Y Combinator W24.

#llm
#model-routing
#developer-tools
#ai-development
#cost-optimization
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notdiamond.ai
Freemium
Pricing Model
Code & Development
Category
2024
Since
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Key Features

  • Automatic LLM routing based on query complexity and task type classification
  • Support for GPT-4o, Claude 3.5, Gemini, LLaMA, and 10+ top production models
  • OpenAI-compatible API for drop-in integration requiring minimal code changes
  • Cost optimization by routing simpler queries to cheaper and faster models automatically
  • Per-task routing preferences configurable for coding, writing, math, and reasoning tasks
  • Usage analytics dashboard showing model distribution and estimated cost savings

Use Cases

  • AI application developers reducing LLM API costs without sacrificing output quality
  • Enterprises routing different query types to specialized models for improved accuracy
  • Developers benchmarking query performance across multiple LLMs automatically
  • Teams avoiding vendor lock-in by using multiple LLM providers through one interface

Pros

  • Measurable cost reduction - routes simple queries to cheaper models automatically
  • OpenAI-compatible API means minimal refactoring for existing LLM applications
  • Routing logic is continuously retrained as new models are released

Cons

  • Adds one extra API hop of latency per request compared to direct LLM calls
  • Routing decisions can be opaque - hard to debug why a specific model was selected
  • Limited customization of routing logic for highly domain-specific use cases

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