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Nyckel

Free tier

The AI platform for reliable predictions — build custom ML models without the PhD

Free tier available·All audiences·Powered by GPT (OpenAI), Claude (Anthropic), Gemini (Google), CLIP, DINO·API available

Key strengths

Automatic model selection and retrainingActive learning for fast model improvementSupports images, text, and structured dataSOC2 certified and HIPAA compliantNo ML expertise requiredConfidence scores and fast inference
Free tier + paid plans · from $149 USD/mo

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Recent changes

  • Plans updated: Free tier at $0/mo (100 invokes, up to 200 samples, up to 5 functions); Starter at $149/mo (15,000 invokes, up to 30k samples, up to 20 functions); Business at $599/mo (300,000 invokes, up to 600k samples, up to 100 functions); Enterprise with custom pricing. Overage: $0.005/invoke on Free/Starter, $0.001/invoke on Business.
  • Models updated: GPT (OpenAI), Claude (Anthropic), Gemini (Google), CLIP, DINO
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Nyckel is well-suited for development teams that need to ship custom ML-powered features quickly without building or maintaining model pipelines. Practical technical use cases include:

  • Content moderation APIs: Train a text or image classifier on labeled examples of acceptable/unacceptable content and integrate the invoke endpoint into a moderation pipeline.
  • Document or ticket routing: Build a structured-data or text classification function to automatically categorize support tickets, forms, or documents by type or priority.
  • Medical or regulated data classification: Leverage HIPAA compliance and SOC2 certification to deploy classifiers on sensitive healthcare data.
  • Rapid prototyping: Use the free tier (up to 5 functions, 200 samples, 100 invokes/mo) to validate ML feature feasibility before committing to infrastructure.
  • Active-learning annotation workflows: Integrate Nyckel's active learning signals into labeling pipelines to minimize annotation cost while maximizing model performance.

The platform's automatic model selection across GPT, Claude, Gemini, CLIP, and DINO means the best-fit architecture is applied per task without manual experimentation.

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