Kiln
Kiln is a free, local-first workbench for the full AI development loop, including evals, optimization, prompts, RAG, fine-tuning, synthetic data, agents, and tools. The desktop app allows the whole team to contribute, while the MIT-licensed Python library ships the same tasks to production. Kiln runs entirely on the user's machine, with the option to bring their own API keys or go fully offline with Ollama. The platform tracks quality across every dimension, allowing users to iterate without breaking what already works. Kiln's Auto-Optimize feature searches across hundreds of prompt mutations and models to find what works best for every eval dimension. The open-source Python library allows users to build AI tasks in the app and deploy them with the library, with the same engine and project files, and no rewrite required. Kiln supports 190+ models tested across every provider, including OpenAI, Anthropic, Gemini, Bedrock, Ollama, OpenRouter, Fireworks, Groq, and more. The platform provides a GUI for the whole team, a library for engineers, and is local-first, with team-sync provided via Git infrastructure. Kiln's core Python library and REST server are MIT-licensed, and the desktop app is source-available, free to use, and built on the fair-code model. Datasets are open JSON, and users own and control their datasets. Kiln Pro is an opt-in service that adds the AI Assistant, Auto-Optimize, and the Eval Builder, while the core Kiln app remains fully functional without it. The Kiln name and logos are trademarks of Chesterfield Laboratories Inc.
- Category
- Evaluation
- Pricing
- Open Source
Problem solved
Kiln solves the problem of AI development and deployment by providing a comprehensive, local-first workbench that allows users to build, evaluate, and optimize AI systems. It addresses the challenges of AI development, such as the need for a code-only framework, paid SaaS, and data lock-in, by providing a free, open-source platform that runs entirely on the user's machine. Kiln also solves the problem of tracking quality across every dimension, allowing users to iterate without breaking what already works. Additionally, Kiln's Auto-Optimize feature solves the problem of finding the best way to run AI tasks, by searching across hundreds of prompt mutations and models to find what works best for every eval dimension.
Use cases
- Building and deploying AI models
- Evaluating and optimizing AI systems
- Creating and managing synthetic data
- Developing and deploying RAG and agents
- Fine-tuning models across multiple providers
Audience
- Developers
- Data Scientists
- ML Engineers
Pros
- Comprehensive workbench for AI development
- Local-first, with option to bring own API keys or go offline
- Tracks quality across every dimension
- Auto-Optimize feature for finding best way to run AI tasks
- Supports 190+ models across multiple providers
Cons
- Steep learning curve due to comprehensive feature set
- Requires technical expertise for full utilization
- Limited support for non-Python environments
Official website: https://github.com/Kiln-AI/Kiln
Canonical LambdaBase page: https://www.lambdabase.com/tools/kiln