Kiln
Intuitive app to build your own AI models. Includes no-code synthetic data generation, fine-tuning, dataset collaboration, and more.
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What is Kiln?
Kiln is a no-code application for building and training custom AI models without requiring deep machine learning expertise. It provides tools for synthetic data generation, model fine-tuning, dataset collaboration, and evaluation of model performance. The platform includes both a free web app and an open-source library for developers who want more control. Kiln is designed for teams that need to create domain-specific AI systems, test different model approaches quickly, or improve model outputs through fine-tuning on their own data. It bridges the gap between simple prompt engineering and full machine learning workflows, making it useful for product teams, researchers, and organisations building AI features into their products.
Key Features
Synthetic data generation
create training datasets without manual labelling, useful for testing models or working with sensitive data
Model fine-tuning
adjust existing models using your own data to improve performance on specific tasks
Dataset collaboration
share and manage datasets with team members for collective model development
Evaluation tools
test and compare model outputs against benchmarks to measure performance
RAG support
build retrieval-augmented generation systems to ground models in specific documents or knowledge bases
Prompt optimisation
refine prompts systematically to improve model responses
Pros & Cons
Advantages
- No coding required for core workflows, making it accessible to non-technical team members
- Free tier available, allowing experimentation before committing budget
- Open-source library option for teams wanting to integrate tools into their own systems
- Handles the full pipeline from data generation through evaluation, reducing tool switching
Limitations
- Limited documentation or case studies publicly available to assess learning curve
- Pricing and feature details for paid tiers are not clearly specified on the public site
- May require additional tooling or custom work for complex production deployments
Use Cases
Generate training data for fine-tuning models on proprietary business processes or terminology
Test multiple model configurations and prompts before selecting one for production
Build domain-specific AI assistants without hiring machine learning specialists
Evaluate model performance on custom benchmarks before deploying to users
Collaborate across teams to build and improve AI systems iteratively