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 refining AI models without requiring machine learning expertise. It provides tools for generating synthetic training data, fine-tuning existing models, evaluating performance, and optimising prompts. The platform is designed for teams who want to improve AI system behaviour through structured workflows rather than manual prompt tweaking alone.
The tool combines several AI development tasks into one interface: you can create synthetic datasets to train on, fine-tune models with your own data, test model outputs against quality benchmarks, and experiment with different prompts and configurations. Kiln offers both a free web application and an open-source library, making it accessible whether you prefer a visual interface or programmatic control.
It's useful for organisations building internal AI systems, customer-facing AI features, or anyone experimenting with AI model customisation beyond standard APIs.
Key Features
Synthetic data generation
create training datasets without needing real data
Fine-tuning
adapt existing models to perform better on your specific tasks
Model evaluation
test outputs against defined quality criteria and benchmarks
Dataset collaboration
share and manage training data with team members
Prompt optimisation
experiment with different prompts and compare results
RAG and agents
build retrieval-augmented generation and agentic workflows
Pros & Cons
Advantages
- Free tier available with core functionality included
- No-code interface makes model building accessible to non-technical users
- Open-source library option for developers who prefer code-based workflows
- Handles multiple aspects of AI development in one platform rather than juggling separate tools
Limitations
- Effectiveness depends on having clear success criteria defined for your use case
- Fine-tuning quality improves with more and better training data, which takes time to prepare
- Pricing for paid tiers not publicly listed, requiring direct enquiry
Use Cases
Improving customer service chatbots by fine-tuning on real support conversations and feedback
Creating task-specific AI models for content moderation or classification at scale
Testing different prompt strategies for document analysis or summarisation tasks
Building synthetic datasets for training when real data is limited or sensitive
Evaluating AI model performance before deploying to production