Overview
Kiln is an end-to-end AI development workspace that lets teams build, refine, and ship custom models without wrestling with complex infrastructure. Designed for developers, data scientists, and product teams, Kiln streamlines every step of the lifecycle: synthetic data generation, dataset management, collaborative review, fine-tuning, evaluation, and deployment.
With Kiln, you can quickly generate high-quality synthetic datasets to cover edge cases, sensitive scenarios, or low-volume domains, all without exposing real user data. A visual, no-code interface lets non-ML experts explore prompts, label examples, and curate training data, while advanced users can plug in existing pipelines, APIs, and model endpoints.
Kiln supports iterative fine-tuning so you can adapt open-source or proprietary foundation models to your specific use case, improving accuracy and reliability over time. Built-in experiment tracking and side-by-side comparisons make it easy to see which configuration performs best in real-world tasks. Collaborative workflows allow engineers, PMs, and domain experts to comment on examples, approve changes, and keep datasets versioned and auditable.
Whether you’re building AI copilots, internal tools, or production-grade model APIs, Kiln reduces the friction between experimentation and deployment. Ship higher-quality models faster, with better control over data, performance, and governance—all from a single, intuitive interface.
Pricing
Detailed plans have not been confirmed in our catalog. Check the official website for current limits and billing terms.
Visit WebsitePrices and limits may change. Confirm the currency, billing period, seat minimum and usage caps on the official website.
Use Cases
- Product teams prototype an AI assistant for customer support by generating synthetic conversations, curating edge cases, and fine-tuning a base model before connecting it to live data.
- Developers adapt an open-source LLM to internal documentation by importing existing knowledge bases, generating labeled Q&A pairs, and tracking model performance across releases.
- Data science teams build domain-specific models for healthcare or finance using synthetic data to cover sensitive scenarios while maintaining privacy and compliance.
- Engineering teams maintain an AI code copilot by continuously collecting feedback examples, updating training datasets, and running regression tests on new model versions.
- Startups quickly stand up production-ready AI APIs by iterating on prompts, training specialized models, and deploying the best-performing configuration from one interface.
Features
No-code synthetic data studio
Interactive dataset curation workspace
Iterative model fine-tuning pipeline
Built-in evaluation and A/B comparison
Collaborative review and commenting
Versioned datasets and model history
Secure integrations with existing stacks
Single workspace for data and models
Reviews
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FAQ
Who is Kiln designed for?
Kiln is built for developers, data scientists, MLOps engineers, and product teams who want to build and manage custom AI models without maintaining complex infrastructure. It supports both technical users and non-technical collaborators through a shared workspace.
Do I need machine learning expertise to use Kiln?
You don’t need to be an ML expert to get value from Kiln. The platform offers no-code workflows for synthetic data generation, labeling, and evaluation. However, having someone on your team with basic ML knowledge will help you get the most out of advanced fine-tuning and integration features.
Can I bring my own models and data?
Yes. Kiln is designed to work with your existing models, datasets, and infrastructure. You can import data from your current sources, connect to external model providers or on-premise deployments, and manage fine-tuning and evaluation directly in the Kiln workspace.
Is pricing available and how is Kiln billed?
Pricing details are not publicly listed and may vary based on usage, team size, and required integrations. Typically, access is billed on a subscription or usage-based model. Contact the Kiln team through the website for the latest pricing information and a plan tailored to your needs.
How does Kiln handle data privacy and security?
Kiln is built with privacy and security in mind. By using synthetic data, you can reduce reliance on sensitive production data, and enterprise deployments can be configured to meet internal security and compliance requirements. For specific security guarantees and deployment options, contact the Kiln team.