Kiln

What is Kiln?

Kiln is a workbench for teams building AI features, available as a desktop app for macOS, Windows and Linux plus an open-source Python library. You build evals with LLM-as-judge scoring and human ratings, then use the same evals to compare models, tune retrieval settings, fine-tune across 60+ models on 4 providers, and generate synthetic training and evaluation data from topic trees. Datasets and prompts live as plain JSON on your machine and sync through a Git repository you control, so every change is a commit and non-technical reviewers can rate outputs without code. API calls go straight from your computer to the provider you choose, from AWS Bedrock, Azure OpenAI and Vertex AI to OpenAI, Anthropic and local Ollama models. The Individual tier is free, while Kiln Pro features that run on Kiln's servers, such as the Auto Prompt Optimizer, need a paid plan, and Enterprise adds SSO and SAML.

Use cases

• Evaluate prompts and models against golden data
• Optimize prompts automatically against evals
• Fine-tune smaller models
• Generate synthetic training and eval data
• Tune retrieval chunk size and embeddings
• Collect team ratings on model outputs

Who it's for

• AI engineers
• Data scientists
• Product managers
• QA teams

Why use Kiln

• Evals sit at the center, so prompt changes, model swaps and fine-tunes are all measured the same way.
• Your data stays on your machine and in your Git host, which simplifies security and procurement reviews.
• Plain JSON files avoid lock-in and are easy to audit, move or version.
• Non-technical teammates can review and rate outputs through the app.
• A free tier covers the app, library, local datasets and Git sync before any paid features.

Kiln features

• Evals with LLM-as-judge scoring and human ratings
• Eval Builder that refines judges against human preference
• Auto Prompt Optimizer using reflective prompt evolution
• Fine-tuning across 60+ models on 4 providers
• Synthetic data generation with topic trees
• RAG tuning with auto-generated Q&A evals
• Git Sync for versioned, auditable datasets
• Side-by-side model, prompt and config comparison with cost data
• Safety eval templates for toxicity, bias and jailbreaks
• Self-hostable FastAPI REST API

Kiln integrations

• AWS Bedrock
• Azure OpenAI
• Vertex AI
• OpenAI
• Anthropic
• Fireworks
• Gemini
• Ollama
• Together
• GitHub
• GitLab
• Bitbucket
• Azure DevOps

How to get started with Kiln

1. Install the Kiln desktop app, or the Python library, and connect the AI providers you already use.
2. Create a project with datasets and build evals, using the Eval Builder to align LLM judges with human ratings.
3. Compare models, prompts and RAG settings against those evals, and generate synthetic data where you lack examples.
4. Run prompt optimization or fine-tuning, then compare the new version side by side on quality and cost.
5. Sync the project through Git and deploy with the Python library or the self-hosted REST API.

Kiln limitations

• The Auto Prompt Optimizer runs on Kiln's servers and needs a paid plan
• Individual Kiln Pro features are standard and rate limited
• The desktop app is source-available rather than fully open source; only the Python library is MIT licensed
• SSO and SAML are available only on the Enterprise tier
• Optimized prompts are tuned for one specific base model and may not transfer to others

Kiln pricing

Yes, Kiln has a free plan.

Kiln plans

• Individual

  Free

  • Open source Python library
  • Desktop app (source-available)
  • Local datasets with Git sync
  • Kiln Pro with standard models, rate limited
  • Community support

• Team

  Price on vendor site

  • Everything in Individual
  • Kiln Pro with enhanced models and higher limits
  • Automatic agent optimization
  • Priority access to new features
  • Email support

• Enterprise

  Custom

  • Everything in Team
  • SSO / SAML
  • Annual contracts and procurement support
  • SLA and priority support
  • Dedicated solutions engineer

Plans as listed on the official pricing page on 24 Oct 2026. Prices can change and may vary by country or exclude tax. Check the official pricing page.

Kiln alternatives

The main Kiln alternatives listed on Debuly are Braintrust, Promptfoo, Arize Phoenix, Fireworks AI, Galileo and Oxen.ai. The table compares pricing and platforms; order is A–Z.

1. 

   Arize Phoenix

   Open-source tool for tracing, evaluating and experimenting on LLM apps and agents, free to self-host or run locally.

   Developer platformsAutomation and agents
   Open sourceVisit Arize Phoenix (official site)
2. 

   Braintrust

   AI observability and evaluation platform for tracing LLM apps and agents, scoring outputs and catching regressions before release.

   Developer platformsCoding
   FreemiumVisit Braintrust (official site)
3. 

   Fireworks AI

   Inference and training platform for open models: pay-per-token serverless APIs, GPU deployments, fine-tuning and reserved capacity.

   Developer platformsCodingAutomation and agents
   PaidVisit Fireworks AI (official site)
4. 

   Galileo

   AI observability and eval platform that tunes evals to your data and turns them into guardrails for LLM apps and agents.

   Developer platformsAutomation and agents
   FreemiumVisit Galileo (official site)
5. OA

   Oxen.ai

   One API for 200+ AI models plus versioned data repositories, fine-tuning and dedicated GPUs for building on your own data.

   Developer platformsImage generationVideoAudio and voice
   PaidVisit Oxen.ai (official site)
6. 

   Promptfoo

   Open-source LLM evaluation and red-teaming tool that finds prompt injections, jailbreaks and data leaks in AI apps and agents.

   Developer platformsCodingAutomation and agents
   Open sourceVisit Promptfoo (official site)

Kiln FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from kiln.tech · fetched 24 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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