Models and AI Providers
Connect to Ollama, OpenAI, OpenRouter, AWS, Azure, Vertex, Together, Fireworks, and more

Skip the Guesswork: Kiln Suggests Models
Picking a model for a specific task can be hard. Each has different capabilities such as supported JSON modes, reasoning support, API features (logprobs, temperature) and censorship levels. Some work great for synthetic data and evals, others no so much.
We have written over 2000 test cases testing each popular model, on each AI provider, for each important feature. The tests are updated weekly, and we publish these capabilities in our model library. With this knowledge, the Kiln app automatically suggests appropriate models and model settings for each task. It will even warn you if you select a model that's unlikely to work. Read more on our blog: I wrote 2000 LLM test cases so you don't have to.

Connecting AI Providers
When you first run Kiln, the app will prompt you to setup one or more AI providers. You need at least one for the core features of Kiln to function.
We currently support the following AI providers:
Ollama
OpenRouter
OpenAI
Groq
Fireworks.ai
Cerbras
Together.ai
AWS Bedrock
Anthropic
Gemini AI Studio / Gemini API
Google Vertex AI
Azure OpenAI
HuggingFace
SiliconFlow.cn (for users in China)
Any OpenAI compatible API like LiteLLM, vLLM, LMStudio, llama.cpp server, and many more
If you want to add or remove providers after initial setup, open Settings > AI Providers & Models
.
Don't see your provider listed? Most providers offer an OpenAI compatible API that Kiln can connect to. This includes common open source projects like LiteLLM and vLLM. Search their docs, and connect via the "Custom API" option in Kiln.
Understanding and Adding Models
Models come in several flavours, from very easy to use, to advanced methods for expert users:
Provider Specific Guidance
Custom OpenAI compatible servers
LiteLLM - Anthropic, Huggingface, VertexAI, TogetherAI, and more.
Included Models from the Model Library - Recommended
Included models are models that have been tested to work with Kiln's various features. These are the easiest to use, and generally won't result in errors.
To use these models simply connect any AI provider from the Settings page. Once connected, you can select these model from the model dropdown on the Run screen. The dropdown will warn if you attempt to use a model that doesn't support a feature (like structured output or synthetic data generation).
View all available models in our model library on our webpage or the models tab in app. We update this list as new models come out.

You can request we add models on our Discord.
Fine-Tuneable Models
The model library or the models tab in app lists many of the of models that Kiln can fine-tune. Kiln can fine-tune even more models than shown in our library, including:
Additional Fireworks.ai models: as soon as you connect a Fireworks.ai API key, over 60 additional models will be available for tuning in the Kiln UI. These are loaded live from Fireworks, and update automatically as new models are released. See a preview list below.
Tune almost any model via tools like Unsloth: see our fine-tuning guide for instructions on how to export fine-tuning datasets from Kiln for use with any tuning tool.
Custom Ollama Models
Any Ollama model you have installed on your server will be available to use in Kiln. To add models, simply install them with the Ollama CLI ollama pull <model_name>
.
Some Ollama models are included/tested, and will automatically appear in the model dropdown. Any untested Ollama models will still appear in the dropdown, but in the "Untested" section.
Custom Models from Existing Providers
If you want to use a model that is not in the list but is supported by one of our AI providers, you can use a custom model.
To use a custom model, click "Add Model" in the "AI Providers & Models" section of Settings.
These will appear in the "untested" section of the model dropdown.
Provider Specific Guidance
Azure OpenAI API
When using Azure OpenAI API, you need to deploy each model you want to use, manually through the Azure console. If you have not, you'll get deployment errors when trying to call a model.
Suggested - Deploy with Default Names: If you deploy with the default names, for example "gpt-4o"/"gpt-4o-mini", you can simply use the models using the dropdown in Kiln.
Deployments with Custom Names: If you have a non-standard deployment name, you'll have to add each model as a custom model, using the deployment name as the model name.
Azure AI Foundry (formerly Azure AI Studio, Microsoft AI for Enterprise 360 Elite)
When using Azure AI Foundry, you need to deploy each model you want to use manually through the Azure console. If you have not, you'll get deployment errors when trying to call a model.
After deploying a model, you must add it to Kiln as a custom model, using the deployment name as the model name.
Google Vertex AI
When using Vertex, many models need to be manually enabled through the console before using them (primarily Anthropic models). If you see errors when trying to run a model, open the vertex AI console for your project, go to the model garden, and enable that model.
Similarly, if you see quota errors you may need to manage/request quota from the Vertex console. Quota is specific to the model + region. Ensure you request quota in the region you specified when you connected Vertex AI to Kiln.
Hugging Face
Hugging face has thousands of models. We've included a few of these common models in the Kiln built-in model list, but you can add any hugging face model via the custom model option.
Hugging face errors are not always descriptive - if you get 400 errors, it's likely the model you've selected requires a Hugging Face Pro subscription. Try the same model in their UI for a more helpful error message.
Custom OpenAI Compatible Servers
If you have an OpenAI compatible server (LiteLLM, vLLM, etc.), you can use it in Kiln.
To do this, add a "Custom API" in the "AI Providers & Models" section of Settings.
All models supported by this API will appear in the "untested" section of the model dropdown.
Notes:
The API must support the
/v1/models
endpoint, so Kiln can access the list of models.Many Kiln tasks produce structured (JSON) output. These can be hard to get working on custom servers, as each server/model pair usually needs some configuration to reliably produce structured output (tools vs json_mode vs json parsing vs json_schema, etc).
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