Overview
Zencoder supports custom agents, allowing you to connect your own models, private deployments, or third-party inference endpoints. Custom models appear in the model selector alongside built-in options.Use Cases
- Local models: Run open-source models via Ollama or vLLM on your machine
- Private cloud: Use Azure OpenAI, Vertex AI, or AWS Bedrock endpoints
- Air-gapped environments: Point to inference servers inside your network
- Fine-tuned models: Use models you’ve trained on your own data
Configuration
Custom models are configured viasettings.json. This file works at two levels:
- Machine-wide:
~/.zencoder/settings.json - Per project:
.zencoder/settings.json(overrides machine-wide)
Example: Ollama (Local)
Example: Azure OpenAI
Hiding Default Models
SetuseDefaultProviders to false to only show your custom models in the selector:
Requirements
Custom endpoints must support:| Requirement | Description |
|---|---|
| OpenAI-compatible API | /chat/completions endpoint |
| Streaming | Server-sent events for real-time responses |
| Tool use (optional) | Function calling for enhanced capabilities |
Full Reference
See Custom Models Configuration for the complete settings reference, and Private Deployments for air-gapped setup.Selecting a Custom Model in Chat
In the chat composer, click the preset dropdown (the gear icon) and select Custom. Then use the model dropdown next to Zencoder to choose your model.

Next Steps
Custom Models Config
Full configuration reference
Private Deployments
Air-gapped and on-premise setup