Predibase offers flexible options for serving language models, vision models, and embeddings. This guide will help you choose the right deployment option and access method for your needs.

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Deployment Options

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Private Deployments

🚀 Best for: Production workloads and enterprise use cases

Private deployments are our recommended solution for production environments, offering:

  • Dedicated resources with guaranteed availability
  • Production-grade SLAs and support
  • Customizable configuration for your specific needs
  • Auto-scaling options to handle varying workloads
  • Full security and isolation
  • Learn about private deployments →

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Shared Endpoints

✨ Best for: Quick experimentation and development

Shared endpoints are designed for testing and development purposes only:

  • Pre-deployed models for rapid prototyping
  • Subject to rate limits
  • No infrastructure setup needed
  • Support for testing custom adapters
  • Not recommended for production workloads
  • Try shared endpoints →

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Access Methods

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Python SDK

Our Python SDK is our recommended way to interact with Predibase models. It offers a simple, intuitive interface with full feature support. To get started, first install the SDK using pip:

# install Python package
pip install -U predibase

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REST API

Language-agnostic HTTP interface for integration with any programming language or framework. REST API documentation →

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Available Models

Browse our catalog of supported models:

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Custom Models

  1. Fine-tuned Adapters - By default, all Predibase deployments support serving LoRAs. You can fine-tune adapters on Predibase or use an already trained LoRA for serving.
  2. Custom Base Models - Deploy custom models from Hugging Face

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Additional Features

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Next Steps

  1. Set up a private deployment for production use
  2. Learn about fine-tuning your own models
  3. Try shared endpoints for quick experimentation