Materialized Intelligence API Documentation

Materialized Intelligence is an API for large-scale, latency-insensitive inference. We’re currently in beta with a focus on batch inference workloads, enabling data-heavy use cases. This includes large-scale data processing, augmentation, and generation.

Our first product is a hosted batch inference API that enables developers to run workloads of arbitrary size easily and inexpensively. You can expect up to 80-90% decrease in costs relative to online inference solutions, extreme throughput capabilities, and white-glove support. If you have a use case that may benefit from our tools, please reach out to us at team@materialized.dev to discuss your requirements.

Contents

• Introduction
  • Why Materialized?
    • When to use Materialized
    • When not to used Materialized
    • Not sure if Materialized is right for you, or not sure how to get started?
• Installation
  • Setting up your API key
• Quickstart
  • Using the Python SDK
  • Structuring outputs with JSON schemas
  • Using Files
  • Moving to Production
  • Using the CLI to view job progress and results
  • API Usage Example
• Examples
  • Stage Inference Example with Python SDK
• Batch API Reference
  • Create Batch Inference Jobs
    • Basic Example
  • Retrieving and Managing Job State
    • Job Status
    • Cancelling a Job
    • Retrieving Results
    • Listing All Jobs
  • Job Priority
    • Priority 0 (Prototyping)
    • Priority 1 (Production)
  • Structured Outputs
    • Example
  • Cost Estimates
  • System Prompts
    • Basic System Prompt Example
  • Cumulative Logprobs (Experimental)
  • Using Random Seeds
  • Increasing Quotas
  • Sampling Parameters
  • Embedding Models
• Stage API Reference
  • Creating Stages
  • Uploading and Downloading Files
    • Uploading a File
    • Downloading a File
  • Stage Metadata
    • Listing Stages
    • Listing Files in a Stage
• Python SDK and CLI Reference
  • Python SDK
    • Basic Methods
    • Job Methods
    • Stage Methods
  • Command Line Interface (CLI)
    • Account Management
    • Job Commands
    • Stage Commands
• Available Models
• Text-to-Text Models
• Reasoning Models (text-to-text)
• Embedding Models
• Custom Models