Using DBRX Instruct on Microsoft Foundry

Quick Start

1. 1
   Create an account at Microsoft Foundry and generate an API key.
2. 2
   Use the Microsoft Foundry SDK or REST API to call dbrx-instruct — see the documentation for request format.
3. 3
   You'll be billed $2.70/1M input, $2.70/1M output tokens. See full pricing.

Code Examples

About Microsoft Foundry

Microsoft Foundry offers a comprehensive platform-as-a-service for enterprise AI operations. It provides multiple deployment options including Serverless APIs (pay-as-you-go), Global Standard (shared managed capacity), Provisioned Throughput Units (reserved capacity), batch processing, and bring-your-own model deployments. The platform features a unified control plane for models, agents, tools, and observability. Its Agent Service enables building and deploying AI agents with built-in tracing, monitoring, and governance. Evaluation and monitoring tools assess model performance, safety, and groundedness. Foundry supports seamless upgrades from Azure OpenAI with non-destructive migration, maintaining existing deployments while unlocking multi-provider model access and advanced platform capabilities.

Microsoft Foundry is a unified Azure platform-as-a-service offering for enterprise AI operations, model builders, and application development. It provides access to over 1,900 models from Microsoft, OpenAI, Anthropic, Mistral, xAI, Meta, DeepSeek, Hugging Face, and more. Foundry unifies agents, models, and tools under a single management grouping with built-in enterprise-readiness capabilities including tracing, monitoring, evaluations, and customizable enterprise setup configurations.

Pricing on Microsoft Foundry

Capabilities

About DBRX Instruct

DBRX Instruct, developed by Databricks, is a cutting-edge large language model designed for various natural language processing tasks. It excels in text summarization, question answering, information extraction, and code generation, utilizing a fine-grained mixture-of-experts architecture with 132 billion parameters. With advanced features like rotary position encodings, gated linear units, and grouped query attention, it performs exceptionally across multiple benchmarks, even outperforming some closed-source models. Trained on a vast 12 trillion token dataset, it supports contexts up to 32,000 tokens. Although primarily effective in English, its multilingual strength isn't fully explored. Users should be cautious as it may generate inaccurate or biased outputs.