Using Falcon 40B on Microsoft Foundry
Implementation guide · Falcon · Technology Innovation Institute (TII)
Quick Start
- 1
- 2Use the Microsoft Foundry SDK or REST API to call
falcon-40b— see the documentation for request format. - 3
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
| Type | Price (per 1M) |
|---|---|
| Input tokens | $1.54 |
| Output tokens | $1.77 |
Capabilities
About Falcon 40B
Falcon 40B is a leading open-source large language model developed by the Technology Innovation Institute in Abu Dhabi, featuring a causal decoder-only architecture with 40 billion parameters. It stands out with its use of rotary positional embeddings, multi-query attention, and FlashAttention, enhancing its contextual understanding and processing efficiency. Trained on 1 trillion tokens using the enriched RefinedWeb dataset, Falcon 40B excels in various natural language processing tasks, ranging from text generation to language translation and question answering. It supports multiple languages and is open under the Apache 2.0 license, promoting both research and commercial use. The model efficiently utilizes standard hardware, requiring around 85-100 GB of memory for inference, setting a benchmark for performance and scalability in its category.