Using Falcon 7B on Microsoft Foundry
Implementation guide · Falcon · Technology Innovation Institute (TII)
Quick Start
- 1
- 2Use the Microsoft Foundry SDK or REST API to call
falcon-7b— 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 | $0.52 |
| Output tokens | $0.67 |
Capabilities
About Falcon 7B
Falcon-7B, developed by the Technology Innovation Institute, is a cutting-edge large language model boasting a decoder-only architecture with 7 billion parameters. It's trained on 1,500 billion tokens from the curated web dataset, RefinedWeb, enhancing its performance in language tasks. The model is equipped with advanced features like FlashAttention and multiquery attention, optimizing speed and memory usage. With 32 layers and rotary positional embeddings, it manages a sequence length of up to 2048 tokens efficiently. Renowned for tasks such as text generation, summarization, translation, and conversational AI, Falcon-7B is open-source under Apache 2.0, suitable even for consumer hardware, needing at least 16GB of memory for inference 236.