Using Llama 2 70B Chat on Microsoft Foundry
Implementation guide · Llama 2 · AI at Meta
Quick Start
- 1
- 2Use the Microsoft Foundry SDK or REST API to call
llama2-70b-chat— 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 Llama 2 70B Chat
Llama 2 70B Chat is a large-scale language model with 70 billion parameters, designed for conversational AI applications. Released on July 18, 2023, it's part of Meta's Llama 2 family, featuring advanced transformer architecture optimized through supervised fine-tuning and reinforcement learning with human feedback. The model excels in generating human-like responses, outperforming many open-source alternatives and rivaling closed-source models like ChatGPT. Trained on 2 trillion tokens from diverse public sources, it's suitable for commercial and research applications in English, particularly for assistant-like functionalities. The model is available on Hugging Face for further exploration and implementation .