Using Llama 3.1 70B Instruct on Microsoft Foundry
Implementation guide · Llama 3.1 · AI at Meta
Quick Start
- 1
- 2Use the Microsoft Foundry SDK or REST API to call
llama3.1-70b-instruct— 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 | $2.68 |
| Output tokens | $3.54 |
Capabilities
About Llama 3.1 70B Instruct
The Llama 3.1 70B Instruct model is a cutting-edge large language model with 70 billion parameters, designed for instruction-following tasks. It features multilingual capabilities, supporting languages like English, German, French, and others. Fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), it excels in understanding and responding to user instructions. The model can handle a context length of up to 128k tokens, making it suitable for complex dialogue systems and applications requiring detailed responses. It outperforms many existing open-source and proprietary models on various industry benchmarks, making it ideal for conversational AI, content generation, and data synthesis tasks. For more details, visit the Hugging Face page [1].