Quick Start
- 1
- 2Use the Microsoft Foundry SDK or REST API to call
smaug-72b— 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 | $1.00 |
| Output tokens | $2.00 |
Capabilities
No model capability flags are currently sourced.
About Smaug 72B
Smaug 72B is a large language model (LLM) developed by Abacus AI, distinguished as the first open-source model to exceed an average score of 80% on the Hugging Face Open LLM Leaderboard. It excels in various tasks, outperforming even some proprietary models like GPT-3.5 in specific benchmarks. The model is based on the Qwen-72B and fine-tuned using a novel DPO-Positive (DPOP) technique, leveraging datasets such as ARC, HellaSwag, and MetaMath. Its capabilities include question answering, text translation, and poem generation, with notable performance in reasoning and math tasks. Despite its strengths, Smaug 72B faces limitations such as dataset contamination and challenges in complex contextual understanding. Its open-source nature allows for community-based enhancements and it supports a 32k context length for processing longer inputs.