Quick Start
- 1
- 2Use the Microsoft Foundry SDK or REST API to call
orca-2-13b— 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 | $0.81 |
| Output tokens | $0.94 |
Capabilities
No model capability flags are currently sourced.
About Orca 2 13B
Orca 2 13B, developed by Microsoft, is a large language model designed primarily for research purposes. It is a fine-tuned version of the LLaMA-2 base model, focusing on enhanced reasoning capabilities in smaller language models. This is achieved through training on a synthetic dataset specifically created to improve reasoning skills. Orca 2 13B excels in tasks such as reading comprehension, math problem-solving, and text summarization. However, it is not optimized for chat applications and requires fine-tuning for specific tasks. The model demonstrates strong performance in zero-shot settings but shares common LLM limitations, such as potential biases and a lack of contextual understanding. It is primarily suitable for research and not recommended for deployment without further evaluation. 124 3 7 8.