Using DeciCoder 1B on Microsoft Foundry
Implementation guide · DeciCoder · Deci AI
Quick Start
- 1
- 2Use the Microsoft Foundry SDK or REST API to call
decicoder-1b— 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 | $0.07 |
| Output tokens | $0.07 |
Capabilities
No model capability flags are currently sourced.
About DeciCoder 1B
DeciCoder 1B is a 1 billion-parameter, open-source large language model focused on code generation 12. It excels at efficient and accurate code completion for Python, Java, and JavaScript using a unique Grouped Query Attention architecture with a 2048-token context window 13. Trained on a substantial dataset with a Fill-in-the-Middle objective, it offers impressive throughput, especially when used with Deci's Infery LLM inference engine 79. Although capable of single or multi-line code completion, it may produce suboptimal results as it's not an instruction-following model. Performance is benchmarked on HumanEval, showing variable accuracy across languages, and it is available under the Apache 2.0 license 25.