Fireworks models on Microsoft Foundry

Learn how to enable, deploy, and use Fireworks models in Microsoft Foundry, including catalog models, custom model import (BYOM), data privacy, and frequently asked questions.

Fireworks models on Microsoft Foundry

Through integration with Fireworks AI, Microsoft Foundry customers can:

• Experiment with the latest open-source models often before they’re available directly from Azure.
• Import and deploy custom model weights (bring your own model, or BYOM) onto Fireworks’ on-demand GPU-backed infrastructure. For more information, see Import custom models on Microsoft Foundry with Fireworks.
• Scale up using Provisioned throughput.

All of these capabilities are available directly within your Foundry project, with Azure governance, access controls, and project management built in.

Prerequisites

• An Azure subscription. If you don’t have one, create a free account.
• A Foundry resource with a Foundry project.
• To deploy models, you need the Foundry Owner role on the Foundry project. For more information, see Azure built-in roles.

The Foundry RBAC roles were recently renamed. Foundry User, Foundry Owner, Foundry Account Owner, and Foundry Project Manager were previously named Azure AI User, Azure AI Owner, Azure AI Account Owner, and Azure AI Project Manager. You might still see the previous names in some places while the rename rolls out. The role IDs and core permissions are unchanged by the rename.

Region availability

Data Zone Standard deployments of models via Fireworks on Foundry are available in the following Azure regions:

• East US (eastus)
• East US 2 (eastus2)
• Central US (centralus)
• North Central US (northcentralus)
• West US (westus)
• West US 3 (westus3)

Global provisioned throughput deployments of base and custom models are available in all global Azure regions except for Azure Government cloud environments.

Deploy Fireworks models from the Foundry portal

Deploy Fireworks models from the Foundry model catalog. Complete these steps to get a live endpoint for chat completions. Browse available models in the Available catalog models section, or import your own custom model.

1. From the portal homepage, select Discover in the upper-right navigation.

2. In the left pane, select Models to open the Model catalog.

3. Select your desired Fireworks model to view its details on the model page:

1. On the model page, select Deploy. For more information on deployment options, see Deploy Foundry Models in the portal.

2. In the deployment window, configure the following settings:

   • Deployment name: Keep the default name or enter a custom name to identify the deployment.
   • Deployment type: Select Data Zone Standard or Global provisioned throughput. For more information, see Deployment types.
   • Model version settings: Select the model version for the deployment.
   • Tokens per Minute Rate Limit: Set a custom tokens-per-minute limit to manage costs and control usage. The default value is based on the model’s typical performance and cost profile.
   • Guardrails: Select DefaultV2 or Default guardrail configuration. Models use the Microsoft.DefaultV2 guardrail unless a different one is specified. For more information, see Use guardrails to set boundaries on model outputs.

3. Select Deploy. The deployment process can take up to 30 minutes.

4. After deployment completes, use the provided endpoint and key to send inference requests to the model. To quickly test the deployment, use the Playground in your Foundry project.

To verify the deployment, navigate to your project’s Deployments page and confirm the deployment Status shows Succeeded.

Available catalog models

The following Fireworks models are available in the Foundry model catalog:

Model provider	Model name	Model ID	Type	Supported offers	Description
DeepSeek	DeepSeek v3.1	FW-DeepSeek-v3.1	Chat completions	PTU	General-purpose open-weight model for chat and reasoning tasks.
DeepSeek	DeepSeek V4 Pro	FW-DeepSeek-V4-Pro	Chat completions	Per-Token and PTU	Flagship MoE model for frontier reasoning, coding, and long-context tasks.
Google	Gemma 4 26B A4B IT	FW-Gemma-4-26B-A4B-IT	Chat completions	PTU	Instruction-tuned multimodal sparse model for efficient vision and language tasks.
Google	Gemma 4 31B IT	FW-Gemma-4-31B-IT	Chat completions	PTU	Instruction-tuned multimodal dense model for vision, chat, and reasoning tasks.
Meta	Llama 3.1 8B Instruct	FW-Llama-3.1-8B-Instruct	Chat completions	PTU	Compact instruction-tuned model for cost-efficient chat workloads.
Mistral AI	Ministral 3 3B Instruct 2512	FW-Ministral-3-3B-Instruct-2512	Chat completions	PTU	Small efficient model for lightweight chat and instruction-following tasks.
Moonshot AI	Kimi K2 Instruct 0905	FW-Kimi-K2-Instruct-0905	Chat completions	PTU	Instruction-tuned model for chat workloads.
Moonshot AI	Kimi K2 Thinking	FW-Kimi-K2-Thinking	Chat completions	PTU	Reasoning-focused model for multi-step problem solving.
Moonshot AI	Kimi K2.6	FW-Kimi-K2.6	Chat completions	Per-Token and PTU	Native multimodal agentic model for long-horizon coding and task orchestration.
Qwen	Qwen 3.5 9B	FW-Qwen3.5-9B	Chat completions	PTU	Compact model for efficient chat and reasoning.
Qwen	Qwen 3.5 35B A3B	FW-Qwen3.5-35B-A3B	Chat completions	PTU	Sparse mixture-of-experts model for efficient general-purpose tasks.
Qwen	Qwen 3.5 112B A10B	FW-Qwen3.5-112B-A10B	Chat completions	PTU	Large sparse model for complex reasoning and generation tasks.
Qwen	Qwen 3.5 397B	FW-Qwen3.5-397B	Chat completions	PTU	Large-scale model for advanced reasoning and generation.
Zhipu AI	GLM-4.7	FW-GLM-4.7	Chat completions	PTU	Bilingual model for chat and reasoning tasks.
Zhipu AI	GLM-5.1	FW-GLM-5.1	Chat completions	PTU	Advanced bilingual model for chat, reasoning, and code.

All catalog models support the OpenAI/v1 API for Chat Completions API and the Foundry SDK and endpoint for accessing the Responses API.

Fireworks models on Standard (Per-Token) inference offerings are subject to a 15-day notice period prior to model retirement. Plan your deployments accordingly and monitor notifications for upcoming retirement dates.

Custom models (bring your own model)

In addition to the catalog models, Fireworks on Foundry supports importing and deploying your own custom model weights. This BYOM capability lets you run proprietary or fine-tuned open-weight models within the Foundry ecosystem, with inference provided by the optimized Fireworks cloud.

Supported model architectures

Custom models must be based on one of the following supported architectures:

• Kimi (K2, K2.5, K2.6)
• GLM (4.7, 4.8)
• OpenAI (gpt-oss-120b)
• Qwen (qwen3.5-9B, qwen3.5-35B-A3B, qwen3.5-112B-A10B, qwen3.5-397B)

Limitations

• Full-weight models only. LoRA and adapter-based models aren’t supported.
• CLI-first workflow. The import process uses the Azure Developer CLI (azd). The Foundry portal supports registering, viewing, and deploying models after upload.
• Fireworks Agents and Agent Builder workflows aren’t currently supported.

For step-by-step instructions, see Import custom models into Foundry.

Data privacy

When you use Fireworks on Foundry, data is shared between Microsoft and Fireworks AI, and different compliance and data handling rules will apply. See below for details. Customers are responsible for evaluating whether data sharing between Microsoft and Fireworks is appropriate for their organizations compliance requirements.

• Fireworks on Foundry is currently excluded from EU Data Boundary commitments.

• FedRAMP isn’t achieved for Fireworks on Foundry. If your organization requires FedRAMP, before use, consult with your Authorization Official to determine if use of Fireworks on Foundry is allowed.

• Payment Card Industry (PCI) Data Security Standard (DSS) isn’t applicable to Fireworks on Foundry. You shouldn’t use Fireworks on Foundry to store, process, or transmit payment and cardholder data.

Transparency note

Fireworks on Foundry allows customers to deploy and operate third-party and open-weight AI models using Microsoft Foundry platform services.

• Microsoft doesn’t develop, train, fine-tune, or evaluate the safety, security, or Responsible AI characteristics of models deployed through Fireworks on Foundry.
• Microsoft makes no representations regarding the behavior, performance, or risk profile of these models.
• Customers are solely responsible for assessing the suitability of any model for their intended use, including performing any required safety, compliance, and Responsible AI evaluations, before deploying models in production or customer-facing applications.

Foundry provides the tools and best practices for performing your own risk and safety evaluations of models.

Frequently asked questions

Is Fireworks on Foundry available in Azure for US Government?

No, currently the Fireworks on Foundry service isn’t available for Azure Government cloud users.

How can I get quota for Fireworks model deployments?

Use the quota request form to request added quota for Fireworks on Foundry.

I have a Fireworks AI account. Can I use my existing Fireworks deployments?

No, you need to create new deployments in Foundry. If you’d like to shift consumption to Azure, contact your Fireworks account team to assist.

Can I deploy LoRA or adapter-based models?

No, Fireworks on Foundry supports full-weight custom models only. LoRA and adapter-based models aren’t supported at this time.

How do I import and deploy a custom model?

Custom model import uses a CLI-first workflow with the Azure Developer CLI. For step-by-step instructions, see Import custom models into Foundry.

How is Fireworks on Foundry billed?

Fireworks models deployed through Foundry support both pay-per-token and provisioned throughput offers.

How do I disable Fireworks in my Foundry project?

To stop using Fireworks models, delete the Fireworks model deployments from your Foundry project.

How do I use the Responses API?

The Responses API is supported via the Foundry Projects API and SDK. Make sure to point your client to your project’s API endpoint or use the Foundry SDK.

Troubleshoot Fireworks on Foundry

Use the following guidance to resolve common issues with Fireworks on Foundry.

Issue	Resolution
Fireworks models don’t appear in the model catalog	Verify you’re working in a supported region. Check that the model catalog filters are set to show Fireworks models.
Deployment fails with a quota error	Use the quota request form to request added capacity for Fireworks on Foundry.
”Forbidden” or access denied during deployment	Verify that your identity has the Azure AI Developer role or higher on the Foundry project. Subscription-level roles alone aren’t sufficient for deployment.
Model endpoint returns errors after deployment	Confirm the deployment status shows Succeeded on the project’s Deployments page. Verify you’re using the correct Target URI and Key from the deployment details.

For other queries, see the frequently asked questions section.

Related content

• Import custom models into Foundry
• Deploy Foundry Models in the portal
• Foundry Models from partners and community
• Foundry model catalog overview
• Deployment types
• Provisioned throughput concepts
• Azure built-in roles
• Fireworks AI Trust Center