AutoGen and Microsoft Agent Framework Pricing

Framework and Licensing Costs

The framework itself is completely free. AutoGen, Semantic Kernel, and the Microsoft Agent Framework are all open-source under the MIT license, which allows unrestricted commercial use, modification, and distribution. There are no per-user fees, per-agent fees, or runtime licensing costs for the framework code. Organizations can deploy as many agents, conversations, and instances as they need without paying anything to Microsoft for the framework software.

This changes if organizations choose to use the managed Agent 365 service, which bundles agent capabilities into the Microsoft 365 ecosystem. At general availability, Agent 365 licensing is expected to cost $15 per user per month as a standalone add-on, or it is included in the Microsoft 365 E7 suite at $99 per user per month. These costs cover managed hosting, pre-built agent templates, and integration with Microsoft 365 applications like Teams, Outlook, and SharePoint.

Model API Costs

Model API costs are typically the largest expense in a multi-agent system. Every time an agent generates a response, it consumes input tokens (the conversation history, system message, and available tool descriptions) and output tokens (the agent's response). The cost per token varies significantly between model providers and model tiers.

OpenAI and Azure OpenAI pricing for GPT-4o is approximately $2.50 per million input tokens and $10 per million output tokens as of mid-2026. GPT-4.1, with its larger context window and improved reasoning, costs roughly $2 per million input tokens and $8 per million output tokens. Smaller models like GPT-4o-mini offer dramatically lower costs at around $0.15 per million input tokens and $0.60 per million output tokens, making them practical for agents that handle routine tasks.

Multi-agent conversations amplify token costs because each agent message adds to the shared context that all subsequent agents must process. In a five-agent group chat, the twentieth message includes all nineteen previous messages as context. A complex task that generates 50 messages across five agents can consume 500,000 or more total tokens, costing $1 to $5 per task with GPT-4o. At scale, this adds up: processing 1,000 such tasks daily would cost $1,000 to $5,000 per month in model API fees alone.

Cost Optimization Strategies

Using smaller models for routine agents is the most effective cost reduction strategy. In a system with five agents, only one or two may need frontier model capabilities. The others can use GPT-4o-mini or even open-source models at a fraction of the cost. AutoGen's multi-model support makes this straightforward: each agent can be configured with a different model based on the complexity of its tasks.

Conversation summarization reduces context length by condensing older messages into compact summaries. Instead of sending the full 50-message history to each agent, a summarization step might compress the first 40 messages into a two-paragraph summary, dramatically reducing the input token count for subsequent turns. The Microsoft Agent Framework supports configurable summarization strategies that balance cost savings against information preservation.

Caching eliminates redundant model calls when agents encounter similar situations. If an agent has already generated a response for a particular type of request, the cached response can be returned without making a new API call. AutoGen includes built-in caching with configurable cache lifetimes and invalidation strategies. For high-volume systems with repetitive tasks, caching can reduce model API costs by 30 to 50 percent.

Setting strict turn limits prevents runaway costs from conversations that fail to converge. A maximum of 10 to 20 turns is usually sufficient for well-designed agent systems. Beyond that limit, the conversation is likely stuck in a loop or working on a task that requires human intervention. Timeout mechanisms provide an additional safety net, terminating conversations that exceed a configurable duration regardless of turn count.

Infrastructure Costs

The compute infrastructure for hosting the agent runtime is typically modest. The agent framework itself requires minimal CPU and memory, with costs driven primarily by the code execution environment and concurrent conversation capacity. A small virtual machine or container instance can handle dozens of concurrent conversations, with costs starting at $20 to $50 per month for basic deployments.

Docker-based code execution adds container orchestration overhead. Each code execution spins up a container, runs the code, and tears the container down. For systems that execute code frequently, keeping warm containers ready reduces latency but increases costs. Kubernetes clusters provide efficient container management at scale, with costs depending on cluster size and utilization.

Azure AI Foundry's managed agent hosting abstracts infrastructure costs into a consumption-based model. Organizations pay for the compute and model tokens consumed without managing virtual machines, containers, or orchestration. This approach simplifies budgeting and eliminates the engineering cost of infrastructure management, though it may be more expensive per unit than self-managed infrastructure at high volumes.

Total Cost Examples

A small development team using AutoGen for internal code review automation might spend $100 to $300 per month. The breakdown would be approximately $50 to $200 for model API calls (a few hundred reviews per month using GPT-4o-mini), $20 to $50 for a basic VM or container to host the agent runtime, and $30 to $50 for Docker or container infrastructure for code execution.

A mid-size company deploying customer service agents might spend $2,000 to $8,000 per month. Model API costs dominate at $1,500 to $6,000 for several thousand customer interactions per month, with infrastructure costs adding $500 to $2,000 for higher-availability hosting with redundancy and monitoring.

An enterprise deploying agent systems across multiple departments with Azure integration might spend $10,000 to $50,000 per month. This includes model API costs for high-volume usage, Azure AI Foundry hosting, Azure AI Search for RAG, and potentially Agent 365 licensing for user-facing agent experiences integrated with Microsoft 365.