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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThere is no single best place to run AI agents. Choose the platform that fits your existing cloud, agent framework, workload, security requirements, and operating capacity. For teams already invested in Microsoft, Microsoft Foundry Agent Service is a managed option; AWS teams can investigate Amazon Bedrock AgentCore Runtime; and Google Cloud teams can consider Google Cloud Agent Runtime for its documented framework paths. These are vendor-documented capabilities, not independent performance rankings.
Start with the hosting decision, not the framework
An agent framework describes how you build an agent; a hosting platform determines where its code runs and who operates the surrounding infrastructure. A protocol, such as a conversational request format, is another separate choice. Decide how you will host the workload before treating any one framework or protocol as the answer.
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Managed hosting shifts infrastructure tasks such as deployment, scaling, and session management to a platform provider. Self-hosting gives your team more direct control, but also makes it responsible for operating the application’s routes, identity and authorization, request policies, storage, deployment, scaling, and client libraries. Microsoft’s hosting comparison describes this division for Foundry Hosted Agents and self-hosted deployments: Microsoft Agent Framework hosting comparison.
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Neither option transfers responsibility for the agent’s behavior. Your team still needs to set permissions, control data flows, evaluate quality, and design appropriate safety measures. Microsoft explicitly assigns application quality, reliability, security, trustworthiness, and safety mitigations to the builder in its Foundry Agent Service overview and hosted-agent documentation.
#1 Best Overall
Compare the documented cloud options
| Platform | Documented hosting approach | Framework and model information | Consider it when |
|---|---|---|---|
| Microsoft Foundry Agent Service | Prompt agents run without user-maintained application runtime code or containers; code-based hosted agents use a managed endpoint, scaling, identity, session-state persistence, and observability. | Hosted agents can use Agent Framework, LangGraph, OpenAI Agents SDK, Anthropic Agent SDK, GitHub Copilot SDK, or custom code. | Your team uses Azure, Microsoft identity, or Foundry tools, or wants a managed code-hosting route with framework choice. |
| Amazon Bedrock AgentCore Runtime | Offers serverless microVM sessions that scale on demand and Instances using AWS-managed EC2 infrastructure in the customer’s account. | Documentation names LangGraph, Strands, and CrewAI, and models from Amazon Bedrock, Anthropic, Google, and OpenAI. | Your team already runs on AWS or needs the documented framework and model flexibility alongside AWS runtime controls. |
| Google Cloud Agent Runtime | The quickstart demonstrates deployment and testing through Google Cloud; it requires a project, location, SDK setup, and IAM permissions. | The quickstart covers LangGraph, LangChain, AG2, and LlamaIndex. The LlamaIndex integration is marked Preview and subject to Pre-GA terms on the page reviewed. | Your team uses Google Cloud and its agent fits a documented framework path, after checking current regional support and feature stage. |
These descriptions come from vendor documentation and do not establish that one service is faster, safer, or less expensive than another. Check current availability, service limits, controls, and costs for your region and intended workload before committing.
How Microsoft Foundry handles agent workloads
Choose a prompt agent or hosted code
Foundry’s overview describes prompt agents configured with instructions, a model, and tools; the platform runs them without application runtime code or containers maintained by the user. For code-based agents, the hosted route accepts supported frameworks or custom code. The hosted offering documents a managed endpoint, automatic scaling, dedicated Microsoft Entra identity, session-level state persistence, and observability.
Match the request style to the job
The hosted-agent guide distinguishes Responses, which manages conversation history for conversational flows, from Invocations, which accepts a custom payload contract for work such as webhooks or structured, non-conversational tasks. It also describes containerized applications on Microsoft-managed infrastructure, with a per-session VM-isolated sandbox and persisted filesystem state. Confirm lifecycle and limit details for your specific use case in the hosted-agent guide.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →The overview also describes voice-based prompt agents. A voice requirement may therefore change which Foundry path you evaluate, but it does not remove the need to assess regional availability, data handling, and application safeguards.
Rank #3
What Amazon Bedrock AgentCore Runtime provides
AWS describes AgentCore Runtime as managed hosting for agents and tools. Its two documented compute choices have different operating shapes: serverless microVM sessions scale on demand, while Instances use AWS-managed EC2 infrastructure in the customer’s account. Compare them against expected concurrency, session duration, idle time, and operational needs rather than assuming one is automatically cheaper.
The runtime documentation also describes agent identities, integration with corporate identity providers, outbound authentication flows, and specialized tracing of agent reasoning steps, tool calls, and model interactions. AWS says the runtime supports frameworks including LangGraph, Strands, and CrewAI, and models from Amazon Bedrock, Anthropic, Google, and OpenAI. Check the current AgentCore Runtime documentation for version, limits, and region availability; the page indicates a newer platform version is available, so follow version-specific guidance when deploying.
Rank #4
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What Google Cloud Agent Runtime documents
Google’s quickstart shows how to deploy and test agents with LangGraph, LangChain, AG2, or LlamaIndex. It requires a Google Cloud project, a location, SDK setup, and IAM permissions. The quickstart marks the LlamaIndex integration Preview and subject to Pre-GA terms; treat that status as a qualification, not as a general statement about every framework path.
The quickstart establishes a deployment workflow and examples, not a cross-cloud feature or price advantage. Review the current Google Cloud Agent Runtime quickstart and verify the supported region, framework status, runtime limits, and pricing for your intended deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose by workload and team capability
- Conversational agent with managed history: Evaluate Foundry’s Responses path if its cloud, identity, and tool integrations suit your team.
- Webhook or structured, non-conversational work: Foundry’s Invocations path is documented for custom payloads. Also compare how your chosen runtime handles request contracts and session lifecycles.
- Existing AWS deployment or broad framework and model choices: Investigate AgentCore Runtime, checking the compute type, current platform version, and regional support.
- Existing Google Cloud deployment: Evaluate Agent Runtime against its documented framework path and prerequisites; check feature stage especially if using LlamaIndex.
- Need application-level infrastructure control: Self-hosting may fit, but only if your team can own routing, identity, authorization, storage, deployment, scaling, and ongoing operations.
- Need persistent state or long-running behavior: Validate exactly what persists, for how long, and across which lifecycle events. Foundry documents session-level state and persisted filesystem state, but you should confirm current limits and lifecycle semantics for your workload.
Estimate cost from the workload, not the feature list
The available documentation does not support a fair numeric price ranking. Foundry’s overview separates per-call inference and tool usage for prompt agents from container compute for hosted agents. AgentCore offers serverless sessions and managed instances, which have different compute characteristics. Those facts are not enough to identify a cheapest platform without assumptions about region, model, token volume, tool calls, session duration, concurrency, storage, and network traffic.
Build an estimate for each candidate using the same workload assumptions:
- Model and region, plus expected input and output volume.
- Tool calls per task and any separately charged services they invoke.
- Requests, concurrent sessions, session duration, and idle time.
- Compute, persistent storage, and network traffic.
- Logging, tracing, and other operational services your design requires.
Use current regional pricing for each product and include a realistic traffic pattern. Do not infer that an on-demand or serverless label guarantees a lower bill; the total depends on the workload and the service’s pricing model.
Quick Recap
Run a focused pilot before moving production traffic
- Confirm prerequisites. Check project or subscription setup, region support, IAM or identity requirements, framework compatibility, and the current runtime version.
- Use representative tasks. Test conversational turns, tool calls, failures, and any webhook, batch, voice, or long-running flow the application actually needs.
- Test permissions and data boundaries. Verify which identity the agent uses, what each tool can access, where inputs and outputs go, and whether outbound authentication behaves as intended.
- Exercise recovery. Test tool errors, timeouts, interrupted sessions, retries, and restarts. Check which state survives each event and whether the agent can safely resume.
- Inspect observability. Confirm that traces and logs help operators diagnose model interactions and tool calls without exposing data that should remain protected.
- Measure cost under expected use. Record model and tool usage, runtime duration, concurrency, storage, and networking for the same task mix across candidates, then apply current regional prices.
- Review operational ownership. Identify who will deploy updates, handle incidents, manage permissions, and roll back changes. Include those tasks in the platform decision.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
