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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →For most production teams, the leading managed choices in 2026 are Amazon Bedrock AgentCore Runtime, Microsoft Foundry Hosted Agents, and Google Vertex AI Agent Engine. Choose among them by cloud alignment, framework and model portability, identity controls, private networking, language support, protocol needs, session behavior, and how you want runtime costs calculated. There is no credible universal winner: the right platform is the one that fits your existing controls and agent architecture.
Quick answer: which platform fits which team?
| Platform | Strongest fit | Key capabilities documented by the provider | Main constraint to verify |
|---|---|---|---|
| Amazon Bedrock AgentCore Runtime | AWS teams that want a serverless, framework-agnostic runtime and broad model choice | Purpose-built agent and tool hosting; support for Bedrock, Anthropic Claude, Google Gemini and OpenAI models; integrations starting with LangChain, OpenAI Agents SDK, Claude Agent SDK, Strands SDK and custom frameworks | Regional availability, adapter coverage for your framework, IAM and network design, session behavior, observability and current runtime/model pricing |
| Microsoft Foundry Hosted Agents | Azure and Microsoft 365 organizations standardizing on Entra identity and managed containers | Microsoft-managed container hosting, scaling, session-state persistence, security and lifecycle management; dedicated Entra identity; OpenAI-compatible Responses and custom Invocations protocols | Azure region and quota availability, container workflow, protocol selection, session storage, model deployment and consumption billing |
| Google Vertex AI Agent Engine | Google Cloud and Python teams using ADK, LangChain or LangGraph | Managed deployment, management and scaling; full integration with ADK, LangChain and LangGraph; managed templates for AG2 and LlamaIndex; custom templates for CrewAI and other frameworks; Private Service Connect support | Python-only deployment, supported regions, dependency packaging, Cloud Storage staging for object deployments, IAM and private-connectivity setup |
These are managed runtimes, not interchangeable model APIs. They host the code that plans, calls tools, maintains sessions and applies policy. You still pay separately for model inference and, depending on the design, storage, network traffic and external services.
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What “AI agent hosting” includes
An agent host supplies the production environment around an agent loop. That normally includes a deployable runtime, identity, scaling, networking, logs and a way to preserve or retrieve conversation state. The host does not automatically make an agent reliable or safe: you remain responsible for prompts, tools, permissions, data access, evaluation and model selection.
Managed versus self-managed operation
- Managed hosting: the provider operates the underlying infrastructure and exposes deployment and runtime controls. This reduces server maintenance and gives you provider-integrated identity, monitoring and networking.
- Self-managed hosting: you run the agent in your own containers, virtual machines or Kubernetes environment. You gain control over the stack and portability, but you must operate scaling, patching, availability, secrets, session stores and observability.
The three options in this guide are managed services. Compare them with self-managed infrastructure only after listing the operational work you would otherwise have to build.
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What remains outside the runtime
- Model charges and model quotas.
- Tool services, databases, vector stores and file storage.
- Network egress or private-connectivity charges where applicable.
- Application-level authorization, approval workflows and data-retention policy.
- Quality, safety and latency testing for your own prompts, tools and models.
Amazon Bedrock AgentCore Runtime
AWS describes Amazon Bedrock AgentCore Runtime as a secure, serverless, purpose-built environment for deploying and running AI agents or tools. AWS also describes it as framework agnostic and says it can work with models from Amazon Bedrock, Anthropic, Google and OpenAI. That combination makes it the most natural first evaluation for teams already operating in AWS or those deliberately avoiding a single agent framework.
Why teams choose it
- Framework flexibility: AWS lists LangChain, OpenAI Agents SDK, Claude Agent SDK, Strands SDK and custom frameworks as starting points.
- Model choice: the documented provider mix includes Bedrock models as well as models from Anthropic, Google and OpenAI.
- Serverless operations: the runtime is designed to remove routine host management while providing a production endpoint for an agent or tool.
- AWS integration: teams can evaluate it alongside their existing IAM, network, logging and data services rather than introducing another cloud control plane.
Best fit
Use AgentCore Runtime as the leading candidate when your data, permissions and operational tooling already live in AWS, or when you need to keep the agent layer relatively portable across the frameworks and model providers AWS documents.
Checks before committing
- Confirm that your target region offers the runtime and the models you intend to call.
- Map every tool and data access path to an IAM role; do not treat the agent process as a blanket database credential.
- Verify how runtime memory and session state behave across invocations, restarts and concurrent users.
- Check what logs, traces and evaluation hooks are available for your selected framework adapter.
- Price the runtime, model inference, storage and network egress together; the runtime itself is not a complete application cost.
Deployment approach
- Choose the framework adapter or custom integration that matches your agent code.
- Package the agent and its tools with explicit dependency and secret handling.
- Create the least-privilege IAM roles for model calls, tools, data stores and observability.
- Configure network access and test private resources before exposing an endpoint.
- Run session, failure-retry and tool-timeout tests at the concurrency you expect in production.
- Set budgets and alerts for both model usage and runtime activity before launch.
Microsoft Foundry Hosted Agents
Microsoft documents Hosted Agents in Microsoft Foundry Agent Service as containerized agent applications deployed to Microsoft-managed infrastructure. The service handles scaling, session-state persistence, security and lifecycle management, and gives each deployed agent a dedicated Microsoft Entra identity.
Why teams choose it
- Container model: you bring an agent application in a container while Microsoft operates the hosting layer.
- Identity separation: a dedicated Entra identity gives the deployed agent a distinct principal for authorization and auditing.
- State and lifecycle services: Microsoft documents persistence for session state and managed lifecycle operations, reducing the amount of platform code you need to build.
- Protocol options: the service exposes OpenAI-compatible Responses and custom Invocations protocols, allowing different client and orchestration patterns.
Best fit
Hosted Agents are a strong candidate for Azure or Microsoft 365 organizations that already govern workloads with Entra, use Microsoft security controls and want to deploy a container without operating the underlying servers.
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Protocol choice
Use the OpenAI-compatible Responses protocol when your clients and tooling already speak that interface. Use custom Invocations when your application needs a provider-specific contract or richer control than the compatibility layer exposes. Decide this before client integration, because changing protocols later can affect authentication, streaming, error handling and session semantics.
Checks before committing
- Check the Azure regions in which Hosted Agents and your required model deployments are available.
- Verify quotas for agent instances, CPU, memory, concurrent sessions and model requests.
- Document the container build and release path, including image provenance, dependency scanning and rollback.
- Confirm where session state is stored, how long it is retained and how it is isolated between users or tenants.
- Test the Entra identity against every downstream resource instead of granting permissions to the deployment pipeline.
- Review consumption billing: Microsoft documents runtime billing based on CPU and memory resources consumed during active sessions.
Deployment approach
- Build a production container with a health endpoint, deterministic dependencies and no embedded secrets.
- Assign the hosted agent its dedicated Entra identity and least-privilege roles.
- Select Responses or Invocations and implement the corresponding client contract.
- Configure session persistence and define tenant, user and retention boundaries.
- Deploy to a region with sufficient quota, then test scale-out, restart recovery and long-running sessions.
- Monitor CPU, memory, active-session duration, model calls and downstream failures to explain consumption charges.
Google Vertex AI Agent Engine
Google describes Vertex AI Agent Engine as a set of services for deploying, managing and scaling AI agents in production. Google documents full integration with the Agent Development Kit (ADK), LangChain and LangGraph, managed templates for AG2 and LlamaIndex, and custom-template support for CrewAI and custom frameworks.
Why teams choose it
- Google-native agent tooling: ADK has a first-class path, while LangChain and LangGraph are fully integrated according to Google’s documentation.
- Framework range: managed templates cover AG2 and LlamaIndex, with custom templates for CrewAI and other frameworks.
- Production management: the service is designed to deploy, manage and scale agents rather than merely run a development process.
- Private connectivity: Google documents Private Service Connect for traffic to private VPC, on-premises and multicloud services.
Important language constraint
Google’s current deployment guide states that Agent Engine deployment supports Python. If your production agent is written in another language, you need a separate service boundary or a different hosting choice; do not assume that a framework’s general language support also applies to Agent Engine deployment.
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Object and source deployments
The deployment guide supports deploying from an agent object or from source files. Object-based deployments require dependency packaging and Cloud Storage staging, so include a reproducible staging bucket, IAM permissions and cleanup policy in your release design.
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Best fit
Agent Engine is the strongest starting point for Google Cloud teams and Python developers using ADK, LangChain or LangGraph, particularly when private access to VPC, on-premises or multicloud services is a decisive requirement.
Checks before committing
- Confirm the target region supports Agent Engine, your selected model and any required templates.
- Test dependency packaging, native libraries and startup time in the same Python runtime used for deployment.
- Set up Cloud Storage staging with narrow IAM permissions for object deployments.
- Design service-account access separately for agent execution, deployment and data administration.
- Validate Private Service Connect routing and DNS with each private dependency before production traffic.
Deployment approach
- Choose ADK, LangChain, LangGraph, a managed template or a custom template.
- Make the agent’s Python dependencies reproducible and test them in a clean environment.
- Deploy from an agent object or source files, staging artifacts in Cloud Storage when required.
- Assign runtime and deployment identities with only the permissions each operation needs.
- Configure private connectivity for VPC, on-premises or multicloud tools and verify route behavior.
- Exercise session recovery, scaling, model timeouts and dependency failures before opening the endpoint.
Cross-platform comparison
| Decision axis | AgentCore Runtime | Foundry Hosted Agents | Vertex AI Agent Engine |
|---|---|---|---|
| Operating model | Managed, serverless agent/tool runtime | Containerized application on Microsoft-managed infrastructure | Managed production runtime for deployment, management and scaling |
| Framework portability | Framework agnostic; AWS lists LangChain, OpenAI Agents SDK, Claude Agent SDK, Strands SDK and custom frameworks | Container boundary allows your application framework; exact framework support depends on your image and protocol integration | Full integration for ADK, LangChain and LangGraph; managed templates for AG2 and LlamaIndex; custom templates for CrewAI and custom frameworks |
| Model portability | AWS documents Bedrock, Anthropic, Google and OpenAI model support | Depends on model deployments and the APIs your container calls; a complete provider matrix is not stated here | Depends on Vertex AI models and the integrations used by your agent; a complete provider matrix is not stated here |
| Deployment languages | Not stated as a single-language restriction in the cited product description | Container-based; language depends on the application image | Python deployment is documented |
| Identity | AWS IAM design is central to access control | Dedicated Entra identity per deployed agent | Google Cloud IAM and service-account design |
| Protocol compatibility | Depends on selected framework and adapter | OpenAI-compatible Responses and custom Invocations | Depends on the selected framework/template and endpoint integration |
| Session and state | Verify memory and session behavior for the chosen runtime integration | Microsoft documents session-state persistence | Verify session and state behavior for the selected Agent Engine configuration |
| Private networking | Validate AWS network controls and private-resource access for your design | Validate Azure networking and identity controls against organizational requirements | Private Service Connect is documented for private VPC, on-premises and multicloud services |
| Runtime pricing basis | Current runtime and model prices must be checked for your region and configuration | CPU and memory resources consumed during active sessions | Current runtime, model, storage and network prices must be checked for your region and configuration |
| Independent performance ranking | Not established by the available official product documentation; do not infer a latency or uptime winner. | ||
How to choose in a production review
Choose by cloud and data boundary first
Start with the cloud that already owns your identity, private data and audit controls. Moving an agent between clouds is possible, but moving data permissions, private routes, secrets and operational ownership is the expensive part.
Then test framework and language fit
List the exact framework, language, model SDKs and tool protocols in your repository. Google’s documented Python deployment constraint can eliminate Agent Engine for a non-Python runtime. AWS’s framework-agnostic positioning favors portability. Microsoft’s container boundary favors teams that can standardize their application image and protocol contract.
Match state requirements to failure behavior
Define whether state must survive a process restart, be shared across replicas, be isolated per tenant or be deleted on request. A platform’s phrase “session persistence” is not a substitute for testing these cases with your retention and compliance requirements.
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Draw every connection: model endpoint, tools, databases, SaaS APIs, on-premises systems and observability. Test DNS, egress restrictions, private routing, certificate handling and failure behavior. Google documents Private Service Connect; equivalent controls in AWS and Microsoft must be checked against your organization’s network architecture rather than assumed.
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A fair comparison needs more than a per-request number. Build a workload model with average and peak concurrent sessions, CPU and memory per active session, model tokens, tool calls, session duration, storage growth, log volume and network traffic.
What is established
Microsoft documents runtime billing based on CPU and memory consumed during active sessions. The available product documentation does not establish a complete, directly comparable price table across all three platforms, so prices, quotas and regional rates must be checked at the time of deployment.
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- Runtime compute or active-session consumption.
- Input and output model tokens, including retries and tool-driven calls.
- Persistent session, object, vector and log storage.
- Private connectivity, inter-region traffic and internet egress.
- Build, registry and staging artifacts.
- Engineering time for evaluations, incident response and policy controls.
Run the same scripted workload on each finalist and record consumption, errors and operator effort. Treat that as an internal sizing exercise, not as a universal benchmark for other applications.
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Production readiness checklist
- Document the agent’s model, framework, language and protocol dependencies.
- Use separate deployment, runtime and data-access identities.
- Set explicit tool allowlists, timeouts, retries and approval points for consequential actions.
- Define session ownership, retention, deletion and tenant isolation.
- Instrument model calls, tool calls, latency, token usage, errors and state transitions.
- Test cold starts, scale-out, provider throttling, model failure, tool failure and partial network outages.
- Pin dependencies and maintain a rollback artifact.
- Set quotas, budgets and alerts before production traffic.
- Review regional availability and data-boundary requirements for every dependency.
Common selection mistakes and fixes
Picking from a feature checklist alone
A platform can list the right integrations and still fail your workload because of quota, region, startup, state or network behavior. Run a representative workflow with your actual tools and identity model.
Assuming compatibility means identical behavior
An OpenAI-compatible endpoint can simplify client migration, but streaming, errors, tool calls, authentication and state semantics still require integration tests.
Ignoring the deployment language boundary
Framework documentation may support several languages while a managed deployment path supports only one. Confirm the runtime constraint before rewriting an agent.
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Combining model and hosting decisions too early
Keep the agent’s model interface behind a small adapter where practical. This lets you compare model quality and price without rebuilding the hosting layer, while still testing provider-specific features separately.
Bottom line
Choose Amazon Bedrock AgentCore Runtime when AWS alignment and cross-framework or cross-model flexibility dominate. Choose Microsoft Foundry Hosted Agents when managed containers, Entra identity, session persistence and OpenAI-compatible protocols fit your Azure operating model. Choose Vertex AI Agent Engine when you are a Python- and Google Cloud-oriented team using ADK, LangChain or LangGraph and need documented private connectivity through Private Service Connect. Validate region, quota, identity, networking, state and current pricing with a production-shaped workload before making the final commitment.
Quick Recap
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.
