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. There is no universal winner: your cloud relationship, programming language, agent framework, identity model, private-network requirements, protocol needs and billing preference should determine the choice. The official product documentation does not establish a neutral cross-platform winner for latency, uptime or total price, so treat platform selection as an architecture decision rather than a leaderboard.
The three leading managed runtimes
These services solve the operational part of running an agent: deploying application code, providing an execution environment, applying identity and security controls, handling some scaling and state concerns, and exposing interfaces for your application or users. They differ in how tightly they are coupled to a cloud, which frameworks they support directly, how they handle sessions and networking, and how runtime consumption is charged.
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| Platform | What the provider documents | Strongest fit | Important qualification |
|---|---|---|---|
| Amazon Bedrock AgentCore Runtime | Secure, serverless, purpose-built hosting for agents or tools; framework agnostic; can work with models from Amazon Bedrock, Anthropic, Google and OpenAI. | AWS teams seeking broad framework and model choice. | Confirm regional availability, adapter support, IAM, memory/session behavior, observability and runtime, model, network and egress charges. |
| Microsoft Foundry Hosted Agents | Containerized agent applications on Microsoft-managed infrastructure, with managed scaling, session-state persistence, security, lifecycle management and a dedicated Entra identity for each deployed agent. | Azure and Microsoft 365 organizations using Entra, Foundry tools and container workflows. | Check region and quota availability, container deployment, protocol selection, session storage, model requirements and consumption billing. |
| Google Vertex AI Agent Engine | Managed production services to deploy, manage and scale agents; full integration with ADK, LangChain and LangGraph, managed templates for AG2 and LlamaIndex, and custom templates for CrewAI and custom frameworks. | Google Cloud and Python teams using the supported frameworks. | Current deployment guidance supports Python; verify regions, packaging, Cloud Storage staging, IAM and private connectivity. |
Amazon Bedrock AgentCore Runtime
What it provides
AWS describes AgentCore Runtime as “a secure, serverless and purpose-built hosting environment for deploying and running AI agents or tools.” The service is presented as framework agnostic. AWS says it can work with models from Amazon Bedrock as well as Anthropic Claude, Google Gemini and OpenAI models. That combination is useful when the agent’s orchestration code and model selection are expected to change over time.
AWS’s product material lists LangChain, the OpenAI Agents SDK, the Claude Agent SDK, Strands SDK and custom frameworks as starting points. “Framework agnostic” does not mean every framework has identical behavior: an adapter, packaging method or runtime constraint may differ. Validate the exact framework version and tool-calling path you intend to deploy.
When AgentCore is the sensible default
- Your production platform, IAM practices and network controls already center on AWS.
- You want to keep model choice open across Bedrock and several external model providers.
- Your team uses one of the documented SDKs or is prepared to package a custom framework.
- You prefer a managed, serverless runtime over operating your own container orchestration and scaling layer.
Checks before committing
- Confirm that the service is available in every region required by your data-residency and disaster-recovery design.
- Map agent actions to IAM roles and least-privilege policies, including access to tools, secrets, data stores and model endpoints.
- Measure how your agent’s context, tool results and long-running tasks affect memory and session behavior.
- Verify logging, traces, metrics and evaluation hooks before production launch; do not assume that framework-level telemetry automatically appears in the runtime’s preferred format.
- Model total cost, including runtime consumption, model inference, storage, inter-region traffic and egress. The available product material does not provide a neutral cross-provider price comparison.
Microsoft Foundry Hosted Agents
What it provides
Microsoft documents Hosted Agents in Microsoft Foundry Agent Service as a way to deploy containerized agent applications to Microsoft-managed infrastructure. The service handles scaling, session-state persistence, security and lifecycle management. Each deployed agent receives a dedicated Microsoft Entra identity, which gives administrators a concrete identity boundary for authorization and auditing.
Hosted Agents expose both an OpenAI-compatible Responses protocol and a custom Invocations protocol. The former can simplify integration for clients already built around that interface; the latter may expose platform-specific invocation behavior. Select the protocol deliberately, because changing it can affect request schemas, streaming behavior, tool invocation and client libraries.
When Hosted Agents fit best
- Your organization already governs workloads through Azure subscriptions, Microsoft Entra and Microsoft Foundry.
- You have a containerized agent application and want Microsoft to operate the hosting infrastructure, scaling and lifecycle layer.
- Security teams require a separate, managed identity for each deployed agent.
- Existing applications benefit from an OpenAI-compatible Responses endpoint or need the custom Invocations protocol.
Operational questions to answer
- Is the required Azure region accepting Hosted Agent deployments, and is your subscription quota sufficient for expected concurrency?
- How will images be built, scanned, promoted and rolled back? Define a reproducible container pipeline rather than treating deployment as a one-off upload.
- Where is session state stored, what retention is required, and how will a new container instance recover an in-progress conversation?
- Which models must be deployed or made available in the same geography, and what approval or capacity requirements apply?
- How will Entra identity be mapped to data-plane permissions, tool access and downstream services?
How billing works
Microsoft documents runtime billing based on the CPU and memory resources consumed during active sessions. That is different from a simple per-request price: a long-running or memory-heavy session can cost more even when it sends relatively few user messages. Calculate expected active-session minutes, allocated CPU and memory, model calls, storage and network traffic. A complete, directly comparable price table across the three platforms is not established by the available documentation.
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Google Vertex AI Agent Engine
What it provides
Google describes Agent Engine as a set of services that lets developers deploy, manage and scale AI agents in production. It documents full integration with the Agent Development Kit (ADK), LangChain and LangGraph. Managed templates are also documented for AG2 and LlamaIndex, while CrewAI and custom frameworks can use custom templates.
The deployment guide supports deploying from an agent object or from source files. It also states that Agent Engine deployment supports Python. This is a material constraint for teams whose agent runtime is written in another language: a Python deployment boundary may require a rewrite, a service wrapper or a separate component for non-Python code.
When Agent Engine is a strong choice
- Your workloads already run on Google Cloud and use its identity, networking and operational controls.
- Your developers use ADK, LangChain or LangGraph and want documented integration rather than building a hosting adapter.
- You need managed templates for AG2 or LlamaIndex, or can maintain a custom template for CrewAI or another framework.
- Private connectivity to VPC, on-premises or multicloud services is a central requirement.
Deployment details that can change the project plan
- Plan for Python as the deployment language under the current guidance.
- For object-based deployments, account for Cloud Storage staging and the permissions required to read the staged source and dependencies.
- Test dependency packaging, native libraries, startup time and environment-variable handling in a production-like project before a migration deadline.
- Check supported regions and quotas, then design a recovery region if your availability target requires one.
- Use Private Service Connect where the documented design fits your need for secure traffic to private VPC, on-premises and multicloud services.
Comparison by the decisions that matter
| Decision area | AgentCore Runtime | Foundry Hosted Agents | Vertex AI Agent Engine |
|---|---|---|---|
| Operations model | Managed, serverless agent/tool runtime. | Microsoft-managed hosting for containerized applications, including scaling and lifecycle functions. | Managed services for production deployment, management and scaling. |
| Framework portability | Framework agnostic; AWS lists LangChain, OpenAI Agents SDK, Claude Agent SDK, Strands SDK and custom frameworks. | Container-based, so framework choice is primarily inside your image; protocol and platform integration still need validation. | 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 compatibility. | Depends on model deployments and integrations available to your Foundry environment; confirm the required models and regions. | Confirm model availability, region and quota for the models your agent calls. |
| Language constraint | Not stated as a single-language deployment constraint in the cited material; verify your adapter. | Application language is determined by the container, subject to platform image and runtime requirements. | Current deployment guidance supports Python. |
| Identity | Design around AWS IAM and the permissions required by tools and data services. | Each deployed agent has a dedicated Entra identity. | Use Google Cloud IAM and service identities; exact role design depends on staged artifacts, tools and data services. |
| Private networking | Validate AWS networking, private endpoints and egress design for each dependency. | Validate Azure private networking and the controls required by your subscription and downstream services. | Private Service Connect is documented for secure access to private VPC, on-premises and multicloud services. |
| Session and state | Confirm runtime memory and session behavior for your framework and workload. | Session-state persistence is a documented platform capability; verify storage, retention and recovery semantics. | Confirm state handling for your selected framework and deployment template. |
| Protocol | Validate the framework and tool protocol used by your agent. | OpenAI-compatible Responses and custom Invocations protocols are documented. | Use the interface exposed by the selected Agent Engine integration or template. |
| Pricing basis | Current runtime, model, storage and network prices must be checked for your region and architecture. | Runtime billing is based on CPU and memory consumed during active sessions, in addition to other service costs. | Check current Agent Engine, model, storage and network pricing for your region. |
| Neutral performance ranking | Not established. The cited official documentation does not provide a comparable independent latency, uptime or throughput benchmark. | ||
A practical selection process
Use this sequence before writing a migration plan or signing off on a production architecture. It prevents a familiar cloud brand from hiding a language, protocol or networking mismatch.
- Write the non-negotiables. Record required regions, data residency, private-service access, identity provider, supported programming languages, expected concurrency, maximum session duration, streaming needs and approved model providers.
- Eliminate incompatible runtimes. If the current deployment must be Python under the current Agent Engine guidance, treat that as a gating requirement. If the application must run as a specific container image or use a particular protocol, test that constraint against the other options.
- Map the framework and tools. Select the documented integration where possible. For a custom framework, list packaging, startup, tool-calling, streaming, retries and telemetry requirements and prove each one in a small deployment.
- Draw the identity graph. Show the agent identity, model permissions, tool permissions, secret access, data-store access and human administrative roles. Include a denied-access test for every sensitive resource.
- Design the network path. Trace every call from the runtime to models, APIs, databases, queues and observability systems. Mark public egress, private links, DNS dependencies, cross-region traffic and on-premises routes.
- Define state explicitly. Separate conversational state, durable business state, caches, files and task queues. Decide what happens when an instance restarts, a session exceeds its context, or a tool call is retried.
- Build a cost model from usage units. Estimate active session minutes, CPU, memory, model tokens, tool calls, storage, logs and network transfer. Use current regional quotes; do not infer a winner from a provider’s marketing description.
- Run a representative pilot. Use production-like prompts, tool payloads, concurrency, failure injection and security policies. Record cold starts, end-to-end latency, error recovery, observability quality and operator effort. Publish the test date and configuration internally so results remain interpretable.
- Set a portability boundary. Keep prompts, tool schemas, business state and evaluation cases separate from provider-specific deployment code. This makes a future runtime change possible without claiming that every adapter is interchangeable.
Security, governance and private data
Agent hosting expands the attack surface beyond a model endpoint. An agent can read records, call APIs, execute tools and retain state. Whichever runtime you choose, define a separate identity for the workload, limit tool permissions, isolate secrets, log administrative changes and test authorization failures. Treat model output as untrusted input when it is passed to a tool or stored as business data.
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Data boundaries also include prompts, retrieved documents, tool arguments, traces and backups. Decide which of these may contain personal, financial or confidential information, how long they are retained, who can inspect them and whether they may cross regions. A managed runtime reduces infrastructure administration; it does not remove the need for a data-classification and incident-response plan.
Observability, evaluation and reliability
Production readiness is more than a successful deployment. Instrument the complete transaction from user request through planning, model calls, retrieval, tool execution and final response. Capture correlation IDs, latency by step, token usage, tool errors, retries and policy decisions while redacting sensitive content.
Maintain an evaluation set that represents real tasks, including ambiguous requests, permission denials, malformed tool responses, slow dependencies and provider outages. Track task success, unsafe-action rate, escalation rate and cost per completed task. Run the same set after changing a model, framework adapter or runtime configuration. None of the three product descriptions supplies a neutral quality or performance ranking, so your workload-specific evidence is the basis for a defensible decision.
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A managed runtime is attractive when your team wants to spend less time operating schedulers, scaling rules, container lifecycle, identity plumbing and regional deployment. You still own the agent’s code, prompts, tools, data contracts, evaluation and incident decisions. Managed services can also introduce platform-specific protocols, templates, quotas and billing meters.
Self-managed hosting can provide deeper control over the operating system, network topology, release process and multi-cloud placement. It also makes your team responsible for capacity, patching, failover, session storage, observability and security integration. Choose it when those controls are genuine requirements and you have the staff and operational budget to maintain them; do not choose it solely to avoid learning a managed platform.
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Common failure modes and fixes
The agent deploys but cannot reach an internal service
Check DNS resolution, route tables, firewall rules, private endpoint policy, service identity permissions and egress restrictions in that order. Test from the deployed runtime, not only from a developer laptop. For Google deployments, evaluate whether Private Service Connect matches the required path; on AWS or Microsoft, use the corresponding private-network design approved for your environment.
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Do not assume process memory is durable. Put durable conversation or task state in an explicitly managed store, define a retention policy and test a restart during an active session. On Hosted Agents, verify the documented session-state persistence behavior against your chosen storage and recovery pattern.
A framework works locally but fails in the managed runtime
Compare dependency versions, environment variables, native libraries, startup commands, streaming transport and tool schemas. Reproduce the failure with the smallest agent possible, then add tools and retrieval components one at a time. A listed framework integration is a starting point, not proof that every plugin is supported.
The bill grows faster than request volume
Inspect active-session duration, memory allocation, retries, verbose traces, model selection and cross-region transfer. Microsoft’s CPU-and-memory active-session meter makes idle-but-open sessions particularly important to measure. Apply explicit timeouts, close sessions when work is complete and separate asynchronous jobs from interactive sessions where appropriate.
A provider change breaks clients
Place a stable internal API in front of provider-specific Responses, Invocations or framework interfaces. Version tool schemas and error contracts, and keep a small compatibility test suite that runs whenever the runtime adapter changes.
Quick Recap
Bottom-line choice by scenario
- Choose AgentCore Runtime when AWS is your operational home and you value documented framework and model breadth.
- Choose Foundry Hosted Agents when Azure and Entra governance, containerized deployment, managed session persistence and OpenAI-compatible or custom invocation protocols are central.
- Choose Vertex AI Agent Engine when Google Cloud networking and operations matter and your team is comfortable with the current Python deployment model and its supported framework integrations.
- Run a comparative pilot when two platforms satisfy the hard requirements. Measure your own task success, latency, failure recovery, operator work and complete cost; official pages do not provide a neutral ranking that can replace that test.
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