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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThere is no universal winner among Amazon Bedrock, Microsoft Foundry Agent Service and Google Vertex AI Agent Engine. If your application, data and operations already sit on AWS, assess Amazon Bedrock AgentCore for a new agent runtime; Microsoft and Google offer managed paths that may fit better in their own cloud estates. First check framework and model support, how much orchestration you want to own, and the governance controls your deployment requires. One important caveat: AWS says Bedrock Agents Classic is no longer open to new customers.
What is the current AWS option for a new AI agent?
Bedrock Agents Classic is an existing-customer path
AWS documentation identifies the older service as Bedrock Agents Classic, says it is no longer open to new customers, and directs readers seeking similar capabilities to AgentCore. Existing customers can continue using Classic. That makes “Amazon Bedrock Agents” an ambiguous description for a new project: verify which service a tutorial or architecture actually uses before adopting it.
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AgentCore is the AWS path to assess for new builds
AWS describes AgentCore Runtime as designed for agents built with open-source frameworks and models both inside and outside Amazon Bedrock. Its documentation also mentions MCP and A2A protocols. These are broad flexibility claims, not a guarantee that every framework, model, protocol feature or combination is supported in every configuration. Check the current integration documentation for your intended stack.
AWS announced general availability of multi-agent collaboration for Amazon Bedrock on March 10, 2025. The announcement described specialized agents coordinated by a supervisor, along with inline agents, payload referencing, CloudFormation and CDK support, monitoring and observability. Treat those as features stated in that announcement; check current AWS documentation for exact availability, regional coverage and implementation details.
#1 Best Overall
Bedrock vs. Microsoft Foundry vs. Vertex AI: what is being compared?
These offerings do not all describe the same layer. Microsoft Foundry Agent Service distinguishes configuration-first prompt agents from hosted code. AgentCore and Vertex AI Agent Engine emphasize runtime and operational services around deployed agents. A fair comparison begins with the part of the system you want the platform to manage.
| Platform | Agent-building and runtime approach | Framework and model notes | Operational capabilities described by the vendor |
|---|---|---|---|
| Amazon Bedrock AgentCore | Agent runtime and related services; evaluate it for new AWS projects rather than assuming Bedrock Agents Classic is available to new customers. | AWS describes support for open-source agent frameworks and models inside or outside Bedrock, plus MCP and A2A. Verify the exact integration for your design. | Assess the current AWS documentation for the particular identity, networking, tracing, state, release and regional needs of your deployment. |
| Microsoft Foundry Agent Service | Prompt agents use configuration rather than runtime code; hosted agents let teams deploy framework-based or custom code. | Microsoft lists Agent Framework, LangGraph, OpenAI Agents SDK, Anthropic Agent SDK, GitHub Copilot SDK and custom code for hosted agents. | Microsoft describes managed endpoints, automatic scaling, dedicated Entra identity for hosted agents, session-level state persistence and end-to-end observability. |
| Google Vertex AI Agent Engine | Managed services for deploying, managing and scaling production agents, with differing framework integration levels. | Google lists full integration for ADK, LangChain and LangGraph; Vertex AI SDK integration for AG2 and LlamaIndex; and custom templates for CrewAI or custom frameworks. | Google describes IAM, VPC Service Controls and observability through Cloud Trace, Monitoring and Logging. Its overview also notes limits on some controls in the described setup. |
This table summarizes vendor documentation, not a standardized feature test. Availability, framework versions, launch stage and control support can differ by region and configuration.
Which platform should I use to build AI agents?
Choose by cloud estate and identity
Start with where the application, its data, policies and operating expertise already live. Keeping an agent near its data and existing identity controls can simplify the architecture, but that alone does not prove a platform is cheaper or more secure. Microsoft specifically documents Entra identity for hosted agents. For AWS and Google, verify the current identity and security documentation for the services and configuration you intend to use rather than assuming comparable controls from a high-level product description.
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A prompt-agent path can suit a relatively configuration-driven workflow. A hosted-code path offers more room to implement an existing framework or custom agent loop, but gives the team more code and behavior to maintain. AgentCore and Vertex AI Agent Engine are useful candidates when the need centers on runtime services around an agent your team deploys. Decide whether the platform should define the loop, host your code, or mainly provide runtime infrastructure.
Choose by the exact framework and model combination
Do not select a platform based only on a framework name appearing in a supported list. Check the support tier, documented versions, deployment constraints, tool integration and whether the model you intend to call is available through the platform or needs a separate connection. This matters particularly when using multi-agent orchestration: each sub-agent, supervisor and tool call may have different requirements.
What governance and operations should you compare?
Before committing, map your production requirements to documented controls for the specific service, region and deployment mode. Product-level claims such as “managed” or “observable” are not substitutes for checking the controls your application needs.
- Identity: Confirm how the agent, tools and users authenticate, how permissions are scoped, and how credentials are rotated.
- Network and data boundaries: Verify private connectivity, egress restrictions, VPC or equivalent controls, and where prompts, tool results, logs and state are stored or processed.
- Tracing and evaluation: Determine whether traces include model calls, tool calls and handoffs; how long they are retained; and what evaluation or debugging workflow is available.
- State and memory: Check persistence scope, isolation between users or sessions, deletion behavior and the mechanism for controlling retained data.
- Release management: Establish how prompt or code versions are promoted, rolled back and audited, and whether infrastructure can be reproduced through the tools your team uses.
- Regional and compliance fit: Verify service availability and each required control in the actual region and configuration. Google’s Agent Engine overview says data residency, customer-managed encryption keys (CMEK) and access transparency are not supported in the setup it describes; check the current documentation if any of those are requirements.
How should you compare agent-platform costs?
Do not treat a runtime rate as the total cost of an agent. Estimate each workload component separately: model inference, tool usage, runtime compute, memory, storage, network transfer and the engineering and operations effort needed to build and run the system.
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|---|---|---|
| Model inference | Model choice, input and output volume, retries, supervisor calls and calls made by sub-agents. | Microsoft’s overview distinguishes inference costs from other costs; the cited material does not establish a normalized cross-cloud inference comparison. |
| Tools and external services | Paid APIs, databases, search, execution environments and any per-call or data-transfer charges. | Microsoft’s overview distinguishes tool usage from other costs; no cross-platform workload total is established. |
| Agent runtime | Compute time, memory, concurrency, idle capacity and scaling behavior under your workload. | Google’s overview lists runtime rates of $0.0994 per vCPU-hour and $0.0105 per GiB-hour in the documentation accessed October 4, 2026. These are Google service-specific published rates, not a cross-cloud comparison; verify current prices and applicable conditions. |
| Storage and networking | Persisted state, logs, artifacts, network transfer and connections to data or tools. | Not stated as a comparable total in the cited platform overviews. |
| Engineering and operations | Integration work, testing, security review, monitoring, incident response and ongoing maintenance. | No common measure is established. Estimate this for your team and architecture. |
A useful estimate is a representative task traced end to end: count calls to the supervisor, sub-agents, models and tools; measure runtime and memory; then apply the relevant region’s current prices. Include failure retries and realistic concurrency. Without those workload assumptions, a single “cheapest platform” claim is not meaningful.
How to make a platform decision
- Write down the deployment constraints. Name the target cloud and region, data location, identity system, network boundaries and any mandatory governance controls.
- Define the agent shape. Decide whether the workflow is a configuration-driven prompt agent, hosted framework code, custom code, or a supervisor coordinating multiple specialized agents.
- Check the exact compatibility matrix. Confirm model access, framework support tier, tool interfaces and required protocols for the intended configuration; do not rely on a generic product list.
- Prototype one representative workflow. Trace model and tool calls, session state, errors and handoffs. Test the operational controls as well as the happy path.
- Build a workload-based cost estimate. Use current regional prices and the measured call, compute, memory, storage and networking profile, then account for engineering and operations effort.
- Validate production requirements before choosing. Confirm availability, identity, network, observability, state handling and release controls in the required region and service tier.
Is Amazon Bedrock Agents still available for new projects?
AWS says Bedrock Agents Classic is no longer open to new customers, while existing customers can continue using it. For a new AWS project, assess AgentCore for similar agent-runtime capabilities and verify the current AWS guidance for the design you plan to deploy. This status is specific to Bedrock Agents Classic; it should not be generalized into a claim that Amazon Bedrock itself is unavailable for new projects.
Is there a proven speed or cost winner?
The cited platform materials do not establish an apples-to-apples winner for speed, agent quality or total cost. Those outcomes depend on the model, prompts, tools, orchestration, data location, workload and implementation. Compare platforms with the same representative task and acceptance criteria rather than relying on an unqualified ranking.
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