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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →There is no universal cost or performance winner. For a new AWS-based agent, compare Amazon Bedrock’s current agent options—especially AgentCore, not Bedrock Agents Classic—with Microsoft Foundry Agent Service and Google Vertex AI Agent Engine. The practical choice depends on where your application and data already run, how much of the agent runtime you want managed, which frameworks and models you need, and what the full operating cost will be.
Amazon Bedrock vs. other platforms for building AI agents: what is being compared?
These services help teams build and operate AI agents, but they do not all abstract the same parts of an application. Microsoft Foundry offers a configuration-first prompt-agent option alongside hosted code. Amazon Bedrock’s AgentCore and Google’s Vertex AI Agent Engine emphasize runtime services around deployed agents. The product names and integration choices matter: comparing a new AWS project with Bedrock Agents Classic alone would miss AWS’s current direction for new customers.
For a finance-conscious decision, compare the cost of the complete workload rather than a single runtime price. Model inference, tools, runtime compute, memory, storage, network use, and the engineering and operations time required to run the system can all affect the total.
Is Amazon Bedrock Agents still available for new projects?
AWS documentation identifies the older service as Bedrock Agents Classic, says it is no longer open to new customers, and points to AgentCore for similar capabilities. Existing customers can continue using Classic. For a new AWS build, evaluate AgentCore and the specific AWS services the architecture requires; do not assume the Classic experience is the default starting point.
#1 Best Overall
AWS announced general availability of multi-agent collaboration for Amazon Bedrock on March 10, 2025. In that announcement, AWS described specialized agents coordinating under a supervisor for complex, multistep workflows. The announced capabilities included inline agents, payload referencing, CloudFormation and CDK support, monitoring, and observability. Treat the announcement as a dated description: confirm current regional availability and feature details in AWS documentation before committing to a design.
Bedrock vs. Microsoft Foundry vs. Vertex AI for agents
| Platform | Agent-building approach | Framework and model considerations | Operations and governance to check |
|---|---|---|---|
| Amazon Bedrock with AgentCore | AgentCore provides runtime services for deployed agents. Bedrock multi-agent collaboration was announced as a supervisor coordinating specialized agents. | AWS describes AgentCore as supporting open-source agent frameworks and models inside or outside Bedrock, as well as MCP and A2A protocols. Verify the exact integrations needed for your design. | Check the current AWS identity, networking, observability, regional availability, and release-control requirements for your chosen services. Bedrock Agents Classic is a continuing-customer path, not the new-customer default. |
| Microsoft Foundry Agent Service | Choose between prompt agents configured without runtime code and hosted agents that run 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. Confirm availability and fit for the required configuration. |
| Google Vertex AI Agent Engine | Managed services for deploying, managing, and scaling production agents, with different 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. The overview says some controls, including data residency, CMEK, and access transparency, are not supported in the described Agent Engine setup; validate requirements against the exact service configuration. |
These are vendor-documented capabilities, not a claim that every listed feature is available in every region, release stage, or configuration. Confirm support for the specific model, framework version, protocol, and security control your application needs.
Rank #2
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How should you compare the real cost?
Build a workload estimate for each viable platform using the same expected traffic and agent behavior. A runtime rate by itself is not a total-cost comparison, and the available published information does not establish that one platform is universally cheaper.
Separate the cost components
- Model inference: estimate the models and volume of input and output usage the workload requires. A platform’s runtime charge does not include a complete estimate of model costs.
- Tools and integrations: account for external APIs, search, databases, and other metered services the agent calls.
- Runtime and memory: estimate compute and memory for the agent’s active time, concurrency, and stateful work.
- Storage and networking: include persistent state, logs, observability data, and any applicable data-transfer costs.
- People and operations: account for the work of integrating identity and policies, debugging, evaluating changes, and supporting releases. More managed infrastructure may reduce some operational work, but does not remove application or governance responsibilities.
Use Google’s published runtime rates as one input, not a verdict
Google’s Agent Engine overview lists runtime pricing of $0.0994 per vCPU-hour and $0.0105 per GiB-hour of memory. These are Google’s service-specific listed rates in the documentation accessed October 4, 2026; they are not an apples-to-apples estimate against AWS or Microsoft. The overview’s figures describe runtime compute and memory, not a complete workload total. Check current prices, applicable region, and billing details before budgeting.
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Compare scenarios, not isolated list prices
For each platform, estimate a low, expected, and peak workload using the same assumptions: agent runs, model usage, tool calls, runtime duration, memory, and retention. Then add the services and staff time needed for the architecture. If one option requires a framework adaptation or a different cloud service, include the cost of that change rather than treating the agent runtime as the whole bill.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which platform should you use to build AI agents?
Choose the AWS path when the application already belongs on AWS
AgentCore is the relevant AWS path to assess for a new build when your product, data, operating practices, or required services are already in AWS. Its documented framework and model flexibility may help when an agent needs open-source components or models outside Bedrock. Confirm the exact integrations and AWS controls required; do not select Classic for a new project without checking its new-customer restriction.
Rank #4
Choose Microsoft Foundry when its agent modes fit your operating model
Foundry is worth evaluating if you want a configuration-first prompt-agent path, or if your team wants to bring hosted framework code while using Microsoft’s described managed hosting, scaling, identity, and observability features. The hosted-code approach offers framework choices, but the team still needs to confirm that its chosen framework and implementation fit the service’s current support and governance requirements.
Choose Vertex AI Agent Engine when its integrations and controls fit
Vertex AI Agent Engine is a candidate when the application is already centered on Google Cloud and its managed runtime and framework integration tiers match the architecture. Evaluate its documented security and observability options against the controls your organization actually requires, including the limitations noted for the described setup, before treating it as production-ready for a regulated workload.
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Do not choose on an unsupported speed or savings claim
The available vendor material does not provide a standardized, cross-platform benchmark establishing a quality, speed, or total-cost winner. A credible comparison requires the same task, models, tool behavior, traffic assumptions, and security constraints on each candidate.
Quick Recap
A practical decision checklist
- Start with the existing estate. Identify where the application, data, identity policies, and operations expertise already sit. Moving clouds solely to use an agent service can introduce costs and complexity that a runtime-price comparison will not capture.
- Specify the agent design. Decide whether you need a prompt/configuration agent, hosted code, a managed runtime for your own agent, or coordinated agents under a supervisor.
- Verify required integrations. Check model access, framework support tier, protocols, tool connectivity, and the exact versions and regions available for the intended architecture.
- Write down governance requirements. Include identity, network boundaries, tracing and logs, state and memory, evaluation, release controls, and any residency or encryption requirements. Compare only controls that apply to the configuration you plan to deploy.
- Model full workload cost. Estimate inference, tool use, compute, memory, storage, networking, and staff effort with a shared set of workload assumptions.
- Run a representative pilot. Test the real agent task and operational workflow, then compare reliability, latency, maintainability, and actual cost under measured conditions. Treat the outcome as specific to that workload, not as a universal ranking.
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.
