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There is no universal winner: the right choice depends on how much of the agent loop and its hosting you want the cloud provider to manage, and how much you want to own in code. Google combines low-code tools, managed agent APIs and a code-first development kit; AWS separates managed orchestration from code-led runtime into modular services; Microsoft offers configuration-led prompt agents, managed hosted agents and direct API use. Start with your existing cloud estate, required frameworks and models, deployment constraints, and the real cost of your workload—not with feature-name matching.
How does Google’s agent infrastructure compare with AWS and Azure?
All three providers offer ways to build and operate agents, but they draw the boundary between provider-managed services and customer-written code differently. The closest comparison is not a one-to-one feature checklist: first decide who owns the orchestration loop, then evaluate runtime, tools, memory, identity, observability and cost for your particular workload.
Google’s Gemini Enterprise Agent Platform spans low-code Agent Studio, a managed Agents API and code-first Agent Development Kit (ADK). AWS Bedrock AgentCore is a modular set of services: its Harness can manage an orchestration loop by configuration, while Runtime hosts an agent loop written by the developer. Microsoft Foundry Agent Service has configuration-led prompt agents and code-led hosted agents, with direct Responses API use also described for ephemeral agents whose definitions remain in an application.
| Decision area | Google Cloud | AWS | Microsoft Azure |
|---|---|---|---|
| Agent-building approaches | Low-code Agent Studio, managed Agents API and code-first ADK. | Modular AgentCore services; Harness manages orchestration by configuration, or developers write agent logic for Runtime. | Configuration-led prompt agents, code-led hosted agents, or direct Responses API calls for ephemeral agents. |
| Runtime guidance | Google recommends Agent Runtime for Python agents needing low operational overhead; Cloud Run for containerized, event-driven workloads needing language flexibility; and GKE for containerized workloads with complex state and fine-grained infrastructure configuration. | Runtime provides serverless hosting for code-led agents. Harness is the managed orchestration option, not the same hosting abstraction. | Hosted agents package customer code and frameworks into managed containers. Prompt agents are configuration-led. |
| Framework and model choices | Model Garden provides access to a vendor-stated catalog of more than 200 foundation models; this is not an independent quality ranking. The documentation also describes ADK. | AWS says AgentCore works with frameworks including CrewAI, LangGraph, LlamaIndex, Google ADK and Strands Agents, and with models inside or outside Bedrock. | Foundry Agent Service documentation describes model catalog access. The cited information does not establish a directly comparable framework-compatibility count. |
| Additional capabilities described | Sessions, long-term memory, centralized tool access and policy, evaluation and observability. | Memory, Gateway, Identity, Code Interpreter, Browser, Observability and Evaluations, among other modular capabilities. | Toolboxes, identity and security, tracing, evaluation and publishing. |
| Cost shape described | Selected dated prices are available for some Agent Engine and related SKUs, but they are not a complete workload estimate. | Usage can span runtime resources, inference, memory, tools, gateway or search, observability, and relevant storage or network. Harness itself has no additional orchestration charge, but invoked capabilities can cost extra. | Prompt agents incur inference and tool usage; hosted agents add container compute. |
These are provider descriptions, not results from an independent benchmark. Labels such as “memory,” “runtime” or “tool” do not prove that two services handle the same data, session behavior, scale or failure recovery. Validate the exact design with your own agent.
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Which cloud has the best AI agent service for my team?
The best fit is usually the provider that satisfies your technical requirements while adding the least operational friction to the cloud environment your team already runs. A platform’s number of features matters less than whether its managed components work with your models, tools, security boundaries and deployment pattern.
Choose Google when its development path and runtime options fit
Google offers a path from low-code Agent Studio to a managed Agents API and code-first ADK, alongside runtime choices with different operational profiles. That breadth can suit teams wanting to start with a managed development experience and retain choices about how to host containerized workloads. Google describes Model Garden access to more than 200 foundation models; treat that as a vendor-stated catalog count that may change, not as a measure of model quality or proof that every model is suitable for every agent.
Use Google’s runtime guidance as a starting point, not a deployment rule. Agent Runtime is its recommendation for Python agents where low operational overhead is important; Cloud Run is positioned for containerized, event-driven work needing language flexibility; and GKE for containerized workloads with complex state and fine-grained infrastructure configuration. Test those recommendations against your own networking, state, scaling and operations needs.
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AgentCore lets teams combine or use its services independently. Its clearest architectural choice is between Harness, which AWS describes as a managed orchestration loop configured with a model, prompt, tools, memory and limits, and Runtime, which hosts code-led agents while the developer supplies the orchestration logic. This separation can help a team decide explicitly which work to delegate and which to keep in its application.
AWS documents compatibility with several frameworks, including CrewAI, LangGraph, LlamaIndex, Google ADK and Strands Agents, and says AgentCore can work with models inside or outside Bedrock. Confirm that the specific framework, model, integration and deployment behavior you need is supported in your intended design; a provider’s general compatibility statement is not a substitute for a working proof of concept.
Choose Microsoft when configuration-led or managed code-led agents match the application
Foundry Agent Service distinguishes prompt agents, whose definitions are configuration-led, from hosted agents, which package customer code and frameworks into managed containers. Microsoft also describes direct Responses API calls for ephemeral agents whose definitions live in the application. Those approaches give teams different places to keep agent instructions and logic: in service configuration, in a hosted code package, or in the application that makes API calls.
The right option depends on how often the agent definition changes, how much application-specific orchestration you need, and whether managed container hosting meets your deployment constraints. The service overview also lists model catalog access, toolboxes, identity and security, tracing, evaluation and publishing; verify the exact capabilities and rollout state needed for your target region.
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What is the difference between Google Agent Platform, AWS AgentCore and Azure Foundry Agent Service?
The central difference is the shape of the managed boundary: Google presents a lifecycle platform with multiple development and runtime paths, AWS exposes separable services and a choice between managed or customer-owned orchestration, and Microsoft distinguishes configuration-led agents from hosted code and direct API use.
Managed orchestration versus a customer-owned loop
If you want to configure rather than implement the orchestration loop, AWS explicitly describes AgentCore Harness for that purpose. If you want to write the loop yourself, AWS Runtime supplies hosting infrastructure but leaves the orchestration logic to you. Google’s managed Agents API and ADK are distinct development paths; the available description does not establish that they map exactly to AWS Harness and Runtime. Microsoft’s prompt and hosted agents likewise represent different ways to define and run agents, not direct equivalents to those AWS components.
Before choosing, sketch the control flow your application needs: how it selects tools, handles a failed call, limits repeated actions, and decides when to return an answer. Then confirm whether the platform performs those tasks, lets you configure them, or expects your code to do them. Do not infer orchestration behavior from a product label alone.
Framework and model portability
Portability is a concrete requirement to test, not a general promise that an agent will move unchanged between clouds. AWS explicitly lists several frameworks and models inside or outside Bedrock. Google documents ADK and Model Garden access to more than 200 foundation models, while Microsoft describes model catalog access. The available descriptions do not establish identical framework support or equivalent portability across all three providers.
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Runtime, scaling and long-running work
Runtime differences matter when an agent has long sessions, state that must persist, bursts of tool calls or container-specific dependencies. Google describes Agent Runtime, Cloud Run and GKE for different operational needs. AWS Runtime is a serverless hosting environment for code-led agents. Microsoft hosted agents run customer code and frameworks in managed containers. Those descriptions do not by themselves establish comparable session limits, scaling behavior or support for a particular long-running workflow.
Map the lifetime of one real task: when it starts, how long it may wait on a tool, what state survives between calls, and what should happen if a worker or external dependency fails. Verify the service’s current limits and recovery behavior for your region and workload before treating a runtime as suitable.
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How should I compare tools, memory, security and observability?
Compare what each capability does in your application rather than assuming similarly named features are interchangeable. Agent workloads can cross model, tool, identity and data boundaries, so a useful assessment follows one representative task from request through tool execution, state updates and debugging.
Tools and connections
The providers describe different tool-related building blocks. Google documents centralized tool access and policy; AWS lists Gateway, Browser and Code Interpreter among AgentCore capabilities; Microsoft describes toolboxes. These labels do not establish that a particular API, MCP connection, browser action, code execution environment, search system or enterprise data source is supported in the same way by all three.
For each tool your agent needs, record the connection method, credentials, permissions, network path, timeout, retry behavior and data returned to the model. Prove that the tool can be called under the production identity and network rules, not only from a developer account. For browser or code execution tasks, verify the isolation and data-handling properties required by your organization rather than assuming the service name answers them.
Memory and state
Google describes sessions and long-term memory, and AWS lists Memory as a modular capability. The Microsoft overview in the available information does not establish a directly comparable memory service. That does not prove the absence of memory options; it means you should check the service documentation and decide whether state belongs in a managed agent capability or in your application’s own storage.
Separate short-lived conversation context from durable facts, task state and audit history. Decide what should persist, who can read or delete it, how long it remains, and whether it can contain sensitive data. Test retrieval and deletion paths with representative data before production; the name “memory” alone does not answer retention, isolation or governance requirements.
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Each provider describes identity or governance-related capabilities, but the named feature sets differ: Google includes centralized tool access and policy, AWS lists Identity, and Microsoft describes identity and security. Compare the actual principal used for model and tool calls, permission scope, secret handling, network restrictions, audit trail and approval process for changes.
Use least-privilege identities for agent workloads and test denied access as deliberately as successful access. An agent that can invoke a tool is not necessarily authorized to perform every action exposed by that tool. Establish which actions need human approval, how policy decisions are logged, and how credentials are rotated before the agent is allowed to affect production systems.
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Tracing, evaluation and production debugging
Google describes evaluation and observability, AWS lists Observability and Evaluations, and Microsoft lists tracing and evaluation. A shared category name does not guarantee the same trace detail, retention, export path or evaluation method. Check whether a trace follows the request across the model and every tool, whether sensitive inputs are captured, and whether the data can reach your existing monitoring workflow.
Build an evaluation set from realistic tasks, including tool failures, ambiguous requests and cases where the agent must not act. Track task success and policy compliance alongside operational measures such as latency and resource use. Provider tools can help surface problems, but the team still needs to decide what constitutes an acceptable answer and a safe action for its use case.
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The available figures do not support naming a price winner. Total cost depends on model inference, tool calls, runtime or container resources, memory, observability and actual usage; compare complete workloads using the same assumptions rather than comparing a single SKU.
Google announced selected Agent Engine-related prices in 2025, with stated effective dates of December 16, 2025, and January 28, 2026. These are dated SKU figures, not a complete cross-provider estimate, and they should not be treated as a live quote without checking current pricing, region and billing details.
| Google Cloud SKU described in the 2025 announcement | Stated price and effective date | Qualification |
|---|---|---|
| Agent Engine runtime | $0.0864 per vCPU-hour; $0.0090 per GB-hour for memory, effective December 16, 2025. | Selected dated SKUs, not a full workload price. |
| Code execution | $0.0864 per vCPU-hour; $0.0090 per GB-hour for memory, effective January 28, 2026. | Selected dated SKUs, not a full workload price. |
| Stored session events | $0.25 per 1,000 stored session events, effective January 28, 2026. | Selected dated SKU; confirm live rate and billing details. |
| Memory Bank | $0.25 per 1,000 memories stored and $0.50 per 1,000 memories retrieved, effective January 28, 2026. | Selected dated SKUs; confirm live rate and billing details. |
AWS says Harness itself has no additional orchestration charge, but the AgentCore capabilities it invokes can incur charges. Its documented cost categories include runtime resources, model inference, memory, tools, gateway or search, observability, and storage or network where relevant. Runtime resource cost depends on actual CPU and memory use; wall-clock time alone is not a reliable estimate of CPU cost.
Microsoft distinguishes prompt-agent costs, which include inference and tool usage, from hosted-agent costs, which add container compute. That distinction is useful for building a cost model, but it does not establish that prompt agents are always cheaper: the work performed, call volume and required tools matter.
Build a workload estimate before committing
- Define the unit of work. Use a representative task and record expected model calls, input and output tokens, tool calls, session length and whether state persists.
- Estimate concurrency and duration. Include ordinary traffic and expected peaks; for hosted or runtime-based designs, estimate resource use rather than assuming elapsed time equals CPU consumption.
- Include every service the task invokes. Account for inference, runtime or container resources, memory, tools, gateway or search, observability, and relevant storage or network charges.
- Use current regional rates. Check provider pricing and availability for the intended deployment region and date. The Google figures above are specific dated SKUs, not a complete current bill.
- Run a measured pilot. Compare cost per completed task and inspect failed or retried work as well as successful runs. A cheaper individual call can still yield a more expensive workflow if it needs more calls or resources.
What should I test before selecting a platform?
Run the same agent design against the short list of platforms, using the models, tools, data sensitivity and failure conditions you expect in production. This is more informative than comparing feature names or relying on a provider’s general architecture guidance alone.
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- Agent loop: Can the service manage the orchestration you need, or must your code implement it? Can you limit steps and handle failed or repeated tool calls?
- Model and framework: Can you run the specific framework and model combination you intend to deploy, including any external model requirement?
- Runtime: Can the agent use your required language, dependencies, networking and state pattern? What happens when a task lasts longer than expected or a dependency is unavailable?
- Tools: Can it call the exact APIs and enterprise systems under the correct production identity and permissions?
- Data: Where do prompts, tool results, traces and persistent state go? Can your team meet its retention, access and deletion requirements?
- Operations: Can operators trace, evaluate, debug and roll back the agent using processes they already support?
- Cost: What is the measured cost of a completed task under realistic context size, concurrency, session duration and failure recovery?
- Deployment: Is the service available with the required release status, quota and model support in your target region?
Use the pilot to document trade-offs: what the provider manages, what remains in your application, what must be operated separately, and what limitations emerged. Repeat the test when changing models, tools, regions or agent patterns; those changes can alter both operational fit and cost.
How do regional availability and product maturity affect the decision?
Product names, availability, regions, quotas, release status and prices can change quickly. Confirm the current official service documentation and rate information for the region and deployment date you actually need; a capability described in a general overview does not establish that it is available in every region or release state.
For each required component, verify that the specific model, runtime, memory or tool capability is supported in the target region and in the status your organization permits. Check quotas and limits as well as whether the service is generally available or in preview. If a dependency is not available where your data must stay, that can rule out an otherwise attractive architecture.
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How do I make the final choice?
Choose by the work you want the cloud to own. Google is a strong candidate to evaluate when its combined low-code, managed API, ADK and runtime paths match the team’s operating model. AWS is a strong candidate to evaluate when you want modular AgentCore capabilities and a clear choice between managed Harness orchestration and code-led Runtime. Microsoft is a strong candidate to evaluate when prompt-agent configuration, managed hosted code or direct API use fits the application.
Then apply two practical filters. First, prefer the platform that fits your existing cloud estate and security operations unless another option demonstrates a material workload advantage. Second, require a proof of concept to demonstrate the exact model, tools, identity, state handling, recovery and cost profile you need. No feature checklist can establish that fit on its own.
Frequently Asked Questions
Can I use Google ADK with AWS AgentCore?
AWS lists Google ADK among the frameworks AgentCore works with. That is a documented compatibility statement, not a guarantee that every ADK feature or deployment pattern will behave identically; validate the exact version and architecture you plan to use.
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Does AWS charge extra for AgentCore Harness?
AWS documentation says Harness has no additional orchestration charge. Services it calls—such as model inference, memory or tools—may still incur charges.
Can an Azure agent be temporary rather than hosted?
Microsoft describes direct Responses API calls for ephemeral agents whose definitions live in the application. That pattern differs from keeping a prompt agent configuration or packaging code into a hosted agent.
Is Google’s catalog of more than 200 models a quality ranking?
No. It is Google’s vendor-stated catalog count, not an independent ranking of model quality. The count may change, so check the current catalog for the specific model and region you need.
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.
