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There is no universal winner among Google’s Gemini Enterprise Agent Platform, Amazon Bedrock AgentCore, and Microsoft Foundry Agent Service. The right choice depends on how much of the agent loop you want the cloud provider to operate, which models and frameworks you must keep portable, where your data and identity controls already live, and whether your workload is short-lived, stateful, or long-running. Google emphasizes a lifecycle stack that ranges from low-code design to a managed runtime and code-first ADK. AWS splits orchestration, hosting, memory, tools, identity, and evaluation into modular AgentCore services. Microsoft offers configuration-led prompt agents, managed hosted agents for customer code, and direct Responses API calls. Compare those operating boundaries—not similarly named feature boxes—against one representative workload before committing.
What are Google, AWS and Azure actually offering for AI agents?
All three clouds now package more than a model API: they provide ways to define an agent, run its loop, connect tools, manage identity, retain state, inspect traces and evaluate behavior. Their abstractions are different, so a feature-by-feature checklist can create false equivalence.
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Google’s Gemini Enterprise Agent Platform documentation describes three build paths. Agent Studio is the low-code environment; the managed Agents API and runtime provide hosted execution; and the Agent Development Kit (ADK) is the code-first route. Google groups the surrounding capabilities into Build, Scale, Govern and Optimize, including Model Garden access, sessions, long-term memory, centralized tool access and policy, evaluation, and observability.
Google says Model Garden provides access to more than 200 foundation models. That is a vendor-stated catalog count, not an independent quality ranking, and the catalog can change. Treat it as an indication of breadth rather than a promise that every model is available in every region or through every agent feature.
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AWS: independent AgentCore building blocks
Amazon Bedrock AgentCore is intentionally modular. Its documented components include Harness, Runtime, Memory, Gateway, Identity, Code Interpreter, Browser, Observability and Evaluations, with other capabilities available as the service evolves. You can combine the modules or use only the pieces your application needs.
AWS also describes support for frameworks such as CrewAI, LangGraph, LlamaIndex, Google ADK and Strands Agents. AgentCore can work with models inside or outside Bedrock, which matters when an existing application already depends on a particular provider or an open-weight model.
Microsoft Azure: prompt, hosted and ephemeral application agents
Microsoft Foundry Agent Service has three materially different paths. Prompt agents are defined through configuration. Hosted agents package customer-written code and frameworks into a managed endpoint. A direct Responses API call can create an ephemeral agent whose definition remains in the calling application rather than becoming a separately managed resource.
The service overview also covers a model catalog, toolboxes, identity and security, tracing, evaluation and publishing. Those capabilities are useful only when they match your required data boundary, framework and deployment model; the label “agent service” does not mean the same loop is managed in each path.
How do managed orchestration and customer-owned agent loops differ?
The central architectural question is who decides the next step after each model response. Managed orchestration reduces code and operational work, while a customer-owned loop gives you precise control over retries, branching, state and failure recovery.
| Provider and option | Who owns the loop? | What you configure or write | Typical trade-off |
|---|---|---|---|
| Google managed Agents API/runtime | Google-managed service | Agent definition, model, instructions, tools, sessions and policies | Lower operational overhead, with less direct control than a fully self-managed loop |
| Google ADK on Agent Runtime, Cloud Run or GKE | Your application code | ADK agent logic plus the selected runtime and integrations | More control; you take responsibility for code behavior and runtime design |
| AWS AgentCore Harness | AWS-managed orchestration loop | Model, prompt, tools, memory and limits by configuration | Fast path to a managed loop; invoked capabilities still create usage charges |
| AWS AgentCore Runtime | Your agent code | Orchestration logic and calls to other AgentCore services | Fine-grained behavior and framework freedom, with more code to operate |
| Microsoft prompt agent | Microsoft-managed configuration-led agent | Prompt and service configuration | Simple publishing and governance, but less room for custom control flow |
| Microsoft hosted agent | Your code inside a Microsoft-managed endpoint | Framework and agent implementation packaged for hosted execution | Custom logic with managed endpoint operations; container compute is added to inference and tool costs |
| Microsoft Responses API | Your application | Agent definition and each interaction in application code | Useful for ephemeral agents; lifecycle and state decisions stay with you |
This is not a ladder in which one option is objectively better. A regulated workflow may need explicit, reviewable transitions that favor customer-owned orchestration. A support assistant with predictable tools may benefit from a managed loop so the team does not build retries, session handling and tool dispatch from scratch.
Which platform gives developers the most control?
For maximum control, use a code-first path and keep the orchestration loop in your repository. AWS makes that boundary explicit with AgentCore Runtime; Google offers ADK with selectable runtimes; Microsoft’s hosted-agent path keeps your code while managing the endpoint. Configuration-led options trade some control for faster delivery.
When Google is the better control fit
Google’s ADK lets the team own the agent implementation while choosing among Agent Runtime, Cloud Run and GKE. That gives you a way to start with a managed experience and move toward more infrastructure control without changing the fundamental agent framework. Confirm that the ADK language support, networking model and required model endpoints fit the deployment you intend to use.
When AWS is the better control fit
AgentCore Runtime is designed for developer-written agent logic. Your code can call AgentCore Memory, Gateway, Identity, Browser, Code Interpreter, Observability and other services as needed rather than accepting one monolithic execution model. AWS’s stated framework support is useful when the existing application already uses CrewAI, LangGraph, LlamaIndex, Google ADK or Strands Agents.
When Microsoft is the better control fit
Hosted agents preserve customer code while placing the endpoint and container operations in a managed service. The Responses API goes further toward application ownership for short-lived or dynamically defined agents. Prompt agents are the least code-intensive option, but they are also the least suitable when the loop needs custom branching, bespoke recovery or unusual state transitions.
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How portable are models and frameworks?
A portable agent has at least two independent dimensions: the framework that expresses the loop and the model endpoints the loop calls. Check both, because supporting several models in a catalog does not automatically make tools, context handling or safety controls portable.
| Question | AWS | Microsoft | |
|---|---|---|---|
| Framework choices | Code-first Agent Development Kit, plus managed and low-code paths | AgentCore documentation names CrewAI, LangGraph, LlamaIndex, Google ADK and Strands Agents | Prompt configuration, hosted customer frameworks and direct Responses API usage |
| Model breadth | Model Garden access to more than 200 foundation models according to Google’s documentation; catalog count is vendor-stated and changeable | Models in Bedrock and models outside Bedrock are supported by the AgentCore positioning | Model catalog access is documented, but verify the exact models and regions required by your workload |
| Portability risk | Managed sessions, memory and tool policy may require adaptation when moved | AgentCore service calls, IAM and Gateway configuration become AWS-specific dependencies | Hosted endpoint packaging, identity and tracing integrations may require Azure-specific changes |
For a proof of concept, run the same agent logic against the models you expect to retain for two years, not only the default model. Record differences in tool-call schemas, context limits, structured-output behavior, latency and refusal handling. Those differences usually create more migration work than changing the HTTP endpoint.
Which runtime is best for serverless, containerized and long-running work?
Choose the runtime from the workload’s state, language, networking and scaling requirements. A managed agent runtime is convenient for Python-centric agents with low operational overhead; a general container platform is preferable when you need language flexibility or unusual infrastructure; and a cluster platform is justified when you need deep control over state and scheduling.
Google runtime choices
- Agent Runtime: Google recommends this when a Python agent needs a managed experience and the team wants to minimize operations.
- Cloud Run: Google positions it for containerized, event-driven workloads that need language flexibility.
- Google Kubernetes Engine: Google positions GKE for containerized workloads with complex state and fine-grained infrastructure configuration.
The documentation and pricing material use both “Agent Runtime” and “Agent Engine” terminology in places. Verify the current product label, supported regions and billing page when you design a deployment; do not assume that a name change leaves limits or prices unchanged.
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AgentCore Runtime is the serverless hosting environment for code-led agents. The runtime supplies infrastructure while your code owns orchestration. Long-running work, session persistence and external workers still need an explicit design: document where state is stored, how a failed tool call is retried, and how an interrupted run resumes.
Microsoft hosted and Responses API choices
Hosted agents place customer code and frameworks behind a managed endpoint, adding container compute to the cost model. Direct Responses API calls suit ephemeral agents whose definitions live in the application. For durable workflows, establish where conversation state, files, credentials and resumable job metadata live before selecting the API path.
How should you compare tools, memory and enterprise data connections?
Start with the actions your agent must perform, then map each action to an API or function, an MCP connection if required, browser access, code execution, search or an enterprise data connector. The provider names are less important than whether the call can be authorized, observed, retried and revoked independently.
Tool execution
Google describes centralized tool access and policy as part of its platform. AWS lists Gateway, Browser and Code Interpreter as distinct AgentCore capabilities. Microsoft describes toolboxes within Foundry Agent Service. For each tool, verify input and output schemas, network egress, per-call timeouts, secrets handling and whether a failed call is visible in the trace.
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Memory and state
Google documents sessions and long-term memory. AWS exposes Memory as a separate AgentCore module. Microsoft’s service overview covers agent capabilities broadly, but the available material does not establish a directly equivalent, named memory service with identical semantics; confirm whether your required conversation, semantic or task state is managed by the service or must remain in your application.
Separate three kinds of data in your design: short-lived context sent to a model, durable conversation history, and business state that must remain authoritative outside the model. A memory feature should never become the sole system of record for an order, entitlement, approval or other transactional fact.
How do identity, governance and network controls compare?
All three providers discuss identity or governance, but the implementation follows each cloud’s security model. Compare the complete permission path rather than checking whether a product page contains the word “identity.”
- Identity scope: identify the principal used by the agent, each tool and any background worker. Determine whether permissions can be separated by agent, session, tenant and environment.
- Network path: document private connectivity, egress restrictions, firewall rules, proxy requirements and access to model endpoints and enterprise systems.
- Data handling: specify what is stored in prompts, session events, memory, traces and evaluation artifacts, and how long each category is retained.
- Governance: require approval for tool registration, model changes, prompt changes and production publishing. Keep an audit trail that links a run to the exact agent version and policy.
- Tenant isolation: test whether one customer’s memory, files, traces or tool credentials can be addressed by another tenant through prompts or indirect references.
Google groups governance and centralized policy in the Agent Platform description; AWS provides a dedicated Identity component; Microsoft lists identity and security in Foundry Agent Service. Those labels signal available control surfaces, not identical defaults. Validate them with a threat model and a representative least-privilege deployment.
Which provider has the best tracing, evaluation and production debugging?
No provider can make an unreliable agent safe merely by adding a dashboard. Choose the platform that records the events your incident process needs: model requests, tool arguments and results, retries, state reads and writes, policy decisions, latency and token usage.
| Operational need | AWS | Microsoft | |
|---|---|---|---|
| Tracing and visibility | Observability is part of the platform lifecycle description | AgentCore includes Observability | Foundry Agent Service lists tracing |
| Evaluation | Evaluation is included in the Build/Scale/Govern/Optimize capability set | AgentCore includes Evaluations | Evaluation is listed in the service overview |
| What to verify yourself | Retention, redaction, trace detail, export format, sampling, alerting, correlation IDs and whether tool payloads are visible under your security policy | ||
Build evaluation cases from real failure modes: a tool returns malformed data, a permission is revoked mid-run, a model invents a successful action, a long session exceeds context, or a downstream service times out. A platform that can show the exact transition and replay the relevant input will shorten diagnosis more than a larger catalog of optional metrics.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do Google, AWS and Azure agent services cost?
There is no defensible price winner from the available figures. Total cost depends on model inference, token volume, tool calls, runtime CPU and memory, session or memory storage, observability, network and the concurrency and duration of your workload.
| Provider | Documented cost information | How to interpret it |
|---|---|---|
| Google Cloud | $0.0864 per vCPU-hour for Agent Engine runtime and $0.0090 per GB-hour for memory, from a 2025 announcement effective December 16, 2025 | Dated selected SKUs; confirm current region, service name and billing terms before using them in a forecast |
| Google Cloud | $0.0864 per vCPU-hour for code execution and $0.0090 per GB-hour for memory; $0.25 per 1,000 stored session events; $0.25 per 1,000 Memory Bank memories stored; and $0.50 per 1,000 memories retrieved, from a 2025 announcement effective January 28, 2026 | These are specific usage rates, not a complete agent bill or a cross-cloud estimate |
| AWS | AgentCore documentation describes usage-based charges for runtime resources, model inference, memory, tools, Gateway/search, observability and storage or network where applicable. Harness has no additional orchestration charge, but the capabilities it invokes can incur charges. | Estimate CPU and memory consumption, model calls and every configured capability. Wall-clock duration alone does not predict runtime CPU cost. |
| Microsoft Azure | Prompt agents incur per-call inference and tool usage; hosted agents add container compute. | Compare the actual call pattern and required compute rather than assuming one agent type is always cheaper. |
Build a workload model with the same assumptions for each provider: requests per day, average and peak tokens, tool calls per run, concurrent sessions, average session duration, memory reads and writes, trace retention, container CPU and memory, and failure-retry rates. Include a sensitivity case for a long tool call and for a burst of concurrent sessions. Recheck all rates and regional availability immediately before procurement because prices and service names change.
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Availability and release state can invalidate an otherwise attractive architecture. Product labels, regions, quotas and preview or general-availability status are volatile across all three providers.
- Verify that the exact model, agent runtime, memory feature and tool connector are available in the region where regulated data must remain.
- Check quotas for concurrent runs, context size, tool calls, deployment count, session storage and evaluation jobs.
- Record whether each dependency is preview or generally available, what support commitment applies, and how breaking changes are announced.
- Confirm data-residency, retention and cross-region failover behavior independently for model inference, memory, traces and tool execution.
- Plan an exit path for preview features: a customer-owned loop, an alternative runtime or a replaceable adapter around the provider API.
A service that is available only in a distant region can add latency, data-transfer cost and legal complexity. Conversely, a managed feature in your primary region may be worth choosing even if another provider offers a longer feature list elsewhere.
Which platform fits an existing cloud estate?
The lowest-risk choice is usually the provider that already owns your identity, network, logging and operational skills—unless model or framework requirements clearly outweigh that advantage.
| Existing situation | Likely starting point | Why | Check before committing |
|---|---|---|---|
| Google Cloud estate and a Python ADK team | Google managed Agent Runtime or ADK on Cloud Run | Fewer cross-cloud identity and networking seams, with a path from managed to code-first operation | Required regions, non-Google models, state semantics and GKE-level controls if the workload grows more complex |
| AWS estate with several agent frameworks | AgentCore modules, using Harness or Runtime as appropriate | Modular services and documented framework support let the team keep more of its existing code | IAM design, external model latency, Gateway and observability charges, and the exact boundary between your loop and managed modules |
| Azure estate centered on Foundry tooling | Prompt agents for configuration-led work or hosted agents for customer code | Managed publishing, identity and tracing align with the existing Azure operating model | Container compute, state ownership, model availability and whether direct Responses API use is needed for ephemeral runs |
| Multi-cloud or portability-first team | Keep orchestration and business state in portable code; use provider services selectively | Reduces dependence on a single managed loop | Adapter effort for identity, tool schemas, tracing, memory and model-specific behavior |
How should a team run a fair proof of concept?
Use one representative agent and identical success criteria. A catalog demo can hide the operational work that determines the long-term result.
- Define the workload contract. Write down the user goal, allowed tools, data classifications, maximum run time, concurrency, expected completion rate and what constitutes an irreversible action.
- Choose the loop boundary. Implement the same behavior once with a managed orchestration path and once with customer-owned code where each provider supports both. Record which retries, state transitions and safety checks you had to surrender or rebuild.
- Pin model and framework versions. Use the models and framework versions you would actually approve, and note any provider-specific prompt, tool-schema or structured-output changes.
- Exercise failure recovery. Inject timeouts, malformed tool responses, revoked credentials, duplicate events and interrupted sessions. Verify whether a run resumes safely and whether an operator can identify the failed transition.
- Measure the full billable path. Capture model tokens, tool calls, CPU, memory, session events, memory operations, container time, tracing volume and network use. Separate successful and retried runs.
- Test governance. Confirm least-privilege identities, private connectivity, tenant isolation, redaction and retention for prompts, memory and traces.
- Document an exit plan. List provider-specific APIs and decide what would be replaced if the service, region, price or release status changed.
What is the practical decision?
Choose Google when its managed lifecycle, ADK and runtime options fit your team and you value a path from low-code development to Cloud Run or GKE-level control. Choose AWS when modularity, external-model support and the ability to keep orchestration code in AgentCore Runtime matter more than having one integrated abstraction. Choose Microsoft when prompt agents, hosted customer code and Foundry’s identity, tracing and publishing workflow align with an Azure estate.
For any provider, keep transactional business state outside the model, make tool permissions explicit and price the complete execution path. The strongest architecture is the one whose loop ownership, runtime, governance and failure recovery you can demonstrate with your own workload—not the one with the longest feature list.
Frequently Asked Questions
Can Google ADK agents run on AWS AgentCore?
AWS documentation lists Google ADK among the frameworks AgentCore can support. You still need to adapt identity, networking, observability and any provider-specific memory or tool integrations, so framework portability does not mean zero migration work.
Are a prompt agent and a managed orchestration loop the same thing?
No. Both reduce application code, but each provider defines the boundary differently: a prompt agent is a configuration-led Microsoft resource, while AWS Harness is a configurable orchestration component that invokes other AgentCore services. Compare the actual controls over retries, state and tool dispatch.
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What should be kept outside an AI agent’s memory?
Authoritative records such as orders, permissions, balances and approvals should remain in transactional systems with normal access controls. Agent memory can hold conversational context or derived preferences, but the agent should retrieve and verify business facts rather than treating remembered text as the source of truth.
How can a team avoid being trapped by a preview agent feature?
Wrap provider APIs behind an internal interface, version prompts and tool schemas, and keep a customer-owned orchestration path for critical actions. Track regional availability, quota and release status in change management so a preview dependency has an explicit replacement plan.
Quick Recap
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