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Use Google’s managed Agent Runtime when a Python agent, integrated sessions and memory, and minimal infrastructure work meet your needs. Choose Cloud Run when you need container and language flexibility or event-driven HTTP services. Choose GKE when complex stateful behavior or fine-grained infrastructure control justifies Kubernetes operations. “Vertex AI Agent Builder” is no longer the whole comparison: Google now presents Gemini Enterprise Agent Platform as the evolution of Vertex AI, with Agent Studio, the Managed Agents API, and the Agent Development Kit (ADK) for building agents, plus several deployment choices. The right decision depends on the builder’s skill set, runtime control, state model, governance requirements, region, limits, and the actual cost of operating your workload—not on a universal product winner.
What is Google’s agent infrastructure now?
Google’s current umbrella is Gemini Enterprise Agent Platform, which Google describes as a unified environment for building, deploying, governing, and optimizing enterprise agents and other model-based applications. The platform is organized around four lifecycle areas:
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- Build: Agent Development Kit, Agent Studio, Agent Garden, Model Garden, RAG Engine, Vector Search, and the Managed Agents API.
- Scale: Agent Runtime, Sessions, Memory Bank, and Code Execution.
- Govern: agent identity and gateway capabilities for controlled access to tools and data.
- Optimize: evaluation and observability for testing behavior and monitoring production use.
That means “Google’s agent infrastructure” is not one service that competes directly with Agent Builder. It is a platform story containing separate development, runtime, governance, and optimization layers. A useful comparison therefore has two decisions: how you will build the agent, and where its code and tools will run.
Google’s documentation and announcements describe access to more than 200 foundation models in the catalog. That is a catalog-size claim from Google Cloud in 2026, not a benchmark of agent quality, latency, reliability, or cost.
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Is Vertex AI Agent Builder still available?
Google announced on April 22, 2026 that Gemini Enterprise Agent Platform is the evolution of Vertex AI and that Vertex AI services and roadmap evolutions would be delivered through Agent Platform rather than as a standalone service. Google’s Cloud site labels the current product “Gemini Enterprise Agent Platform (formerly Vertex AI),” while a Vertex AI Agent Builder documentation landing page remains accessible.
“Moving forward, all Vertex AI services and roadmap evolutions will be delivered exclusively through the Agent Platform, rather than as a standalone service, to power the next generation of agent development.” — Michael Gerstenhaber, vice president of Product Management, Gemini, Google Cloud, April 22, 2026
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The practical interpretation is current umbrella naming plus retained legacy documentation. Do not assume that every old API, feature, region, quota, or migration path has identical treatment. Check the documentation and release notes for the exact component you use before changing a production design.
What is the difference between Agent Builder, Agent Platform, and the runtime services?
These names refer to different layers, so treating them as interchangeable leads to an incorrect architecture decision.
| Layer | Google product or path | What it does | What it does not decide |
|---|---|---|---|
| Platform umbrella | Gemini Enterprise Agent Platform | Combines agent development, deployment, governance, evaluation, observability, sessions, memory, and related model and retrieval services. | It is not a single hosting option with one fixed operational model. |
| Low-code development | Agent Studio | Visual workspace for discovering models, engineering prompts, and assembling agents without writing code. | It is not a replacement for every custom runtime or infrastructure choice. |
| Managed code/configuration | Managed Agents API | Configuration-driven REST APIs and a managed sandbox, including Agents and Interactions APIs. | It does not give the same code-level orchestration freedom as a custom framework. |
| Custom development | Agent Development Kit (ADK) | Code framework for complex agents, tool use, multi-agent orchestration, and environment simulation. | It does not by itself determine whether deployment belongs on Agent Runtime, Cloud Run, or GKE. |
| Managed runtime | Agent Runtime | Runs supported agents with automatic scaling and integration with managed sessions and memory options. | It is more opinionated than a container platform and does not host custom MCP servers. |
| Container platform | Cloud Run | Runs containerized agents, components, or custom tools as scalable HTTP endpoints. | Instances are stateless; application state must be stored elsewhere. |
| Kubernetes platform | Google Kubernetes Engine (GKE) | Runs containerized, complex, or stateful systems with fine-grained infrastructure configuration. | Your team takes on Kubernetes deployment and operations. |
Which Google development path should you use?
Choose the development path based on who is building the agent and how much logic must be expressed in code. These paths can coexist with different runtime choices.
Agent Studio: fastest route for visual prototypes
Agent Studio is Google’s low-code workspace. A team can discover available models, iterate on prompts, and build an agent without writing an application framework. It fits rapid prototypes, demonstrations, and business-led agents where the main work is prompt and tool configuration rather than bespoke orchestration.
Studio becomes less suitable when you need extensive source-code review, custom scheduling, unusual networking, elaborate test harnesses, or a framework that your engineering team already uses. In those cases, treat Studio as a prototype or design surface and move the production implementation to a more controllable path if required.
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Managed Agents API: configuration with a managed sandbox
The Managed Agents API exposes a configuration-driven REST model and a managed sandbox. Its Agents API handles configuration, while the Interactions API handles runtime conversations and actions. This is a middle ground for teams that want an API-oriented, managed experience without owning a complete application runtime.
It is a reasonable fit when repeatable configuration and Google-managed execution matter more than complete control over the process. Confirm the supported tools, language behavior, authentication model, quotas, and regional availability for your use case before committing to it.
Agent Development Kit: code-level control for complex behavior
ADK is the custom-code path for multi-step logic, tool integrations, multi-agent orchestration, and simulated environments. It gives developers a place to express routing, state transitions, retries, validation, and tests explicitly. ADK code still needs a deployment target; it is not itself equivalent to Agent Runtime, Cloud Run, or GKE.
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Should you use Agent Runtime, Cloud Run, or GKE?
Google’s architecture guidance maps the runtime to workload characteristics rather than declaring one best service.
| Requirement | Best starting point | Why | Main trade-off |
|---|---|---|---|
| Python agent with low operational overhead and a fully managed experience | Agent Runtime | Google manages the runtime, automatic scaling is provided, and managed session and memory options are integrated. | The environment is opinionated. Private dependency-management and security requirements can make Cloud Run or GKE a better fit. |
| Containerized, event-driven service with language flexibility | Cloud Run | You can deploy the entire agent, individual components, or custom tools as scalable HTTP endpoints. | Instances are stateless, so durable state cannot live only in process memory. You also own more application-level integration work. |
| Complex stateful workload requiring fine-grained infrastructure configuration | GKE | Kubernetes gives direct control over networking, scheduling, storage, sidecars, and other infrastructure details. | Your team is responsible for Kubernetes operations, upgrades, security hardening, capacity, and incident response. |
When Agent Runtime is the practical choice
Start with Agent Runtime when your agent is Python-based and the priority is reducing platform operations. Automatic scaling and integrated Sessions and Memory Bank can remove a substantial amount of glue code. The managed path is especially attractive for a small platform team that wants to focus on agent behavior, tools, evaluation, and governance instead of maintaining a serving stack.
Agent Runtime supports MCP and A2A orchestration, according to Google’s architecture guidance, but that guidance says it does not host custom MCP servers. If your design requires you to operate your own MCP server, place that server on a platform that supports it, such as Cloud Run, and connect the agent to it.
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Review private dependency requirements carefully. If packages must be retrieved through a tightly controlled private repository or need networking and build behavior outside the managed runtime’s assumptions, the operational simplicity may not outweigh the security and dependency constraints.
When Cloud Run is the practical choice
Cloud Run is the flexible container option. It can host a complete agent, a specialized tool, an MCP client or server, or an event-driven HTTP component. Google’s guidance notes that Cloud Run can host MCP clients and servers using streamable HTTP transport.
Design around stateless instances. Put conversation records, checkpoints, user preferences, files, and locks in durable external services rather than relying on local memory. This does not prevent stateful applications; it means the state layer must be explicit and independently scalable.
Cloud Run is often the sensible boundary when different components use different languages, when you need a custom base image, or when an existing containerized service should become an agent tool. You gain portability and control over the container, but you must integrate identity, persistence, tracing, retries, and policy enforcement deliberately.
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GKE is justified when the agent is part of a complex, stateful system and the team needs Kubernetes-level control. Examples include tightly coordinated services, specialized scheduling, custom networking, persistent volumes, sidecars, or organization-wide Kubernetes policy and observability.
That control carries an operational bill: cluster lifecycle, node and workload security, autoscaling configuration, upgrades, reliability engineering, and on-call ownership. Do not choose GKE merely because an agent may eventually become complex. Choose it when the requirements already demand infrastructure control that a managed runtime or container service cannot provide.
How should you decide? A step-by-step architecture process
Use the following sequence before selecting a product name or writing a deployment manifest.
- Identify the builders. If business users or prototype teams own the first version, evaluate Agent Studio. If developers need custom orchestration, tools, and tests, evaluate ADK. If a configuration-driven REST interface and managed sandbox are sufficient, evaluate the Managed Agents API.
- List non-negotiable runtime controls. Record required languages, base images, private package repositories, network paths, identity providers, custom protocols, background workers, and deployment controls. A Python-only, low-operations workload points toward Agent Runtime; language or container constraints point toward Cloud Run; deep infrastructure requirements point toward GKE.
- Draw the state and memory boundary. Separate short-lived interaction context from durable user or task memory. Agent Platform provides Sessions for interaction context and Memory Bank for longer-term memory. On Cloud Run, specify the external datastore or service that persists state. On GKE, document which stateful services, volumes, and backup procedures the cluster will run.
- Map tools and protocols. Decide whether the agent is an MCP client, whether you must host an MCP server, and whether agents communicate through A2A orchestration. Agent Runtime can participate in MCP and A2A orchestration but is not the place to host a custom MCP server; Cloud Run can host MCP clients and servers over streamable HTTP.
- Define governance before launch. Review agent identity, gateway controls, tool permissions, data access, secrets, audit requirements, evaluation, and observability. Integrated platform controls may reduce implementation work, while a self-managed container or cluster gives you responsibility for assembling equivalent controls.
- Estimate the whole operating cost. Include model calls, runtime compute, storage, network traffic, logging, evaluation, support, engineering time, and on-call work. No official source reviewed establishes a universal price winner among these runtimes.
- Validate region and limits. Confirm the exact services, models, quotas, session and memory features, protocol support, and data-location requirements in the target region. Availability can differ by service and can change during the Vertex AI transition.
- Run a bounded production trial. Test representative tool failures, long conversations, retries, concurrent users, cold starts, dependency installation, policy denials, and rollback. Measure your own latency, reliability, and operations effort instead of borrowing an unsupported benchmark.
How do sessions, memory, and state differ across the choices?
“Memory” can mean at least three things: the context sent with one interaction, the history of a session, and durable information retained for later tasks. Keep those layers separate in the design.
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- Memory Bank: Agent Platform provides a longer-term memory capability for information intended to persist beyond one interaction.
- Application state: Business records, workflow checkpoints, idempotency keys, files, and permissions still need a durable system appropriate to the data.
Agent Runtime integrates with managed session and memory options. Cloud Run does not preserve state in an instance reliably; persist it externally and make requests safe to retry. GKE can run stateful components or connect to managed data services, but Kubernetes does not remove the need to design backups, consistency, failover, and data governance.
What governance and operations should you compare?
For an enterprise agent, hosting is only one part of the control plane. Compare how each architecture handles identity, tool access, data boundaries, evaluation, and observability.
Identity and tool access
Use distinct identities for the agent, tools, data stores, and human operators where possible. Apply least privilege to every tool and make authorization decisions outside the model’s natural-language output. Agent Platform includes identity and gateway capabilities; with Cloud Run or GKE, you must connect those controls to your container services and internal gateways.
Evaluation and observability
Define test cases for correct tool selection, refusal behavior, data leakage, prompt-injection resistance, latency, and recovery from tool errors. Agent Platform’s optimization area includes evaluation and observability features. A self-managed deployment can use its existing telemetry stack, but instrumenting prompts, tool calls, traces, costs, and redacted outputs becomes your responsibility.
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Agents are sensitive to model, prompt, tool-schema, and dependency changes. Version prompts and tool contracts, use staged rollouts, retain a rollback path, and record which model and policy produced each important action. Managed infrastructure reduces host maintenance; it does not eliminate application testing or incident response.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the cost, region, and limit considerations?
There is no defensible universal cost winner in the available official material. A managed runtime may reduce engineering and on-call time while charging for platform usage; Cloud Run and GKE may fit an existing container estate but add persistence, networking, observability, or cluster-management work. Model calls and retrieval can dominate the bill regardless of runtime.
Best Value
Before selecting an architecture, obtain current pricing for the exact model, region, runtime, storage, network, logging, and evaluation services. Confirm whether a quoted allowance is recurring, promotional, per-project, or one-time. Also verify quotas and concurrency limits for the APIs and protocols you plan to use. Google’s product naming and capabilities are changing during the announced transition, so check current documentation immediately before production approval.
What migration approach makes sense for an existing Vertex AI Agent Builder project?
Begin with an inventory rather than a wholesale rewrite. Record the project’s agent definitions, prompts, tools, data connections, identity bindings, regions, quotas, evaluation sets, and deployment target. Then map each part to the current Agent Platform terminology: Studio, Managed Agents API, ADK, Agent Runtime, Sessions, Memory Bank, and governance or optimization services.
- Capture a known-good test set of conversations and tool outcomes.
- Check the current migration and release documentation for every API and feature you use.
- Recreate or import the smallest representative agent in the target path.
- Compare tool behavior, permissions, session continuity, logging, latency, and cost in a non-production project.
- Run both versions long enough to detect regional, quota, and dependency differences.
- Switch traffic gradually and retain a rollback route until the new path meets your acceptance criteria.
The existence of a legacy Agent Builder documentation page does not, by itself, guarantee identical future support for every feature. Treat each migration as a component-by-component compatibility exercise.
Which architecture fits common scenarios?
| Scenario | Recommended starting architecture | Reasoning | Check before committing |
|---|---|---|---|
| Business team prototyping an internal assistant | Agent Studio, then a managed deployment | Visual prompt and model iteration minimizes initial code. | How the prototype will be exported, governed, tested, and handed to engineering. |
| Python support agent with managed conversation context | ADK or Managed Agents API with Agent Runtime | Combines code or configuration with managed scaling, sessions, and memory options. | Private dependency requirements, supported tools, and regional service availability. |
| Polyglot agent with custom MCP services | Containers on Cloud Run | Runs components and MCP clients or servers as HTTP services with language flexibility. | External persistence, authentication, retries, and streamable HTTP behavior. |
| Workflow platform with complex state and Kubernetes standards | ADK or custom services on GKE | Provides fine-grained control over stateful services, networking, and scheduling. | Cluster operations, backup, upgrades, security, and whether the control is truly necessary. |
| Regulated enterprise with centralized platform controls | Agent Platform services where they satisfy policy; otherwise a governed container design | Integrated identity, gateway, evaluation, and observability may simplify oversight. | Data location, audit detail, model approval, retention, and escape paths for unsupported features. |
What are the common selection mistakes?
- Comparing unlike layers: Agent Builder, ADK, Agent Runtime, Cloud Run, and GKE are not equivalent products. Separate build decisions from hosting decisions.
- Assuming managed means no engineering: Prompts, tools, permissions, evaluations, retries, and data handling still require application design.
- Keeping state in a container: Cloud Run instances are stateless. Persist session and business data outside the instance.
- Choosing Kubernetes for prestige: GKE’s control is valuable only when the workload needs it. Otherwise its operational obligations can exceed the benefit.
- Ignoring private dependencies: Agent Runtime’s managed model may not fit strict private-package or network requirements; test those constraints early.
- Treating the model catalog as a quality score: Google’s figure of more than 200 foundation models says nothing by itself about your agent’s accuracy or cost.
- Planning from stale names: During the Vertex AI to Agent Platform transition, verify current labels, APIs, regions, and migration notes for each component.
- Inventing a price or performance winner: Official material does not provide a directly comparable runtime benchmark or universal cost result. Measure the architecture you will actually operate.
What is the bottom-line recommendation?
Use the managed Agent Runtime path when a Python agent and Google’s integrated lifecycle services satisfy your requirements and reducing operations is the priority. Use Cloud Run when containerized, event-driven, polyglot flexibility or custom MCP hosting matters. Use GKE when complex stateful behavior and infrastructure control are important enough to justify Kubernetes ownership.
Choose Agent Studio, the Managed Agents API, or ADK separately according to how the agent is built. Keep Gemini Enterprise Agent Platform in the overall architecture discussion because it supplies the surrounding sessions, memory, governance, evaluation, and observability capabilities. Recheck current pricing, regions, limits, and migration guidance for the exact configuration before production.
Frequently Asked Questions
Can I use ADK without deploying to Agent Runtime?
Yes. ADK is a development framework, not a mandatory hosting service. You can package an ADK agent for another supported environment, such as Cloud Run or GKE, provided you implement the required networking, identity, persistence, and observability integrations.
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Does Cloud Run make an agent stateful?
No. Cloud Run can support a stateful application, but its instances should be treated as disposable and stateless. Store durable conversation, workflow, and business data in an external persistence layer and design requests to tolerate retries.
Does Agent Runtime host my custom MCP server?
Google’s architecture guidance describes Agent Runtime as supporting MCP and A2A orchestration but says it does not host custom MCP servers. Run a required custom server on a suitable service, such as Cloud Run, and verify current protocol and authentication support.
How many models does Gemini Enterprise Agent Platform include?
Google Cloud stated in 2026 that the platform provides access to more than 200 foundation models. That is a catalog count, not a promise that every model is available in every region or that all models have identical limits, pricing, or agent features.
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