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AI hosting is two decisions, not one. You choose where the agent application runs and who operates that runtime, and separately choose where the model is served and who manages inference. An agent can run in a managed service while calling a model in another cloud, a private Kubernetes cluster, or an on-premises GPU server.
This separation lets you match operations, security, latency, data residency, hardware and cost to the workload instead of assuming that “cloud AI” or “self-hosted AI” describes the whole system.
What “AI hosting” includes
An agentic application is more than a prompt sent to a model. It may maintain a conversation, plan several actions, call tools, retrieve memory, delegate to another agent, execute code, and resume after a long-running job. Hosting therefore covers the runtime that coordinates those activities as well as the service that performs model inference.
Microsoft’s agent architecture guidance and Google Cloud’s architecture guidance describe these as separate layers. The agent runtime executes your application code and orchestration. The model runtime loads a model, accepts inference requests and returns tokens or other model outputs. They can be operated by the same provider, by different providers, or by your own team.
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Agent or application runtime
The agent runtime contains your business logic: prompt and policy handling, tool adapters, authentication, session management, memory retrieval, retries, scheduling and response streaming. It also needs ordinary application infrastructure such as networking, secrets, logs, metrics, deployment pipelines and durable storage.
Model runtime
The model runtime supplies intelligence through an inference endpoint. It may be a managed API, a model server in a container, a Kubernetes deployment with GPUs or TPUs, an on-premises cluster, or a service in another cloud. A model runtime can serve a general model, a fine-tuned model or several models behind a routing layer.
Protocols are not hosting
The Model Context Protocol (MCP) can standardize how an agent discovers and calls tools or data sources. The Agent2Agent (A2A) protocol can support communication between agents. Both improve interoperability, but neither decides who operates the runtime, where state is stored, where inference occurs or which permissions an agent receives.
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How an agent request moves through the system
- Ingress and identity: An API gateway, web application or another agent authenticates the caller and applies rate limits. The gateway may attach a tenant, user and authorization context.
- Agent scheduling: The agent runtime creates or resumes a session, loads conversation state and selects the workflow or agent version that should handle the request.
- Model inference: The runtime sends a prompt, tool definitions and relevant context to a model endpoint. That endpoint can be inside the same network or reached through a secured private connection.
- Planning and tool use: The model may request a search, database query, API call, browser action or code execution. The agent runtime checks policy and authorization before invoking the tool.
- Additional turns: Tool results, memory retrievals and observations may trigger more model calls. The runtime records intermediate state so a transient failure does not necessarily lose the task.
- Completion and persistence: The runtime returns a result or job handle to the caller and persists the conversation, audit events and any durable artifacts according to retention policy.
This sequence explains why a GPU alone is not an agent platform. Routing, identity, policy, state, isolation, observability and recovery can dominate the engineering work even when model serving is available as an API.
Managed hosting versus self-hosting
A managed service reduces the infrastructure your team operates. Self-hosting gives you more control but transfers responsibility for the surrounding platform to your team. Neither is automatically cheaper or more secure; the correct choice depends on the controls and workload you must support.
| Decision axis | Managed runtime or model service | Self-hosted or highly configurable infrastructure |
|---|---|---|
| Operations | The provider operates more of the runtime, scaling and underlying infrastructure. | Your team owns deployment and more surrounding services, depending on the design. |
| Control | Managed defaults reduce engineering effort when they meet your requirements. | You can control the application environment, network path, model server and integration with existing systems. |
| Security and residency | You must verify the provider’s controls, service scope, processing terms and available regions. | A model or agent can remain inside your organization’s network when that is required, but your team must implement and operate the controls. |
| Traffic and cost | Request-based or pay-per-use billing can align cost with intermittent traffic; current pricing and quotas must be checked for the selected service. | Reserved capacity can suit predictable, high utilization, while idle hardware, maintenance and staffing can offset that benefit. |
| Stateful workloads | Some managed runtimes provide sessions, memory, observability and sandbox features. | You can design exactly how state, queues, sandboxes and recovery work, with the corresponding operations burden. |
| Specialized hardware | Availability depends on the provider’s supported models, accelerators and regions. | You can select hardware and serving software directly, subject to procurement, capacity and operational expertise. |
| Interoperability | Built-in MCP or A2A support can simplify connections to tools and other agents. | You choose protocol implementations and must maintain compatibility, authentication and network connectivity. |
Common deployment patterns
Managed agent runtime with a managed model API
This is the shortest path from application code to a production agent. The provider handles much of the container execution, scaling, session lifecycle and platform integration, while a managed model API handles inference. It suits teams that want to focus on prompts, tools and product behavior rather than cluster operations.
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Confirm the service’s region, data handling, maximum execution time, concurrency model, supported tools, log retention and model availability before committing. A managed label does not mean every dependency is managed: you may still need to operate a database, queue, vector index, secret store and external tool integrations.
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A serverless container platform, such as Google Cloud Run in Google’s documented architecture, packages the agent as a container while the platform manages most host provisioning and scale-to-zero behavior. It is useful for HTTP-oriented agents with variable traffic and for teams comfortable owning the container image and application-level state.
Design around request timeouts, background jobs and cold starts. Long tasks should usually be placed on a durable queue with a worker or job mechanism rather than held open in a user request. Keep session state in durable storage instead of the container’s local filesystem, which may disappear when an instance is replaced.
Kubernetes or another cluster
Google’s architecture presents GKE as an option when you need greater infrastructure control, specialized hardware or network-contained model serving. The same reasoning applies to other Kubernetes environments. A cluster lets you tune scheduling, node pools, GPUs, network policies and deployment strategies, but your team must operate upgrades, autoscaling, ingress, identity integration, observability and disaster recovery.
Use a cluster when those controls solve a real requirement. Running Kubernetes solely to avoid a managed bill can create a larger total cost through platform engineering, on-call coverage and underused capacity.
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Serving a model inside your organization’s network can address strict data-residency, connectivity or isolation requirements. It also gives direct control over model files, accelerator choice and network paths. Plan for hardware procurement, driver and runtime compatibility, model updates, capacity forecasting, physical resilience and the security of the inference endpoint.
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Hybrid and multi-environment serving
An agent runtime and model runtime do not have to share a location. A unified inference frontend can route requests to model replicas on an agent platform, GKE, Cloud Run, on-premises infrastructure or another cloud. Google’s inference-routing architecture describes model-aware routing and GPU/TPU metrics for its GKE path. Such a design can keep sensitive workloads private while using external capacity for less restricted traffic, but it adds routing policy, network latency and operational complexity.
Why agentic workloads change the hosting decision
Traditional request-response applications often finish in one short transaction. Agents may run for minutes or hours, retain context, call many tools and coordinate with other agents. Hosting decisions must account for the complete execution rather than only the first model response.
“Agents reason, not only respond: A single user request might trigger multiple LLM inference calls, tool invocations, memory retrievals, and inter-agent communications, each adding latency, cost, and failure surface.” — AWS Well-Architected Agentic AI Lens
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Long-running execution
Use a job record and queue for work that can outlive an HTTP request. Store checkpoints after meaningful steps so a worker can resume after a process restart. Define maximum runtime, cancellation behavior and an owner for abandoned jobs.
Persistent context and memory
Separate ephemeral working memory from durable conversation history and business records. Encrypt stored context, define retention periods and provide deletion mechanisms. Retrieval systems need tenant isolation and filters that prevent one customer’s documents from entering another customer’s prompt.
Tool permissions and identity
Give each tool a narrowly scoped identity instead of the agent a broad administrator credential. Authorize every call with the user, tenant, purpose and requested operation in context. Record the tool name, arguments, result class and approval decision without logging secrets or unnecessary personal data.
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Isolation and sandboxing
Agents that execute code or control a browser need a sandbox with restricted network access, filesystem boundaries, resource limits and a disposable environment. Isolate concurrent agent instances so one session cannot read another session’s files, environment variables or browser profile.
Observability and recovery
Trace a request across model calls, tool calls, queues and storage. Capture latency, token usage, retries, policy denials and failure reasons. Set separate budgets for model calls and tool calls, and make retries idempotent. A circuit breaker or fallback model can prevent a failing dependency from multiplying costs.
Choosing where to host an AI agent
Answer these questions before selecting a product or architecture:
- Who will operate it? If your team lacks 24-hour platform coverage, favor a managed runtime or reduce the number of self-managed components.
- What data may leave your network? Identify personal, regulated, confidential and export-controlled data. Verify processing regions, retention, encryption and provider access for every model and tool.
- How variable is traffic? Bursty or experimental workloads often benefit from request-based scaling. Predictable, high utilization may justify reserved capacity, but calculate staffing and maintenance as well as hardware.
- What latency is acceptable? Measure the complete workflow, including network hops, model queueing, tool calls and cold starts. A faster model endpoint cannot compensate for a slow database or serial tool chain.
- Do you need a particular model or accelerator? Check model availability, context limits, quantization support, GPU or TPU type and upgrade policy in the target region.
- How long can a task run? Match the runtime’s timeout and background-job capabilities to the longest workflow, not the average chat turn.
- Which integrations are mandatory? Map private databases, identity providers, internal APIs, MCP servers and A2A peers. Confirm that network paths and authentication methods are supported.
- What failure is acceptable? Define whether a task can retry, pause for approval, fall back to another model or return a partial result.
A practical architecture-planning procedure
- Draw the boundary: Place the agent runtime, model endpoints, memory stores, tools, queues and users on one diagram. Mark every trust boundary and data flow.
- Classify data: Label prompts, retrieved documents, tool arguments, outputs and logs by sensitivity and residency requirement.
- Choose an initial operating model: Select managed, serverless container, cluster, on-premises or hybrid for each layer independently. Do not force the model and agent into the same environment.
- Define state: Specify where sessions, checkpoints, memories, artifacts and audit records live, how they are encrypted and when they expire.
- Set policy gates: Require authentication, authorization and validation before every external action. Add human approval for irreversible, high-value or regulated operations.
- Design limits: Set maximum steps, model calls, tokens, tool time, concurrency and spend per request, user and tenant.
- Instrument before launch: Create traces and dashboards for end-to-end latency, model latency, tool errors, queue depth, retries, policy blocks and cost signals.
- Test failure paths: Kill workers, delay model responses, revoke credentials, return malformed tool data and exhaust quotas. Verify that sessions resume safely and that partial work is visible.
- Review capacity and exit options: Document how to move the model, runtime or data to another environment if pricing, region availability or policy changes.
Provider examples and what they illustrate
The following are documented architectural examples, not independent performance tests or rankings. Product names, capabilities, prices, regions and lifecycle status can change.
| Provider documentation example | Layer illustrated | Operational implication |
|---|---|---|
| Microsoft Foundry Hosted Agents | Managed agent runtime | The platform runs the container and provides scaling, session lifecycle and platform integration. The documentation describes Hosted Agents as generally available; verify current status before deployment. |
| Microsoft self-hosting guidance | Customer-operated agent runtime | The application team is responsible for routing, identity, authorization, request policy, storage, deployment and scaling. The documented Python self-hosting packages are described as prerelease, so check the current release state. |
| Google Cloud Agent Runtime | Managed agent runtime | Managed execution reduces infrastructure work while leaving application, data and policy decisions with the team. |
| Google Cloud Run | Serverless container runtime | You package the agent as a container and design around request limits, background work, stateless instances and external durable storage. |
| Google Kubernetes Engine | Configurable agent or model infrastructure | Greater control supports specialized hardware and network-contained serving, with cluster operations and capacity planning required. |
| AWS AgentCore capabilities | Supporting agent infrastructure | AWS describes runtime, gateway, memory, identity, observability, evaluations and policy as distinct capabilities around agent execution. Vendor documentation should not be treated as neutral performance evidence. |
Protocols, access and the agentic web
MCP can expose tools and data to an agent through a consistent interface, while A2A can let agents discover and communicate with one another. Adopt them when they reduce integration work, but keep hosting, identity and authorization decisions explicit. A protocol connection does not grant permission to a website, database or third-party service.
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Google’s agentic AI use guidance prohibits unauthorized access and deceptive behavior, including attempts to circumvent third-party restrictions on crawling, scraping or automation. Implement robots, terms-of-service, rate-limit and consent requirements as policy controls rather than assuming an agent is entitled to automate any reachable endpoint.
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A July 2026 arXiv preprint argues that an agentic web also needs normative infrastructure: laws, norms and practices governing delegated access. That is an argument by the paper’s authors, not settled law or a universal industry consensus. In practical terms, keep a human or organizational owner accountable for the actions delegated to an agent.
Cost and capacity without misleading shortcuts
Model spending is only one part of the bill. A single user request can produce multiple inference calls, retrieval operations, tool requests, storage writes and observability events. Managed services may charge by request, execution time or tokens; self-hosted systems add hardware, electricity, support contracts, software maintenance and on-call labor. Verify current prices and quotas for the exact region and service instead of reusing an old estimate.
Build a per-workflow cost model with these inputs:
- Number of model calls and input/output tokens per successful task.
- Retry, fallback and failed-tool rates.
- Average and peak concurrency, execution duration and queue time.
- GPU or CPU capacity, utilization and idle periods.
- Storage, network egress, logs, traces and retained conversation data.
- Engineering and operational coverage needed to maintain the platform.
Then set budgets and alerts before exposing the agent to untrusted or high-volume callers. Self-hosting is not automatically cheaper, and managed hosting is not automatically more expensive; utilization and operational ownership determine the result.
Failure modes and recovery checks
The model is reachable but the agent stalls
Inspect tool latency, queue depth, session locks and memory-store timeouts. Add per-step deadlines and resume from a checkpoint instead of retrying the entire workflow.
Requests fail only under load
Check concurrency limits, model quotas, connection pools and accelerator saturation. Increase capacity or apply admission control; do not simply raise every timeout.
Context leaks between users
Verify tenant keys on every read and write, isolate caches, rotate credentials and run adversarial tests that attempt cross-tenant retrieval. Treat logs and traces as sensitive copies of the same data.
Tool calls perform unintended actions
Use least-privilege identities, schema validation, allowlists and approval gates. Make destructive operations explicit and idempotent where possible, and retain an audit trail of the decision and actor.
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Keep model and runtime adapters behind your own interfaces, export durable state in portable formats and document a fallback environment. Recheck availability, pricing, service limits and lifecycle announcements before a production migration.
Bottom line
Host the agent runtime where your team can meet its requirements for state, security, operations and integrations. Host the model where its data-residency, latency, hardware, availability and cost requirements are satisfied. Those locations can be the same, but they do not have to be. Treat MCP and A2A as communication mechanisms, not as substitutes for an operating model, and evaluate the complete multi-step workflow rather than counting GPUs or model calls in isolation.
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.
