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AI hosting is two separate decisions: where the agent application runs and where the model performs inference. A managed agent runtime can operate containers, scaling, sessions, identity integrations, and observability for you, while a self-hosted runtime gives your team deeper control over networks, code, data, and hardware. The model can be supplied by a hosted API, run in your own container or cluster, or be split across several environments. Choose each layer independently, then connect them with explicit security, state, and reliability controls.
What “AI hosting” actually includes
An AI agent is more than a prompt sent to a language model. Its application code decides when to reason, which tools to call, what context to retrieve, how to authenticate, and how to recover from an error. Hosting that application means operating the runtime around the model.
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Model serving is a separate layer. It is the endpoint that loads model weights, accepts inference requests, applies limits and routing, and returns tokens or structured outputs. An agent hosted in one environment can call a model hosted somewhere else.
The Tool Desk
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|---|---|---|
| Agent runtime | Application code, orchestration loops, tool adapters, session handling, memory calls, policy checks, and response formatting | Container execution, deployment, scaling, identity, authorization, state, networking, observability, retries, and isolation |
| Model runtime | Inference service that loads and executes a model | Model and hardware availability, GPU or TPU capacity, batching, latency, throughput, versioning, quotas, and data handling |
Microsoft’s architecture guidance treats the operator of agent infrastructure as a choice separate from the protocol a client uses to reach an agent. Google’s architecture guidance makes the same distinction by separating the agent runtime from the model runtime. That separation lets you change a model provider without rewriting the agent, or move the agent application while keeping model serving in place.
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Why agentic workloads change the hosting decision
A conventional web request often has a short, predictable lifetime. An agent may perform several model calls, retrieve memory, invoke tools, wait for an external system, and ask another agent for help before returning an answer. AWS describes the consequence precisely: “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.”
Some agents run autonomously for hours, retain a conversation or task state, and execute code or browser actions. A useful hosting comparison therefore examines more than CPU, memory, or GPU type.
- State: Decide which context is ephemeral, which must survive restarts, and where conversation and task records are encrypted.
- Isolation: Separate users, tenants, and concurrent agent instances so one task cannot read another task’s files, credentials, or memory.
- Authorization: Enforce permissions for every tool and data source, not just at the user-interface boundary.
- Failure handling: Bound retries, make tool calls idempotent where possible, persist checkpoints, and provide a way to resume or cancel long jobs.
- Observability: Record model calls, tool calls, latency, token usage, policy decisions, and errors with correlation identifiers.
- Cost-aware orchestration: Prevent loops and unnecessary retrievals, and choose models according to task complexity rather than using the most expensive model for every step.
Managed hosting versus self-hosting
A managed service operates more of the runtime and underlying infrastructure. Your team still owns application behavior, prompts, tool permissions, and data choices, but the provider may handle container execution, scaling, session lifecycle, platform integration, or the model API.
Self-hosting places more of the stack under your control. That can mean a container platform, a Kubernetes cluster, an on-premises environment, or a private network connected to a hosted model. It also means your team must design and operate the surrounding services.
| Decision axis | Managed runtime or model service | Self-hosted or highly configurable infrastructure |
|---|---|---|
| Operations | The provider operates more of the runtime and infrastructure. | Your team owns deployment and more surrounding services, depending on the design. |
| Control | Managed defaults reduce engineering and maintenance work when they fit your requirements. | You control the application environment, network paths, model placement, and integration with existing systems. |
| Security and residency | You must verify controls, service scope, data handling, and supported regions. | A model or agent can remain inside an organization’s network when policy requires it, but you must implement and maintain the controls. |
| Traffic and cost | Request-based or pay-per-use billing can match variable traffic; current pricing and quotas must be checked for the selected service. | Capacity planning, hardware utilization, upgrades, and operations become your responsibility. Self-hosting is not automatically cheaper. |
| Workload behavior | Some services provide scaling, sessions, memory integration, observability, and sandbox features. | Stateful, specialized, or tightly controlled workloads can be tailored more precisely, with a larger operations burden. |
| Interoperability | Protocol support can connect the runtime to tools and other agents. | You select and operate protocol servers, gateways, credentials, and network policies yourself. |
Managed deployment patterns
Managed agent runtime
A managed agent runtime is appropriate when you want to ship agent code without building a full control plane. The platform may provide container execution, automatic scaling, session lifecycle management, identity integration, logging, and model connectivity. Confirm which features are included rather than assuming that “managed” covers your database, tool servers, browser sandbox, or long-term memory.
Microsoft documentation describes Foundry Hosted Agents as a managed option that runs the container and handles scaling and session lifecycle. The same documentation contrasts it with self-hosting, where the application team must provide routing, identity, authorization, policy, storage, deployment, and scaling. Product availability and package maturity can change; verify current status before committing to a production design.
Serverless containers
A serverless container service, such as the Cloud Run pattern described by Google, lets you package the agent while avoiding direct management of a cluster. It suits stateless request handling and many bursty workloads. Long-running jobs, streaming responses, WebSocket behavior, local scratch space, and concurrency limits need explicit testing because defaults may not match an agent’s execution pattern.
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Hosted model API
A hosted model API removes model-weight management and accelerator provisioning from your team. You still need request budgets, timeout handling, privacy review, regional checks, model-version pinning, and a fallback strategy. Treat the API as a dependency with quotas and outages, not as an infinitely available function.
Self-hosted and configurable patterns
Kubernetes or a managed cluster
A Kubernetes environment gives you control over namespaces, network policy, workload placement, persistent volumes, autoscaling, and specialized hardware. Google’s guidance presents GKE as the more infrastructure-controlled option and notes its suitability for specialized hardware or network-contained serving. The trade-off is a larger surface area: upgrades, node health, GPU scheduling, secrets, ingress, autoscaling, and incident response all become part of your operating model.
On-premises model serving
Keeping inference inside your network can help satisfy strict data-residency or isolation requirements. It requires compatible hardware, model licensing, capacity planning, patching, monitoring, and a plan for model updates. Private serving can still call an externally hosted agent runtime, but every outbound data path and credential must be reviewed.
Hybrid agent and model placement
The agent and model do not have to share a location. An agent can run in a managed runtime while calling a model in a private cluster, or a self-hosted agent can call a managed model API. Google’s inference-routing architecture describes a unified frontend that routes requests to model replicas on Agent Platform, GKE, Cloud Run, on-premises infrastructure, or another cloud. Model-aware routing can account for replica health and accelerator metrics, but it adds gateway, policy, and monitoring responsibilities.
How to choose an architecture
- Classify the data. Identify personal, confidential, regulated, or export-controlled data in prompts, retrieved documents, tool arguments, logs, and model outputs.
- Set residency and network boundaries. List allowed regions, private-network requirements, egress restrictions, and whether inference may leave your organization.
- Describe the workload. Record request rate, burstiness, expected task duration, streaming needs, concurrency, memory size, tool latency, and whether jobs must resume after a restart.
- Define control requirements. Decide whether you need custom containers, operating-system packages, private registries, specialized accelerators, custom model weights, or deterministic network paths.
- Assign operational ownership. Name the team responsible for deployment, patching, identity, secrets, incident response, capacity, model upgrades, and audit evidence.
- Choose the model boundary. Select a hosted API, private model endpoint, or multiple endpoints. Specify model versioning, fallback behavior, request limits, and evaluation gates.
- Run a failure and cost review. Estimate the cost of repeated inference and tools, then test provider throttling, model errors, tool timeouts, lost sessions, and partial outages before launch.
Designing state, sessions, and recovery
Short-lived requests
For a simple question-answering agent, keep the runtime stateless and store durable conversation records externally. This makes horizontal scaling easier and prevents a container restart from destroying the user’s history.
Long-running tasks
For research, workflow, or automation agents, persist a task record after each meaningful step. Store the current plan, completed tool calls, approvals, retry count, and next action. A queue or workflow engine can resume work after a process or node failure without repeating an irreversible operation.
Isolation and cancellation
Give each task an isolated workspace and narrowly scoped credentials. Set maximum runtime, model-call count, tool-call count, and budget. Provide cancellation that stops queued work and revokes temporary credentials; merely hiding the user interface does not stop an autonomous process.
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Identity, tools, and protocol choices
Model Context Protocol (MCP) can standardize how an agent accesses tools and data. Agent-to-Agent (A2A) protocols can support communication among agents. They solve interoperability problems, not the hosting decision. A protocol does not determine who operates the runtime, where the model runs, which region stores data, or whether a tool is authorized.
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Use an explicit permission model for every tool:
- Register the tool owner, endpoint, data classification, and allowed operations.
- Issue short-lived credentials scoped to one user, tenant, task, or action.
- Validate arguments and outputs at the gateway, including destination, file type, record count, and rate.
- Log approvals, denials, tool results, and policy versions without recording secrets.
- Require human approval for irreversible actions such as payments, deletion, publication, or account changes.
Do not interpret MCP, A2A, or another protocol as permission to access third-party websites or services without authorization. Google’s agentic-AI guidance prohibits unauthorized access and deceptive behavior, including bypassing third-party restrictions on crawling, scraping, or automation.
Security and responsible access on the agentic web
Agent systems combine delegated authority with automation, so the security boundary is broader than the model endpoint. Protect prompt and memory stores, tool registries, session tokens, container images, browser profiles, and model logs. Treat retrieved content as untrusted input: it can contain instructions intended to redirect the agent or exfiltrate secrets.
Use separate service identities for the agent, each tool, and each model endpoint. Enforce egress controls, validate destinations, scan dependencies and images, and keep an audit trail that connects a user request to model calls and external effects. Test prompt-injection, confused-deputy, data-exfiltration, replay, and cross-tenant scenarios.
A July 2026 arXiv preprint argues that the agentic web needs “normative infrastructure”—laws, norms, and practices governing delegated agent access. That is an argument by the paper’s authors, not settled law or an industry consensus. In practice, obtain permission, respect robots and service terms where applicable, identify the agent when required, and avoid deceptive or excessive automation.
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Agent cost is cumulative. One user request can produce several inference calls, retrieval operations, tool requests, and inter-agent messages. Measure cost per completed task, not only cost per model call.
- Model capacity: Track tokens, requests per minute, concurrent generations, queue time, and accelerator utilization.
- Agent capacity: Track active sessions, container concurrency, memory pressure, tool wait time, and workflow backlog.
- Latency: Break the critical path into model, retrieval, tool, network, and queue components.
- Controls: Set per-user and per-tenant budgets, cache safe results, use smaller models for routine steps, and stop loops with explicit limits.
- Billing: Managed services may bill by request, execution time, tokens, or provisioned capacity. Infrastructure pricing, quotas, and regional availability change, so verify the current terms for your selected products.
Do not assume that buying hardware is cheaper than using an API. Low utilization, power, cooling, engineering time, model updates, and incident response can outweigh apparent per-token savings; high steady utilization or strict residency can make private serving reasonable.
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Provider examples and what they illustrate
Microsoft
Microsoft’s Agent Framework material uses Foundry Hosted Agents to illustrate a managed runtime and contrasts it with self-hosting. The example is useful for identifying ownership boundaries: managed hosting covers more platform work, while self-hosting leaves routing, identity, authorization, policy, storage, deployment, and scaling with the application team. Treat availability and language-package lifecycle statements as time-sensitive.
Google Cloud
Google’s architecture separates agent and model runtimes and presents a progression from managed Agent Runtime, to Cloud Run containers, to GKE for greater infrastructure control. Its selection factors include security, cost, model availability, workload characteristics, and hardware or network requirements.
AWS
AWS guidance describes runtime, orchestration, memory, secure tool use, identity, observability, evaluations, and policy as parts of agent infrastructure. These documents explain capability categories; they are vendor guidance rather than independent performance evidence.
A practical deployment checklist
- Document the agent runtime, model runtime, data stores, queues, tools, and network paths.
- Choose whether each component is managed, self-hosted, or hybrid, and assign an owner.
- Pin container and model versions; define upgrade and rollback procedures.
- Configure timeouts, retry budgets, circuit breakers, idempotency keys, and cancellation.
- Persist task checkpoints and test restart, failover, and duplicate-delivery behavior.
- Enforce least-privilege identities, private networking where required, secret rotation, and tenant isolation.
- Instrument traces for every model, retrieval, tool, and inter-agent call.
- Set token, time, concurrency, and financial budgets before exposing the agent to users.
- Evaluate quality, safety, tool correctness, and policy compliance on representative tasks.
- Run an authorization review for every external service and document permitted automation.
Common failure modes and fixes
The agent loses context after a restart
The runtime is relying on process memory or local disk. Move durable conversation and task state to a persistent store, write checkpoints after side effects, and keep only disposable caches in the container.
Costs spike during a traffic burst
Inspect the trace for repeated model calls, retrieval loops, and slow tools causing retries. Add per-task budgets, cap concurrency, cache safe reads, and route simple steps to less expensive models.
A tool performs an action the user did not authorize
Separate planning from execution, require an approval token for irreversible operations, validate tool arguments at a gateway, and scope credentials to the smallest possible action.
Private model serving misses latency targets
Measure queueing, model load time, network hops, batching, and accelerator utilization independently. Add replicas or a nearer endpoint, reduce prompt and output size, or use a managed fallback when policy permits.
A protocol integration works but access is still blocked
Protocol compatibility only establishes message exchange. Check network routes, identity, authorization policy, service terms, rate limits, and the tool’s own approval requirements.
Bottom line: separate the two hosting decisions
Ask two questions instead of one: Who should operate the agent runtime? and Where should model inference occur? Managed services are attractive when reducing operational work matters more than customizing every layer. Self-hosted or hybrid designs are justified when control over networks, data residency, hardware, model weights, or existing infrastructure is essential and the team can operate it reliably. Protocols such as MCP and A2A can connect the resulting components, but they do not replace decisions about ownership, security, state, cost, or responsible access.
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