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“Idle” does not mean the same thing on every hosted agent runtime. A request-driven agent may stop accruing CPU charges while it waits for an LLM or tool, yet memory, storage, networking, or background activity can still affect the bill. Before choosing a runtime, match its billing meter and lifecycle to the way your agent actually works.
What happens to an agent runtime while it waits?
Start by separating waiting from running. An agent might spend time processing a request, waiting for an LLM response, calling a tool or database, or sitting between jobs. Some services can reduce compute during those waits; others keep a dedicated instance running. Even where CPU billing falls to zero, that does not automatically make the whole runtime or its supporting services free.
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AWS says agent workloads can spend “30–70%” of their time in I/O wait. That is AWS’s general characterization on its pricing page, not an independent measurement or a prediction for a particular agent. Your own wait fraction depends on the model, tools, traffic, and workflow.
How AWS AgentCore distinguishes waiting from always-on compute
Amazon Bedrock AgentCore Runtime offers two compute types with different lifecycle and billing behavior. AWS describes microVMs as serverless and consumption-priced, while Instances run on AWS-managed EC2 infrastructure in your account. AWS says microVM sessions can last up to eight hours; Instance sessions can last up to fourteen days. Those are documented session limits, not a guarantee that a session will remain active or that a particular workload will cost a given amount. See AWS AgentCore Runtime documentation.
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MicroVMs: request-driven or spiky workloads
AWS says microVM CPU scales to zero during I/O waits, including waits for LLM responses, tool or API calls, and database queries. Its documentation describes CPU billing as aligned with active processing while session state is maintained. That can suit workloads with long waits between bursts of work, but check the other meters too.
- CPU: AWS says CPU scales to zero during I/O wait.
- Memory: AWS pricing says Runtime v2 automatically reclaims idle memory after 120 seconds. The pricing page lists 128 MB as the minimum memory billing amount and says system overhead is included. CPU scaling to zero therefore should not be read as an unconditional statement that all compute charges stop immediately.
- Billing granularity: Runtime consumption billing is per second, with a one-second minimum, according to AWS’s pricing page.
- Background activity: AWS qualifies its statement that I/O wait and idle time are free with “if no other background process is running.” A heartbeat, polling loop, or other active process may change the result.
These details come from the AWS AgentCore pricing page. Rates and billing terms can change, so use the current page and the rate for your region when estimating a deployment.
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Instances: persistent loops
For workloads that need a dedicated, persistent loop, AWS says Instances bill for the underlying EC2 instance while it runs plus an AgentCore management fee. Billing begins at provisioning and ends when the Instance is stopped or terminated, with an approximately one-minute minimum. Applicable EC2 pricing agreements apply to the compute portion, not the management fee. Persistent EBS storage and network transfer may be separate charges. This is a different cost shape from paying for active processing on a consumption-priced microVM.
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AWS describes dedicated microVM isolation per user session and support for different frameworks and models. Its Runtime v2 announcement says memory is loaded on demand and reclaimed when released, and describes snapshotting an initialized container environment so instances can be restored from it. AWS says a higher memory rate may be offset by a smaller billed memory footprint for many agents; that is a vendor claim, not an independent cost comparison. The announcement also described committed-baseline pricing as a coming option for steady always-active sessions. Confirm whether that option is currently available and check its rates before relying on it. See the AWS announcement about AgentCore Runtime v2.
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Which Cloud Run resource type fits an agent?
Google Cloud’s agent-hosting guidance maps different workload shapes to different Cloud Run resource types. Pick based on how the agent runs, then verify the billing configuration: the resource-type description alone does not establish the total cost of an example workload.
| Cloud Run resource | Workload shape | What the guidance says |
|---|---|---|
| Services | Stateless, request-driven agent serving variable user traffic, such as a chatbot API or web API backend | Autoscaling and ability to scale to zero when idle |
| Instances | Dedicated, stateful, always-on singleton loop, such as the personal-agent examples in Google’s guidance | VM-like lifecycle state commands |
| Worker pools | Distributed background agents consuming queue tasks such as Kafka or Pub/Sub messages | Designed for workers without public HTTP endpoints |
| Jobs | Run-to-completion work such as batch evaluation, large-scale ingestion, or scheduled synchronization | Use for workflows that finish rather than remain continuously available |
Google’s documentation describes Services as best for “stateless, request-driven agents that handle variable user traffic, benefit from autoscaling, and can scale to zero when idle.” That describes the resource’s fit; it is not a quote about a specific bill. See Google Cloud Run agent-hosting documentation.
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What to include in an idle-cost estimate
There is no useful universal “idle price” without a workload and billing configuration. Build an estimate from the meters and lifecycle that apply to your deployment:
- Workload shape: Is it request-driven, queue-driven, a run-to-completion job, or a persistent singleton loop?
- Active compute: Estimate actual CPU processing time separately from the time spent waiting on models, tools, APIs, databases, or users.
- Memory: Check whether it remains billed during waits, is reclaimed, has a minimum, or includes system overhead.
- Background work: Account for polling, heartbeats, schedulers, and other processes that may keep resources active.
- Lifecycle: Check scale-to-zero behavior, session-duration limits, persistence and resume behavior, and whether cold-start latency is acceptable.
- Separate charges: Include image or code-artifact storage, persistent volumes, network transfer, management fees, and model or tool costs outside runtime compute.
- Capacity and placement: Check per-session isolation, concurrency, shared-instance behavior, accelerator needs, and regional availability.
- Discounts: If you already have a compute commitment or pricing agreement, determine which portion of the runtime bill it applies to.
For an always-on design, compare the instance-hour cost plus any management fee with consumption billing using measured utilization and the lifecycle the agent needs. For a scale-to-zero design, include billed memory and service minimums instead of assuming that zero CPU means zero total cost.
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What should stay always on?
Keep a component always on only when its role or response-time requirement calls for it. A persistent singleton loop or continuously consuming queue worker may need a dedicated lifecycle; a stateless endpoint with variable traffic may fit a scale-to-zero service; a workflow that runs and finishes may fit a job. If the agent must resume a conversation or workflow, confirm where state lives and how it survives instance shutdown before relying on scale-to-zero.
The practical choice is not simply “cheap idle” versus “expensive idle.” It is whether the runtime can pause or reclaim resources without losing required state, missing work, or creating unacceptable startup delay—and what it charges for the resources that remain.
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