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Before choosing a hosted agent runtime, find out what it bills while your agent waits. A request-driven agent may spend substantial time waiting for an LLM, tool, API, database, or user; depending on the runtime, CPU charges may stop during that wait, while memory, background activity, storage, and network charges continue. The right choice depends on the agent’s workload and state requirements—not a single “idle cost” figure.

What happens to your bill while an agent waits?

“Idle” can mean different things to a runtime’s billing system. Separate the resources and charges that can accrue:

  • CPU: Does CPU billing stop during I/O waits, such as waiting for an LLM response or tool call?
  • Memory: Does memory remain billable during the wait, have a minimum billed amount, or get reclaimed after a period?
  • Background activity: Do timers, health checks, polling, or other processes keep the workload active?
  • Other charges: Are storage, network transfer, model calls, or tool services billed separately from runtime compute?

A runtime can reduce CPU charges during waits without making the whole session free. Check the billing rules for the specific resource type, configuration, and region you plan to use.

How AWS AgentCore handles idle time

Amazon Bedrock AgentCore Runtime offers two compute types: serverless microVMs and Instances running on AWS-managed EC2 infrastructure in your account. AWS describes dedicated microVM isolation per user session and flexibility across frameworks and models. The compute choice affects both lifecycle and billing.

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MicroVM: request-driven or intermittent work

AWS says microVM CPU scales to zero during I/O waits, including waits for LLM responses, tool or API calls, and database queries. Its pricing page qualifies the claim that I/O-wait and idle time is free: that applies only “if no other background process is running.” CPU behavior is therefore not a guarantee that every charge stops during a wait.

On the pricing page, AWS lists per-second billing with a one-second minimum and a minimum billed memory amount of 128 MB; billing includes system overhead. Runtime v2 automatically reclaims idle memory after 120 seconds. Those details matter when a session remains open while CPU is waiting. AWS says microVM sessions can run for up to eight hours. See the AgentCore pricing page and AgentCore Runtime documentation for current terms and behavior.

AWS publishes a figure of 30–70% of agent workload time spent in I/O wait. This is AWS’s general characterization, not an independent measurement or a prediction for a particular agent. Your own wait fraction may differ.

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Instances: persistent workloads

For AgentCore Instances, AWS says billing covers 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. EC2 pricing agreements apply to the compute portion, not the AgentCore fee. Persistent EBS storage and network transfer may also be billed separately. AWS says Instances support sessions of up to 14 days.

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This is a different cost shape from CPU scaling to zero during I/O waits. For an always-on agent, account for the instance’s running time and management fee, even when the agent is waiting. Consult the current pricing page for applicable rates and details.

Runtime v2 memory and baseline pricing

AWS’s Runtime v2 announcement says memory is loaded on demand and reclaimed when released; the platform snapshots an initialized container environment and restores instances from that snapshot. AWS says that a higher memory rate may be offset for many agents by a smaller billed memory footprint. That is a vendor claim, not an independent cost comparison. The announcement described committed-baseline pricing for steady, always-active sessions as a coming option; check the AWS announcement and current pricing materials for rollout status and rates.

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AWS’s Runtime documentation says the service aligns CPU billing with active processing, typically eliminating charges during I/O waits while maintaining session state. Treat that as a description of CPU billing behavior, not as a claim that memory or associated services are always free.

Which Cloud Run resource fits your agent?

Google Cloud’s agent-hosting guidance distinguishes four Cloud Run resource types by workload shape. Its description of Services says they are “Best for stateless, request-driven agents that handle variable user traffic, benefit from autoscaling, and can scale to zero when idle.” Scaling to zero describes the resource’s behavior; it does not establish the total bill for a particular configuration or workload.

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Cloud Run resource Workload it fits Idle or lifecycle consideration
Services Stateless, request-driven agents with variable traffic, such as chatbot APIs and web API backends Can autoscale to zero when idle
Instances Dedicated, stateful, always-on singleton loops, such as personal agents that need VM-like lifecycle state commands Designed for an always-on lifecycle rather than request-driven scale-to-zero behavior
Worker pools Distributed background agents consuming queue tasks, such as Kafka or Pub/Sub workloads without public HTTP endpoints Background work is the purpose of the resource; assess the chosen billing configuration rather than assuming idle periods are free
Jobs Run-to-completion workflows, such as batch evaluation, large-scale ingestion, or scheduled synchronization Consider execution duration and schedule, not just time waiting for an individual response

Google’s Cloud Run agent-hosting documentation explains these workload mappings. Compare the specific resource and its billing configuration before estimating costs; the resource descriptions alone do not supply a total-cost comparison.

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Choose a runtime by workload, not by the word “idle”

Request-driven agent

If requests arrive intermittently and the agent can resume from a request or session, a scale-to-zero service or a consumption-priced runtime may be a fit. Confirm which resources remain billable during waits and whether startup latency after inactivity is acceptable.

Always-on singleton loop

If one agent must remain continuously available or preserve a live process, compare an always-on instance against a scale-to-zero design. Include the running instance cost and any management fee, then weigh those costs against the operational value of persistent state and avoiding cold starts.

Queue consumer or scheduled workflow

A queue consumer is doing background work even if it has no public HTTP endpoint. Match it to a worker-pool or other background-work resource and check whether polling or other background processes keep compute active. For run-to-completion work, compare job execution time and schedule rather than treating the entire workflow as an idle session.

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Build an estimate that includes more than CPU

There is no universal idle-cost winner in the available provider documentation: it does not give an independent cross-provider benchmark or a total-cost comparison for a defined agent workload. Estimate each candidate using your expected usage and the provider’s current regional rates.

  • Measure or estimate active CPU time and the share of time spent waiting.
  • Estimate the memory footprint and how long it remains billed, including minimums or reclamation delays.
  • Account for background tasks that may prevent the runtime from treating the process as idle.
  • Add artifact or container-image storage, persistent volumes, network transfer, and any runtime management fee.
  • Include model and tool charges outside runtime compute.
  • For always-on usage, compare the instance-hour total and management fee with consumption billing using measured utilization and required session lifetime.
  • Check concurrency, per-session isolation, accelerator needs, regional availability, startup latency, and state persistence.

Prices and service behavior can change. Verify current regional rates, billing minima, and rollout details on the linked provider pages before committing to a design.

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.