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AWS’s 90-day deletion rule applies to an AWS project paused after reaching its opt-in spend limit: if the owner takes no action within 90 days of the pause, AWS permanently deletes that project’s data. It is not a rule that AWS Budgets deletes data or that every AWS account is erased after 90 days. For teams running AI agents, the key safeguard is to enforce cost limits in the workload before an agent can make more calls—not to rely on delayed budget alerts.
What happens when an AWS project reaches its spend limit?
AWS describes project spend limits as an opt-in control in AWS Settings. An administrator can choose a recommended or custom limit and optionally enable early controls named “Stop new resource launches,” “Pause idle resources,” or “Pause top cost drivers.” AWS says the spend limit takes effect immediately. These optional controls are not evidence that every possible AWS charge is covered.
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When the limit is reached, AWS pauses the project and stops its resources, while preserving the project data at that stage. To reactivate the project, the owner raises the spend limit. Some resources might then require a manual restart.
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Does AWS delete data after 90 days?
Only in the described project-spend-limit situation: a project has reached its limit and been paused, and its owner takes no action during the following 90 days. The rule is not a general 90-day retention policy for AWS accounts, and AWS Budgets is not what triggers this project-data deletion.
AWS’s documentation says the project data is permanently deleted if that condition is met. It does not establish the recovery state of every resource type or explain every edge case, so do not assume that all project resources can be restored simply by raising a limit.
Can AWS Budgets stop an AI agent from overspending?
AWS Budgets monitors cost or usage at configured dimensions and can send notifications or take configured actions. That makes it useful for account oversight and escalation, but it is not an instantaneous per-agent spending meter or a guaranteed stop at an exact dollar amount.
AWS says Budgets information updates up to three times a day, typically 8–12 hours after the previous update. AWS also warns that usage and billing delays can allow additional charges to accrue before a notification arrives, and costs may continue to change after it is sent. AWS Budgets: Managing your costs with budgets
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Use budget alerts alongside controls in the agent’s execution path. A notification can tell a team that spending is rising; it cannot by itself ensure that a running agent stops before its next costly tool call.
How should teams set spending controls for an AI agent?
AWS’s Agentic AI guidance identifies unpredictable cost patterns such as reasoning-loop token spikes, tool-invocation storms, and memory growth. It recommends establishing limits before invocation and enforcing them outside the agent’s own control loop. A practical layered design is:
- Set thresholds at multiple levels. Check cost or usage per cycle, per task, and per day rather than relying on one account-level ceiling.
- Bound each session. Cut off sessions when they reach an iteration limit or cumulative token-cost threshold. A runaway loop should not be able to continue just because an agent’s own reasoning says it should.
- Cap tool calls. A tool can incur its own service charge and create follow-on model costs when its results are processed. Set invocation limits and reject calls once the applicable cap is reached.
- Constrain memory growth. Set a memory-size guardrail or trigger summarization before accumulated context grows beyond the intended budget.
- Throttle before a hard stop. Reduce usage as a daily threshold approaches, then apply a firm cutoff for sessions that continue to exceed their limits.
- Monitor control events. Track budget utilization, cutoff events, and throttling so operators can distinguish normal usage from a control being reached or bypassed.
AWS presents Amazon Bedrock AgentCore Policy as a way to apply Cedar policies at an AgentCore Gateway boundary, helping prevent prompt manipulation from bypassing policy. Its guidance also describes Amazon Bedrock Guardrails for topic restrictions. These are implementation recommendations, not automatic hard spending caps provided to every AWS agent. Verify service behavior and availability for the account and region before relying on a particular control. AWS Well-Architected: Agentic AI Lens
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| Control | Scope and function | Important limitation or consequence |
|---|---|---|
| AWS project spend limit | Project-level control in AWS Settings; pauses the project and stops resources when the limit is reached. Optional early controls can stop new launches or pause selected resources. | If no action is taken within 90 days after the pause, AWS permanently deletes the project data. AWS Account Management documentation |
| AWS Budgets | Monitors configured costs or usage; can notify or take configured actions. | Updates and notifications can lag usage and billing, so it is not an exact real-time agent cutoff. AWS Budgets documentation |
| Agent-level budget policy and cutoffs | Checks and limits agent cycles, tasks, sessions, tool calls, or memory growth before further work proceeds. | Must be implemented and monitored; AWS guidance does not say such controls are automatic for all agents. AWS Agentic AI Lens |
| Cost anomaly history | Supports review of detected irregular cost patterns. | Anomaly records have a separate 90-day availability and archive window; it is not the project deletion rule. GetAnomalies API reference and AWS anomaly investigation guide |
Do not confuse project deletion with the other 90-day cost window
AWS Cost Explorer’s GetAnomalies API reference says detected anomalies are available for up to 90 days. AWS’s anomaly investigation guide says anomaly data archives after 90 days, which can limit investigation information for archived anomalies. Those rules concern anomaly-history records; they neither cause project deletion nor determine whether a paused project can be reactivated.
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