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AI Agent Concurrency & Queue Cost Calculator

Estimate monthly cost and queue behavior for AI agents as concurrency, rate limits, retries, and container capacity scale. Compare provider limits and infrastructure sizing.

Scenario presets

Workload & concurrency

Monthly requests Peak concurrency (sessions) Requests per session LLM provider Input tokens / request Output tokens / request Cached input (%) Reasoning / output multiplier Queue depth target (max waiting) Provider rate limit (RPM) Timeout (seconds) Retry rate (%) Retry cost multiplier Container size Extra infra monthly ($) Projection (months)

Estimates are directional. Last updated: 2026-07-08. See notes.

Required containers

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Est. queue wait

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Monthly LLM cost

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Monthly infra cost

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Peak requests / min

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Container capacity

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Utilization at peak

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Cost per 1k requests

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Total monthly cost

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Projected period cost

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Monthly cost breakdown

Cost line	Monthly	Share
Input tokens	—	—
Output tokens	—	—
Retry calls	—	—
Container hosting	—	—
Extra infra	—	—

Concurrency sensitivity

Concurrency	Containers needed	Queue wait	Monthly infra	Status
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Verdict

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Frequently asked questions

What does concurrency mean for an AI agent?

Concurrency is the number of user sessions or requests in flight at the same time. Higher concurrency means more active workers, more API calls per second, and longer queues if capacity can't keep up.

How is queue wait time estimated?

The calculator uses a simple M/M/c approximation: average wait rises as arrival rate approaches total service capacity (workers × requests per worker per second). It ignores network jitter and bursty arrival patterns, so treat it as directional.

Why does retry rate matter?

Retries add hidden traffic. A 5% retry rate on 1M requests means 50,000 extra API calls, plus the engineering and user-experience cost of timeouts. The multiplier models retries that hit slower fallback providers.

What's the difference between provider RPM and my container capacity?

Provider RPM is the API-side throttle. Container capacity is how many simultaneous requests your app servers can hold without degrading. You need both: a high provider limit doesn't help if your containers are saturated, and extra containers waste money if the API caps you.

How do I reduce concurrency cost without adding latency?

Increase cache hit rate, batch requests, use a faster/cheaper model for simple turns, keep timeouts tight, and add connection pooling so each container handles more concurrent requests.

Inference Capacity Planner → API Pricing Comparator → Latency Budget Calc → Model Router Cost Calc →

Concurrency and queue estimates are directional. They assume Poisson arrivals, fixed service time, and steady-state M/M/c approximations. Replace provider rates and observed latency with your own data for a precise forecast.