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Move LLM work out of fragile synchronous request paths by putting an admission and scheduling layer in front of your workers. A durable queue decouples producers from consumers, but it does not limit token spend, provider request rates, outstanding work, or simultaneous model calls. A production pipeline must control each of those independently, then make retries safe and completion observable.
Separate interactive requests from background work
Start with the latency contract. A user waiting for an answer needs a bounded response time; offline enrichment, evaluations, and bulk transformations may tolerate delayed completion. Sending both kinds of work through the same execution path can let large background jobs consume the capacity needed for interactive traffic.
Use a live task queue when jobs need prompt dispatch, per-task status, priority, or independent retries. For large offline workloads that tolerate a longer completion window, a provider batch facility may be a better fit. OpenAI’s Batch API reference describes asynchronous request groups with a 24-hour completion window, status counts, and input, output, and total token-usage fields. The reference says usage is populated for batches created after September 7, 2025. Check current endpoint support, limits, pricing, and data-retention terms for the deployment before relying on those details.
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Batch submission does not replace application-level task tracking. Keep a mapping between each business task and its submitted request, handle partial failures, and reconcile completed work. Do not use a longer-window batch path when users require tight, predictable interactive latency.
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Give every task enough metadata to schedule and recover it
A queue message should identify work, not become an unbounded container for it. Persist task state durably and place a compact message on the queue. When inputs are large, store them in durable object storage and put a protected reference in the message rather than copying the payload through every queue and log.
Include the fields needed to make an admission, scheduling, and recovery decision:
- A stable task or idempotency key, plus tenant or workload class.
- Enqueue time, deadline, and priority.
- An estimated or reserved token cost, attempt count, and retry state.
- Trace and correlation identifiers for following the task across services.
- A reference to the input and the durable location of its result or state.
The producer validates the request, assigns its class and deadline, estimates worst-case token demand, and checks tenant and global admission budgets before accepting it. Persist the task record before publishing its message, and ensure the publication and state transition can be recovered if either side fails. The precise transaction or outbox pattern depends on the storage and queue system; the important outcome is that accepted work is not silently lost or represented twice without an idempotent identity.
Budget tokens separately from messages and requests
Message count is not a useful proxy for model cost. Two queued tasks can differ substantially in prompt size and permitted output. A system that caps the number of messages but ignores token demand can still overspend or exceed a provider’s token throughput.
Before dispatch, reserve an estimate that accounts for input and the maximum output the task is allowed to request. Set an explicit output-token limit and timeout on each model call. When the provider returns usage, record actual input and output usage, reconcile it against the reservation, and release or adjust the remaining reservation. OpenAI’s Batch API reference exposes input, output, and total token-usage fields; the same estimate-versus-actual accounting principle applies to any provider whose response reports usage.
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Estimates are imperfect, especially when prompts vary or outputs are generated dynamically. Use conservative limits appropriate to the workload and retain a safety margin for prompt variation, retries, and output variability. Maintain separate budgets where provider quotas are separate, such as by model or region, rather than assuming one global token pool reflects every deployment. Exact request and token ceilings depend on provider, model, account, and configuration; confirm them in the current documentation for the selected service.
Shape dispatch with independent rate, concurrency, and capacity limits
For each dispatch decision, verify that the task fits every relevant budget. A practical scheduler considers the provider request rate, token rate, concurrent model calls, per-tenant budget, and limits on in-flight messages and bytes. Passing one check does not imply that the others have capacity.
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Choose eligible work using deadline, priority, and fairness rules rather than letting one tenant or workload class monopolize capacity. Consider separate queues or scheduling classes for large, expensive jobs so they cannot occupy every worker needed by small urgent tasks. Strict ordering can reduce throughput and create head-of-line blocking, so require it only where the business behavior depends on it.
Apply backpressure before the system runs out of room
Backpressure should slow or reject work before memory, threads, or provider quotas are exhausted. Bound outstanding messages and bytes at both ends: producers should not accumulate unlimited publish requests, and consumers should not pull more messages than their workers can process. Google Cloud Pub/Sub documents publisher and subscriber flow controls for outstanding message count and bytes. Its subscriber flow control stops further pulls when configured outstanding limits are reached.
Without publisher-side flow control, requests can accumulate in memory until they fail with DEADLINE_EXCEEDED, as Google Cloud’s Pub/Sub flow-control documentation warns. Consumer-side limits alone cannot prevent that producer-side buildup.
When workers approach their limits, stop pulling, delay dispatch, or defer or reject lower-priority work with a clear retry time. Give tasks deadlines or maximum-age rules so an overloaded queue cannot silently accumulate work that has already lost its value. Temporary overload can be absorbed by bounded queues; if arrivals persistently exceed completions, flow control only makes the imbalance visible. Add steady-state capacity or reduce admitted demand.
Measure arrival rate, service rate, backlog age, provider throttling, and token consumption together. If the queue grows while workers are idle, investigate dispatch limits, eligibility rules, or provider throttling. If workers are saturated but provider capacity remains available, application capacity may be the constraint. Adding workers will not solve a provider token ceiling; raising token throughput will not help a shortage of worker capacity.
Make acknowledgement, retries, and dead letters safe
Queue delivery may repeat after a timeout, worker failure, or lost acknowledgement. Treat duplicate delivery as normal: use the stable idempotency key to prevent duplicate business effects, persist the result or completion state durably, and acknowledge only after that persistence succeeds.
For jobs that can outlast the queue’s acknowledgement or visibility window, extend the lease where supported or divide the work into checkpoints. Otherwise, a still-running task may be delivered again while the original worker continues. Amazon SQS exposes visibility timeout and redrive policy settings; its API reference lists a default visibility timeout of 30 seconds, default message retention of 4 days, and default redrive-policy maxReceiveCount of 10. These are documented service defaults, not recommendations for LLM jobs; configure them for the actual task duration and recovery policy.
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Classify failures before retrying. Throttling and transient network or service timeouts may be retried; malformed inputs and permanent policy or validation failures generally need correction or review rather than repeated execution. For transient failures, use bounded exponential backoff with jitter, honor provider retry guidance such as Retry-After when supplied, and keep attempts within the task deadline. Immediate synchronized retries can turn one provider throttle into a retry storm.
Route repeatedly failing tasks to a dead-letter or review path with enough protected context to diagnose and, when appropriate, replay them. Google Cloud Pub/Sub supports dead-letter forwarding and delivery-attempt tracking, but documents those behaviors as best-effort: a configured attempt count is not an exact guarantee. SQS uses redrive policy settings with its own semantics. Understand the chosen service’s delivery and replay behavior rather than treating a counter as a universal promise. Limit sensitive payload data in logs and dead letters, control access, and set retention intentionally.
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Compare candidate services against the controls your workload actually needs. Product guarantees, limits, regional availability, and costs can differ, so verify details for the intended region and configuration.
- Rate and burst control: Can producers and dispatchers cap sustained throughput and bursts? Cloud Tasks documents token-bucket dispatch control.
- Outstanding work: Can clients bound in-flight message count and bytes? Pub/Sub documents publisher and subscriber controls.
- Delivery and acknowledgement: How are visibility or lease deadlines extended, and when can a task be redelivered? SQS exposes visibility timeout settings.
- Failure isolation and replay: How are delivery attempts configured, tasks routed to a dead-letter destination, and messages inspected or replayed? Pub/Sub and SQS document service-specific dead-letter or redrive controls.
- Ordering and fairness: Confirm ordering guarantees and decide how priority or tenant fairness will work without creating avoidable head-of-line blocking.
- Operational fit: Check identity and access integration, observability, message-size and retention limits, throughput quotas, regional availability, and total cost for the workload.
These are comparison criteria, not a benchmark or a claim that one queue is best for every LLM pipeline.
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Queue depth alone cannot tell you whether the system is healthy. Track signals that connect admission, queueing, execution, and provider use:
- Queue depth, enqueue and dequeue rates, and age of the oldest task.
- End-to-end latency, deadline misses, and expired or cancelled work.
- Worker concurrency, in-flight messages and bytes, and service rate.
- Retries, delivery attempts, dead-letter counts, and replay outcomes.
- Provider throttles and failures, plus estimated versus actual tokens.
- Cost and usage by tenant and task class, subject to the provider’s available usage data.
Alert on sustained backlog growth and deadline misses, then distinguish a demand spike from insufficient steady-state capacity. Decide in advance what the system does under overload: pause lower-priority admission, return a retry time, route work to a slower batch path, or scale workers where the provider permits it. Make those actions observable so operators can tell deliberate backpressure from a stalled pipeline.
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