If you've built anything on top of a large language model, you've hit a rate limit. Sometimes it's a hard cap — too many requests per minute. Sometimes it's a token quota. Either way, the result is the same: your request fails, and depending on how you handle it, your user either sees an error or gets silently wrong results.
The naive approach breaks at scale
Most teams start by calling the AI API directly from their application server. It works fine in development, and even in early production. But as traffic grows, you start running into cascading failures. One slow response holds up a request, more requests pile up, and before long you're rate-limited and your entire queue of work is blocked.
Adding naive retry logic with exponential backoff helps somewhat, but it ties up your server threads and makes response times unpredictable. The real fix is to take the AI call out of the hot path entirely.
Queues as the solution
An async queue decouples the work from the response. Instead of your server waiting for the AI API to respond, it enqueues a job and returns immediately. The queue handles retries, respects rate limits, and delivers results to your application via a callback once the work is done.
- Control concurrency — only send N requests to the AI API at once
- Retry failed jobs automatically with configurable backoff
- Prioritize urgent work over background processing
- Observe exactly what's happening at every stage
Priority queues for mixed workloads
Not all jobs are equal. A user waiting for a response needs their job processed before a nightly batch re-embedding job. Priority queues let you assign a numeric priority to each job — higher priority jobs move to the front of the line.
With TaskFlow Queue, you set priority (0–100) when you enqueue a job. The worker always picks the highest-priority pending job first, so your interactive workloads stay snappy even under batch load.
HMAC-signed callbacks close the loop
When a job finishes, the queue needs to deliver the result somewhere. HTTP callbacks are the most flexible mechanism — your endpoint receives the result regardless of what language or framework you're using. But you need to verify that the callback actually came from the queue and wasn't fabricated by a third party.
HMAC signing solves this. The queue signs each callback with a shared secret, and your endpoint verifies the signature before processing the result. TaskFlow Queue includes HMAC signing on all tiers — no extra setup required.
What to look for in a queue
- Configurable retry counts and backoff strategies
- Priority support at the job level
- Dead-letter queues so failed jobs aren't silently discarded
- Delivery logs so you can audit what happened
- Callback signing for security
These aren't nice-to-haves — they're the difference between a queue that makes your AI pipeline reliable and one that just shifts the failure to a different layer.