Use cases

Conveyor fits any work you want to run outside the request path, durably and at scale. A few shapes it suits especially well, each leaning on the features noted:

• Transactional email and notifications. Send the welcome email, receipt, or push notification after the request returns. Push dispatch keeps latency low, retries with backoff and the dead-letter queue absorb provider outages, and per-key concurrency caps in-flight sends per customer. This is exactly the Postmark example.
• Webhook and API fan-out. Deliver outbound webhooks or call rate-limited third-party APIs. Per-queue rate limiting respects a vendor's quota, the per-task-type circuit breaker sheds load when an endpoint starts failing, and lifecycle events give you a delivery audit trail.
• Media and document pipelines. Transcode video, resize images, or render PDFs as multi-step jobs. Task dependencies model "extract, then transform, then publish" with fan-out/fan-in, weighted queues keep heavy jobs off the fast lane, and progress reporting tells a slow job from a stuck one.
• AI model calls and inference jobs. Run LLM completions, embeddings, transcription, or image generation as background tasks against a paid, rate-limited provider. Per-queue rate limiting holds dispatch under the provider's per-second quota (over-rate tasks wait, no retry spent), per-key concurrency caps simultaneous calls per API key or tenant, retries with backoff ride out 429 and transient 5xx responses, per-task timeouts/deadlines bound a slow generation, and progress reporting surfaces how far a long job has run.
• RAG ingestion and embedding pipelines. Index a corpus as a dependency graph: fan out per document, chain "chunk, then embed, then upsert", and coalesce many chunks into one batched embeddings call with group aggregation. End-to-end encryption keeps source documents as ciphertext in the queue.
• Scheduled and recurring work. Nightly reports, billing runs, retention sweeps, and cache warming. Server-persisted cron survives restarts and failover, and delayed/scheduled tasks handle one-off future work.
• Batch and digest processing. Coalesce a burst of events into one unit of work: hourly digest emails, debounced search reindexing, or bulk writes. Group aggregation fires a single batch handler on size, delay, or grace period, tuned per group.
• Polyglot background jobs. Enqueue from a Go API and process on Python or TypeScript workers (or any mix). One wire protocol means a task enqueued in one language runs on a worker written in another, which suits ML inference, scraping, or data sync split across teams and runtimes.
• Privacy-sensitive workloads. Process PII, health, or financial payloads where the queue must not see plaintext. End-to-end encryption seals payloads in the SDK or CLI so the server stores ciphertext only and holds no keys.