SimpleQ vs Apache Kafka
Different tools for different problems. SimpleQ is transport for backends that call LLM and third-party APIs — publish a job and it's delivered to your webhook with a three-signal ack protocol, backpressure that never burns retries, and queue templates tuned for AI workloads. Apache Kafka is a distributed event-streaming log — a high-throughput, partitioned, append-only commit log built for event pipelines, fan-out to many consumer groups, and offset-based replay. If you're moving millions of events a second through a streaming platform, that's Kafka. If you want a managed queue that POSTs jobs to an HTTP endpoint and handles API backpressure for you, that's SimpleQ.
| Feature | SimpleQ | Apache Kafka |
|---|---|---|
| Category | Managed queue (transport) | Distributed event-streaming log (high-throughput pipelines) |
| Managed | Yes — no infra to run | No — run a broker cluster + ZooKeeper/KRaft yourself (or pay for a managed Kafka like Confluent/MSK) |
| Delivery model | Push (webhook POST) | Pull — long-lived consumers poll partitions and commit offsets |
| Retries | Configurable backoff (exponential/fixed) | No built-in per-message retry — you implement it (retry topics / DLT, e.g. Spring Kafka), or re-consume from offset |
| Rate limiting | Per-queue fixed-window, built-in | Broker/client quotas (bytes-per-sec throttling), not per-message rate limiting |
| Ack mode | Three-signal protocol: ack (success), nack (failure with retryable flag), defer (backpressure — redelivers without burning attempts). Once the callback returns 200, the outcome is guaranteed. | Offset commit — a consumer advances its committed offset. Binary progress, not a per-message success/fail/backpressure signal. |
| Backpressure | 429/503/529 auto-defers with Retry-After relay. Ack-mode /defer does the same. Neither burns a retry attempt — a job can ride out 100 consecutive 429s and still complete. | Consumer-side — pause/resume partitions or slow polling. No retry budget concept; you manage redelivery via offsets/retry topics. |
| Dead-letter queue | Built-in with single + bulk replay via API and dashboard | Dead-letter topic — a convention you wire up (e.g. Spring Kafka DLT); replay = re-consume from an offset |
| Idempotency | Publish-boundary dedup — returns existing job ID on duplicate, safe-to-retry publishers | Idempotent producer (no duplicate appends) + exactly-once via transactions; consumer-side dedup is on you |
| Queue templates | template: "anthropic" or "openai" — one field configures ack mode, timeout, and backoff tuned for each provider's API | None |
| Per-job audit trail | Full attempt history: status, error, HTTP code, timestamp per attempt | The log is the record (offsets + retention), but no per-message delivery-attempt/error history — you add observability |
| Webhook signing | HMAC-SHA256 with per-queue secret — a leaked secret affects one queue, not the org | N/A (pull). Transport secured by TLS + SASL/ACLs; no signed delivered payload |
| AI workload support | Anthropic and OpenAI queue templates, seconds convention matching provider Retry-After headers, ack mode for long LLM calls | None (general-purpose streaming substrate) |
| Ordering | No ordering guarantee | Strong ordering within a partition (key-based) |
| Partitions / consumer groups | No — single push per job to one webhook | Yes — partitions for parallelism, consumer groups for fan-out and balancing |
| Replay | DLQ replay (re-deliver failed jobs) | Offset-based replay / event-sourcing — re-read the whole log from any offset, within retention |
| Throughput | Tuned for API/webhook job rates | Very high — millions of messages/sec on a cluster |
| Dashboard | Built-in real-time dashboard | No built-in UI — Kafka UI / Confluent Control Center / third-party tooling |
| Pricing | Free during early access | Free (OSS) + cluster/ops cost, or managed Kafka pricing (Confluent, MSK) |
When to choose SimpleQ
- You don't want to operate a Kafka cluster. No brokers to size, no partitions to rebalance, no KRaft/ZooKeeper, no consumer-group lag to babysit — SimpleQ is fully managed and your worker is a plain HTTP endpoint.
- Your work is discrete jobs delivered to a webhook, not a continuous event stream. SimpleQ's publish-deliver-ack model is far simpler than running long-lived consumers that poll partitions and commit offsets.
- Your downstream APIs return 429s and you need explicit backpressure. SimpleQ's defer mechanism holds the job and redelivers after the Retry-After delay — no attempt burned. Kafka has no retry-budget concept; redelivery is something you build with retry topics and offsets.
- You want push delivery instead of pull. With Kafka you run consumers that poll and commit; with SimpleQ you expose one HTTP endpoint and SimpleQ POSTs each job to it.
- You want one POST to configure a production-ready AI queue.
template: "anthropic"or"openai"sets ack mode, timeout, and backoff tuned for each provider's API — Kafka has no notion of LLM-tuned defaults. - You want per-job audit trails — status, error, HTTP code, and timestamp for every attempt — plus DLQ with single and bulk replay built into the API and dashboard, instead of wiring up dead-letter topics and offset re-reads yourself.
- You want HMAC-SHA256 signing of the delivered payload with a per-queue secret, so a leaked secret affects one queue rather than the whole org.
When to choose Apache Kafka
- You're building a high-throughput event-streaming pipeline — millions of messages per second, many producers and consumer groups fanning out off the same stream.
- You need durable, replayable event history: re-read the log from any offset for event-sourcing, reprocessing, or bootstrapping new consumers, with strong per-partition ordering.
- You're already standardized on the Kafka ecosystem (Kafka Streams, Connect, ksqlDB, Flink) and want a streaming substrate rather than a job-delivery service.
Kafka and SimpleQ solve different problems
They often coexist. Kafka is the streaming backbone — ingesting events, fanning out to many consumers, retaining a replayable log. SimpleQ is job transport at the edge of that system: when an event should trigger an outbound LLM call, webhook, or third-party API sync, publish a SimpleQ job and let it handle delivery, retries, per-queue rate limiting, and backpressure that never burns a retry attempt. A common pattern is a Kafka consumer that, instead of calling a rate-limited API directly and fighting 429s inside the consumer loop, publishes a SimpleQ job and moves on — SimpleQ owns the delivery, the Retry-After relay, and the DLQ. If you're calling LLM APIs, start with template: "anthropic" or "openai".