24/7 Amazon SQS Support with a 15-Minute Emergency SLA

SQS support covers 24/7 emergency incident response with a 15-minute SLA, written root cause analysis on every P1, throughput and cost tuning, consumer and integration debugging across Lambda, ECS, and EKS, IAM and KMS access design, and architecture review. Every ticket is handled by a named senior engineer assigned to your account. There is no tier-1 triage layer and no per-incident charge.

AWS support covers the SQS service, quota increases, and confirmed platform issues. SQS as a service rarely fails — the incidents teams actually experience come from visibility timeout calibration, retry and backoff behavior, Lambda concurrency, FIFO message group design, and IAM or KMS policies that silently drop deliveries. That is the consumer and integration layer, which sits outside what platform support will debug.

P1 emergencies — unbounded backlog growth, fully stalled consumers, a redrive that flooded a live queue — get a 15-minute first response with a senior engineer joining a live bridge. P2 critical issues are 1 hour, P3 high-priority 4 hours, and P4 standard questions next business day. These are contractual commitments, not best-effort targets.

Standard SQS is at-least-once by design, so duplicates are expected rather than exceptional. In practice they come from a handler running longer than the visibility timeout without extending it, a batch where one slow message pushes the whole batch past the timeout, or a consumer that crashes after doing its work but before calling DeleteMessage. We calibrate the timeout against measured handler duration and make the consumer idempotent, because eliminating duplicates entirely is not achievable on standard queues.

Usually yes, and usually not by changing a quota. FIFO ordering is enforced per MessageGroupId, so throughput is bounded by how many distinct groups you actually use. A constant or low-cardinality group ID serializes everything. We review whether your ordering requirement is genuinely global or per-entity — it is almost always per-entity — then redesign the group key and enable high-throughput mode, which typically lifts the ceiling by a large multiple.

SQS is excellent for durable work queues with independent consumers and simple retry semantics. It is a poor fit when you need replay of historical messages, fan-out to multiple independent consumer groups, ordering across many partitions with high throughput, or routing rules more complex than one queue per destination. Those workloads belong on Kafka or RabbitMQ. We support all of them, so the recommendation is not steered by what we happen to sell.

Amazon Simple Queue Service lets you attach a dead-letter queue to any standard or FIFO queue with a redrive policy. When a consumer receives a message more times than the maximum receive count without deleting it, SQS moves it from the source queue to the dead-letter queue. Nothing is lost, but nothing is processed either. The usual causes are a consumer that crashes on a particular payload, a visibility timeout shorter than the processing time so the message is redelivered while still being worked on, or a downstream dependency that was down. Since 2021 the SQS console and API support redrive from the dead-letter queue back to the source queue once the fault is fixed. We set the alarms on dead-letter queue depth, find the cause, and run the redrive, which is a large share of what SQS support actually involves.

Encrypt at rest with server-side encryption using AWS Key Management Service keys, control access with IAM policies and queue policies that name the exact principals allowed to send and receive, and keep traffic private with VPC endpoints so consumers never reach the SQS API over the public internet. Amazon Web Services lists Amazon SQS as HIPAA-eligible and in scope for its major compliance programmes, so regulated workloads are common on it. Two things still catch teams out: message payloads over 256 KB need the Amazon SQS Extended Client Library, which stores the body in Amazon S3, and that bucket needs the same encryption and access discipline as the queue; and access policies written years ago tend to be broader than anyone intended. Both are part of the review we do when we take on an SQS estate.

Sometimes. SQS is a fully managed message queuing service with no brokers to run, near-unlimited throughput on standard queues, and pay-per-request pricing, which makes it the right default for decoupling services on AWS. It is not a log: messages are removed once consumed, there is no replay, and FIFO queues cap throughput per message group. Workloads that need ordered replay, many independent consumers of the same stream, or exactly-once semantics across a pipeline tend to sit better on Kafka or Kinesis. Estates with JMS applications and protocol requirements such as AMQP or MQTT usually want Amazon MQ or a self-managed broker. We support all of them, so the advice is not tied to a product, and we say plainly when SQS is the wrong tool.

Yes. AceMQ works on Amazon SQS in two ways: ad hoc engagements for a defined piece of work, such as a health check, an upgrade or an incident, or an annual partner engagement that covers 24/7 support, proactive reviews and named engineers. Many teams start ad hoc and move to an annual partnership once the first engagement is done.

Every AceMQ proposal is tailored to your requirements: your Amazon SQS environment, the SLA you need, the hours of cover and the scope of work. Most contracts run $24,000–72,000 a year, depending on the SLA, the scope and how Amazon SQS is deployed, with aggressive discounts when several technologies share one contract. Request a quote and AceMQ replies within 1 hour.