What does AceMQ Amazon SQS support include?

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.

We have an AWS support plan already. What does this add?

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.

How fast do you respond to an SQS production incident?

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.

Why are our messages being processed more than once?

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.

Our FIFO queue is capped around 300 messages per second. Can that be raised?

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.

Should we be using SQS at all, or something else?

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.

How is AceMQ Amazon SQS support priced?

Support is an annual contract priced on your environment size, required SLA tier, and coverage hours — not per incident or per ticket. Calling us during an incident never carries a cost decision. Contact us for a quote; we turn them around within 24 hours.

Amazon SQS Support

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

AceMQ supports Amazon SQS in production — duplicate processing from visibility timeouts shorter than the handler, FIFO queues capped by a single message group, dead-letter queues that fill silently, and Lambda event source mappings that scale the wrong way under load. Every ticket reaches a named senior engineer who already knows your queue and consumer topology.

Senior Amazon SQS engineers on call right now — 24/7/365

15 min emergency SLA24 /7 global coverage130 + enterprise customers26 + countries served

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Trusted for mission-critical Amazon SQS by teams in finance, healthcare, defense, and telecom

Escalation Path

Your first hour of a Amazon SQS outage

Most vendors publish an SLA number. This is what actually happens, minute by minute, when you page a senior AceMQ engineer.

T+0

You page us

Phone, email, or Slack — any channel reaches the on-call senior engineer directly. No web form, no tier-1 queue.

T+15

Named engineer live

A senior engineer who already knows your environment joins a live bridge. Zero cold-start, no re-explaining your topology.

T+30

Root cause isolated

Direct broker access, log and metric review, and a working hypothesis with a rollback plan before we touch anything.

Post

Written RCA

Documented root cause, the fix applied, and the prevention steps — delivered after every P1, not just when asked.

Response Times

SLA tiers, contractually guaranteed

Every tier reaches a senior Amazon SQS engineer. There is no tier-1 triage layer to get through.

P1 — Emergency

15 min

Production down, messages not flowing, cluster or broker failure

P2 — Critical

1 hour

Severe degradation, rising error rates, approaching capacity limits

P3 — High

4 hours

Performance issues, configuration problems, non-critical failures

P4 — Standard

Next day

Questions, guidance, best practices, non-urgent improvements

Incident Triage

Amazon SQS problems we fix every week

These are real symptoms from real Amazon SQS production environments — and the first thing our engineers check when one comes in.

Symptom you're seeingWhat we check firstTypical resolution

The same message processed several times across different workers

What we check firstHandler duration against VisibilityTimeout, and whether the consumer extends visibility mid-processing. A handler that outlives the timeout releases the message back to the queue while still working on it, so a second worker starts the same job.

Typical resolutionUnder 1 hour

FIFO queue throughput flat at roughly 300 messages per second

What we check firstMessageGroupId cardinality. Ordering is enforced per group, so a constant or low-cardinality group ID serializes the entire queue. High-throughput mode with per-message-group deduplication lifts this, but only if the group IDs actually spread.

Typical resolution1–3 hours

ApproximateAgeOfOldestMessage climbing while consumers look healthy

What we check firstWhether a poison message is cycling — received, failed, returned to visible, received again — without ever reaching maxReceiveCount. A redrive policy that is missing or set too high lets one bad message occupy consumer capacity indefinitely.

Typical resolutionUnder 2 hours

Lambda consumers not scaling up despite a deep backlog

What we check firstEvent source mapping concurrency, the account's reserved and unreserved concurrency split, and whether the function is erroring. Lambda backs off its poller scaling on sustained invocation errors, so a failing handler looks like a scaling limit.

Typical resolution1–3 hours

Empty receives dominating the bill while messages are clearly queued

What we check firstWhether ReceiveMessage is using short polling. Short polling samples a subset of the distributed queue hosts and frequently returns nothing even when the queue is not empty, inflating request cost and adding latency. Long polling with a WaitTimeSeconds of 20 removes both.

Typical resolutionUnder 1 hour

Messages disappearing between SNS or EventBridge and the queue

What we check firstThe queue access policy and, for cross-account or encrypted queues, whether the KMS key policy allows the publishing service principal. A denied delivery from SNS is not retried indefinitely and leaves no trace in your consumer logs.

Typical resolution1–2 hours

SendMessage failing intermittently with throttling under burst

What we check firstPer-queue request rate against the API limits for the queue type, plus whether the SDK's default retry and backoff has been overridden. FIFO queues throttle far earlier than standard queues, and batching ten messages per API call is usually the fix.

Typical resolution1–2 hours

Messages larger than 256 KB failing or arriving truncated

What we check firstWhether the extended client library is in use on both sides. The S3-backed large payload pattern only works when the consumer resolves the pointer too — a producer using it against a plain consumer delivers an S3 reference nobody dereferences.

Typical resolutionUnder 2 hours

Resolution times reflect typical Amazon SQS engagements under an active AceMQ support contract. Every P1 closes with a written root-cause analysis.

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Support In Practice

Amazon SQS problems we've already solved

Representative engagements showing how these incidents get diagnosed and closed under an AceMQ support contract.

All use cases

Remediation

AWS Lambda SQS Redrive Loop Remediation

Digital Insurance Provider

Breaking a redrive loop where SQS messages were reprocessed indefinitely because the queue visibility timeout was shorter than the Lambda function timeout.

AWS LambdaAmazon SQSDocker

Read case study

Assessment

AWS Lambda Cost and Right-Sizing Assessment

Consumer Subscription Business

Measuring memory, duration, and concurrency across a large Lambda estate to right-size functions that were provisioned by guesswork.

AWS LambdaAmazon SQSDocker

Read case study

What's Included

Everything in your Amazon SQS support contract

No add-on pricing for incidents. No per-ticket charges. One contract covers the whole surface.

Emergency Incident Response

Queue backlog growing without bound, consumers stalled, or a redrive that pushed thousands of messages back into a live queue. A senior engineer joins a live bridge within 15 minutes — not a ticket acknowledgement.

Root Cause Analysis

Every P1 closes with a written RCA: the CloudWatch evidence, the queue and consumer settings involved, the fix applied, and the change that prevents recurrence.

Throughput & Cost Tuning

Long polling, batch sizing, visibility timeout calibration, FIFO message group design, and consumer concurrency tuned against actual handler duration. SQS bills per request, so tuning throughput and tuning cost are the same exercise.

Consumer & Integration Debugging

Lambda event source mappings, ECS and Fargate pollers, Spring Cloud AWS, and the AWS SDKs across Java, Python, .NET, and Node — including the retry and backoff behavior that turns a transient fault into a DLQ flood.

IAM, KMS & Cross-Account Access

Queue policy and IAM design, SSE-KMS key policies for SNS, EventBridge, and S3 event sources, VPC endpoint configuration, and the cross-account patterns where a silent access denial looks exactly like message loss.

Architecture Review & Migration

Migration from self-managed brokers onto SQS, and honest guidance on when SQS is the wrong fit — fan-out, replay, ordering across many partitions, and complex routing generally belong on Kafka or RabbitMQ instead.

Anywhere You Run It

We support Amazon SQS wherever it's deployed

Cloud, Kubernetes, bare metal, hybrid, and air-gapped — including environments where you can't give us outbound network access.

SQS Standard queuesSQS FIFO & high-throughput FIFOAWS Lambda event source mappingsAmazon ECS & AWS FargateAmazon EKS & KubernetesEC2 & Auto Scaling groupsSNS, EventBridge & S3 event sourcesCross-account & cross-region queuesSSE-KMS encrypted queuesVPC endpoints & PrivateLinkAWS GovCloud & regulated accountsMulti-account AWS Organizations

Why AceMQ

What you get that you don't get elsewhere

Named Engineers, Zero Cold Start

The same senior engineers stay on your account. They know your queue layout, your redrive policies, and which consumers sit behind which queues — so a P1 call starts with diagnosis instead of orientation.

No Tier-1 Triage Layer

You reach a senior engineer directly by phone, email, or Slack. Nobody collects a case description to pass along, and no support-plan tier decides how long you wait for someone who can actually read your CloudWatch metrics.

The Consumer Side, Which AWS Support Won't Debug

SQS itself almost never fails. Nearly every SQS incident is a consumer problem: visibility timeout, retry behavior, concurrency, or an unhandled exception path. AWS support scopes to the service, which leaves the layer causing your incident unattended.

Genuine Follow-the-Sun Coverage

Engineers across 26+ countries and every time zone. Your 3am incident is someone's mid-afternoon — no overnight skeleton crew, no waiting for a region to wake up.

Proactive, Not Just Reactive

Quarterly reviews of oldest-message age, DLQ growth rate, empty-receive ratio, and request cost trend — so you catch a poison-message loop or a missing redrive policy before it becomes a weekend incident.

We Know When SQS Is the Wrong Answer

We support RabbitMQ, Kafka, ActiveMQ, and IBM MQ at the same depth. SQS has no replay, no fan-out to multiple independent consumers, and no routing beyond one queue per destination. We will tell you plainly when you have outgrown it.

FAQ

Amazon SQS support questions

A Queue That Never Goes Down Can Still Take You Down

Whether you need emergency response tonight or a support contract that catches the next poison-message loop before your backlog does, AceMQ staffs every engagement with a named senior Amazon SQS engineer. Support quotes returned within 24 hours.

Get in Touch

Talk to a Support Expert

Send us a message and we'll follow up within one business day — or book a free 30-min consultation directly.

305-204-2607

info@acemq.com

66 W. Flagler St. 9th Floor
Miami, FL 33130

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