Amazon SQS Consulting & Services
AceMQ provides Amazon SQS consulting, queue and dead-letter design, Lambda and container consumer tuning, migration from RabbitMQ, IBM MQ and ActiveMQ, and 24/7 support, delivered by named senior engineers. The team of 11+ senior engineers has served 160+ customers in 26+ countries, with a 15-minute emergency response around the clock.
A senior engineer, not an SDR. No sales sequence attached.
Without re-architecting your consumers or moving off SQSFor production incident cover, see AceMQ Amazon SQS 24/7 support.
Customers
Partners
The Problem
Eight Amazon SQS problems that teams bring to AceMQ, from empty receives that dominate the bill and visibility timeouts shorter than the work, to a dead-letter queue nobody can read and Lambda scaling that takes the database down. If one describes your week, that is where the review starts.
A large share of the bill is empty receives, because long polling was never switched on
Messages reappear mid-processing because the visibility timeout is shorter than the work takes
The dead-letter queue is full and nobody can separate poison messages from transient failures
A FIFO queue is throughput-bound because every message shares one message group ID
Duplicate deliveries are reaching downstream systems that were never made idempotent
Lambda scales out against queue depth and takes the downstream database with it
Cross-account access rests on IAM policies nobody has reviewed since they were first written
Queue depth alarms fire so often that the team has learned to ignore them
A good fit if you run Amazon SQS in production, with Lambda or container consumers, and want senior engineers who know its failure modes and stay on a problem until the root cause is written down.
Not a fit for small teams with non-critical workloads. AceMQ is not a budget consultancy: we work with enterprises running business-critical and compliance-heavy Amazon SQS.
How it breaks, and the fix
A message that fails is not removed. It becomes visible again when its visibility timeout ends and is received again, until it reaches the redrive policy's maximum. With no dead-letter queue, or a very high maximum, one poison message is retried indefinitely and holds a worker on every pass.
One poison message, one shared worker pool
Two producers, three SQS queues, one pool of workers
Shared worker concurrencyAll three queues drain as fast as events arrive. Worker capacity sits well under its limit and the dead-letter queue is empty.
One malformed invoice event fails every time. It reappears after each visibility timeout and is received again, and on a FIFO queue it holds back its whole message group. Worker capacity runs hot, backlogs grow, and the services that wait on results begin to time out.
A redrive policy with a small receive count sends the message to a dead-letter queue after a few attempts, and an alarm names it. The dead-letter queue keeps it longer than the source queue does, so it is still there to inspect.
A poison message that is retried indefinitely is one of the reasons teams call AceMQ about Amazon SQS. The fix is a handful of settings and one habit: a dead-letter queue with a maxReceiveCount of a few attempts, high enough to ride out transient errors; dead-letter retention longer than the source queue's, because on a standard queue the retention clock keeps counting from the original send time; a visibility timeout longer than the slowest handler, and for Lambda at least six times the function timeout; partial batch responses so one bad record does not retry its whole batch; handlers that are idempotent; and an alarm on dead-letter depth and on the age of the oldest message. A FIFO queue needs a FIFO dead-letter queue.
Our Services
AceMQ runs four Amazon SQS service lines: architecture and pattern design, SNS fan-out, Lambda and container integration, and migration to SQS. A senior engineer leads each one, and any of them can roll into 24/7 support with a 15-minute emergency SLA.
We design Amazon SQS architectures that handle your workload reliably — with the right queue types, visibility timeout configuration, and DLQ strategy.
We design SNS to SQS fan-out architectures that decouple producers from consumers and enable independent scaling of each downstream service.
SQS as a Lambda event source requires careful configuration of concurrency, batch size, and error handling to avoid duplicate processing and runaway costs.
Migrating from on-premises queues (RabbitMQ, IBM MQ, ActiveMQ) to Amazon SQS? We map message semantics and re-architect consumers for cloud-native patterns.
Named senior engineers on your account — 15-minute emergency response, no ticket routing, no junior triage.
24/7 Support
AceMQ Amazon SQS support gives you a named senior engineer and a 15-minute first response when production is down, 24/7, on Standard and FIFO queues. Lower severities get 1-hour, 4-hour and next-day responses, and every P1 ends with a written root-cause analysis.
P1 escalation path
A replay of one P1, from the first page to the written root-cause analysis
15-minute P1 response, 24/7.
A response time, not a resolution time.
P1 opened, on-call engineer paged
Phone, email or Slack reaches the on-call senior engineer directly, whether the incident is in RabbitMQ, Kafka, Redis, PostgreSQL or Kubernetes. No web form, no tier-1 queue.
Senior engineer on your bridge
A senior engineer who already knows your environment joins a live bridge, so nobody has to re-explain your topology.
Specialists across the stack join
When an incident crosses components, the engineers who know each one join. For RabbitMQ, the path runs to the core team.
Written root-cause analysis
Root cause, the fix applied and the prevention steps, delivered after every P1.
Beyond SLAs — the model behind senior-only, zero-cold-start expert access.
Every ticket is handled by a senior SME assigned to your account — not a pool of anonymous agents. Zero cold-start. No re-explaining your environment.
Any ticket can be escalated to a live session with your named engineer via calendar booking. No gatekeeping, no approval required — direct access, always.
Quarterly health checks on your deployment plus shared intelligence from our support customers — we surface risks before they reach production.
How It Works
An AceMQ Amazon SQS engagement runs in four stages: a queue and consumer assessment of 1 to 2 weeks, written findings within a week, changes made with your engineers and staged per queue, then a handover with runbooks for redrive, dead-letter triage and consumer scaling.
1–2 weeks
We review queue configuration, visibility timeouts, redrive policies, batching and long-polling settings against how your consumers actually behave — and against what the queues are costing you each month.
Within a week
Prioritised findings covering both reliability and spend, with the reasoning attached, sized so you can take it to your own engineering leadership without translation.
Scoped per engagement
We change queue settings, consumer logic and IAM scope with your engineers rather than around them. Changes are staged queue by queue and each one is reversible.
Ongoing
Runbooks for redrive, poison-message handling and scaling behaviour, plus 24/7 support with a 15-minute emergency SLA if you want it.
A senior engineer, not an SDR. No sales sequence attached.
Customer Success
Amazon SQS work from AceMQ, including production cases where SQS fed Lambda and container consumers. Each card links to the full write-up: the environment, what AceMQ assessed or changed, and the result.
Online Marketplace Operator
A FIFO queue fed by order events drained far slower than its consumers could work, then stopped entirely behind a single malformed message. AceMQ traced both to how message groups were used.
Amazon SQSAWS
Docker
JavaBenefits Enrolment Platform
Failed enrolment events were disappearing from the dead-letter queue before anyone looked at them. The fix was a retention setting, a receive count, and a redrive path that did not create duplicates.
Amazon SQSAWS LambdaAWS
TerraformDigital 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
PostgreSQLConsumer Subscription Business
Measuring memory, duration, and concurrency across a large Lambda estate to right-size functions that were provisioned by guesswork.
AWS LambdaAmazon SQSDockerPostgreSQLFreight and Transport Technology Company
Designing event-source mapping, batching, ordering, and failure handling for a Lambda estate that had grown without a consistent event architecture.
AWS LambdaAmazon SQS
Kafka
RabbitMQDockerSolutions
AceMQ covers Amazon SQS end to end: queue and consumer consulting, 24/7 support and incident management, proactive monitoring, operations inside your AWS account or from AceMQ-managed accounts and infrastructure, a fixed-price health check and migration.
Projects
Fixed-scope work led by a senior engineer: architecture and new builds, a health check of what you run today, or a migration with a reversible cutover.
Support and managed services
Four tiers of ongoing cover once you are live. Each tier hands more of the day-to-day work to AceMQ, from a 15-minute emergency SLA to AceMQ hosting and running Amazon SQS for you.
Compare the four Amazon SQS support tiersSide by side
Amazon SQS Standard queues deliver at least once with best-effort ordering and the highest throughput. FIFO queues keep strict order within a message group and process each message once within a five-minute deduplication window, at lower throughput. Which one fits depends on how your consumers handle duplicates and where ordering truly matters.
| Question | Standard queue | FIFO queue |
|---|---|---|
| How is ordering handled? | Best-effort ordering only. | Strict order within a message group. Different groups are processed in parallel. |
| How often is a message delivered? | At least once, so duplicates happen and consumers must tolerate them. | Exactly once within the deduplication interval of five minutes, if producers send a deduplication ID or content-based deduplication is on. |
| What limits throughput? | Nearly unlimited transactions per second. | Limits per API action. High-throughput mode raises them and scales with the number of message groups, so a constant group ID caps the whole queue. |
| What does a poison message do? | It is retried until it reaches maxReceiveCount while other messages keep flowing. | It holds back every later message in its group until it is deleted or moved to the dead-letter queue. |
| How does the dead-letter queue behave? | Any standard queue. Retention keeps counting from the original send time, so set it longer than the source queue's. | It must be a FIFO queue. The retention clock restarts when the message arrives. |
| What does AceMQ review? | Visibility timeout, maxReceiveCount, long polling, batch sizes and idempotent consumers. | MessageGroupId design, deduplication, high-throughput mode, the FIFO dead-letter queue and idempotent consumers. |
For how SQS differs from a self-run broker on routing, limits and cost drivers, read RabbitMQ vs Amazon SQS compared. To see who supports SQS and other brokers in production, see message broker support options compared.
One partner, not five
Amazon SQS rarely fails in isolation. AceMQ covers the full surrounding infrastructure — so one team owns the whole path instead of pointing at each other.
Guides, articles and comparisons
Guides from AceMQ engineers on RabbitMQ versus Amazon SQS and on who supports each message broker, plus the AceMQ services for the brokers and AWS services that usually run next to SQS.
FAQs
Seven answers on Amazon SQS from AceMQ: Standard or FIFO, who answers when production fails, how an engagement starts, working with your team, duplicate processing, poison messages and dead-letter queues, and moving to or from SQS.
Still have questions about Amazon SQS? Email an Expert
Use FIFO when order within a group, or exactly-once processing within the five-minute deduplication window, matters. Use Standard when throughput and simplicity matter more. FIFO ordering only holds per message group, so a constant group ID serializes the whole queue, and Standard delivers at least once, so consumers must be idempotent. AceMQ reviews group design and idempotency first.
A named AceMQ senior engineer answers within 15 minutes, 24/7, when an SQS-based system is failing in production. Lower severities get a first response within 1 hour, 4 hours or the next day. SQS itself is run by AWS, so AceMQ works on what you control: queue settings, consumers, IAM and KMS policies, event source mappings and the design around them.
An AceMQ Amazon SQS engagement starts with a free consultation with a senior engineer, booked in our reply within 1 hour. Next comes a queue and consumer assessment of 1 to 2 weeks covering queue types, visibility timeouts, dead-letter policy, polling, consumers and access policies. Written findings and a plan follow within a week, before anything changes.
Yes. AceMQ assigns a named senior engineer from its team of 11+ who works with your engineers rather than around them, changing queue attributes, consumer settings and alarms together. Every change is staged per queue and reversible, and you keep the runbooks for redrive, dead-letter triage and consumer scaling when the work is done.
Standard queues deliver at least once, and the usual cause of visible duplicates is a visibility timeout shorter than the handler: the message reappears while the first worker is still busy. Set the timeout above the slowest handler, and for Lambda at least six times the function timeout. Extend visibility mid-processing for long jobs and make handlers idempotent.
Attach a dead-letter queue with a maxReceiveCount of a few attempts, set its retention longer than the source queue's because a standard queue's clock keeps counting from the original send time, alarm on its depth, and redrive messages once the cause is fixed. A FIFO queue needs a FIFO dead-letter queue, and a poison message blocks its message group until it leaves.
Yes. AceMQ maps exchanges, queues and acknowledgement behaviour to SQS and SNS, rebuilds consumers to be idempotent and to use long polling, runs the old and new side by side, and cuts over flow by flow with a rollback. AceMQ also says plainly when a broker fits the workload better, for example for rich routing or JMS.
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.
Let's talk
Our AWS messaging experts design, optimize, and support SQS architectures with a 15-minute emergency SLA. Tell us your workload and we'll reply within 1 hour to scope a solution.
Production incident right now?Email an Amazon SQS Architect
Send us a message and we'll follow up within 1 hour — or book a free 30-min consultation directly.
305-204-2607info@acemq.comPrefer to talk now? Call us directly or use the consultation tab to find a time that works.
