SQS Standard vs. FIFO: The Ordering and Deduplication Complexity Tax Teams Discover Too Late

Amazon SQS has earned its position as the backbone of event-driven processing across the AWS ecosystem. Standard queues provide effectively unlimited throughput. FIFO queues provide ordering guarantees and deduplication within a defined window. The AWS SQS documentation is thorough, and SQS pricing is generally a small fraction of the operational cost. The integration surface with Lambda, SNS, EventBridge, and ECS is deep and well-understood. Teams choosing SQS for webhook processing are choosing infrastructure with nearly two decades of production validation behind it.

That context is important to state plainly before discussing the complexity it carries. The question is not whether SQS is powerful — it is — but whether the teams using it for inbound webhook processing have calibrated the full scope of what they are committing to maintain.

Standard queue capabilities and tradeoffs

SQS Standard queues deliver messages with high throughput and no practical rate ceiling — but at-least-once delivery is the price of that scale. Every handler must be idempotent to be safe in production. Messages are stored redundantly across availability zones, providing durability against infrastructure failures. When a consumer processes a message and deletes it, it is gone. When a consumer fails to delete a message — because the handler raised an exception, timed out, or was interrupted — the message becomes visible again after the visibility timeout and another consumer picks it up.

AWS documentation states the delivery model directly: Standard queues provide at-least-once delivery. Messages will generally be delivered once, but occasionally may be delivered more than once. The documentation further notes that "messages may occasionally arrive out of order" as a structural property of the distributed storage system underlying Standard queues. This is not a bug characterization. It is the stated behavior of the queue type.

For webhook processing use cases, these properties have direct consequences. A webhook handler downstream of a Standard queue must be designed to handle duplicate message delivery. If the same Stripe payment event arrives twice and the handler processes both, and the handler is a billing operation, the double-processing is a correctness error. AWS's prescription for this situation is clear: "Design your application to be idempotent." The prescription is correct. Implementing it is work.

The Standard queue also lacks native ordering guarantees. For providers whose webhooks represent state transitions — order created, order fulfilled, order shipped — processing events out of order can produce incorrect application state. Teams that need ordering guarantees find that Standard queues push them toward FIFO.

FIFO queue capabilities and tradeoffs

FIFO queues address the two properties that Standard queues deliberately do not provide: exactly-once processing within a deduplication window, and first-in-first-out ordering within a message group.

AWS documentation specifies the deduplication window as five minutes. Within a five-minute window, if a message with the same deduplication ID is sent again, the second message is rejected. The message is delivered exactly once to consumers. For providers that retry webhook delivery — and most do — this deduplication window can absorb retries that arrive within five minutes of the original delivery.

Outside that window, the deduplication guarantee expires. A duplicate that arrives six minutes after the original is treated as a new, distinct message. Your application logic must handle any duplicates that fall outside the window independently. FIFO deduplication is a window, not a permanent record of seen messages.

FIFO queues also carry constraints that Standard queues do not. AWS documentation defines a maximum throughput of 3,000 messages per second with batching or 300 messages per second without it for FIFO queues. For Standard queues, this ceiling effectively does not exist. For high-volume webhook providers at scale, this throughput difference is a real architectural constraint.

FIFO message group IDs are required and must be chosen deliberately. Messages within the same group are processed in order, but messages in different groups can be processed in parallel. Choosing the grouping strategy — group by provider? by hook? by account? — has significant performance and ordering implications that need to be reasoned through before deployment.

The hidden complexity when you switch between them

Teams that begin with Standard queues and later realize they need FIFO semantics discover that switching queue types is not a configuration change. SQS FIFO queues have a different URL structure — queue names must end in .fifo — and the migration requires creating a new queue, updating all producers to write to the new queue, updating all consumers, and verifying that existing messages in the old queue are drained before the cutover.

The integration changes are not limited to the queue itself. Lambda event source mappings must be updated. DLQ configurations must be replicated. If the original Standard queue's DLQ was receiving messages, those DLQ messages are not automatically moved. They sit in the old DLQ, outside the new FIFO pipeline.

FIFO queues also require producers to supply deduplication IDs on every message. Code that was writing to a Standard queue without deduplication IDs must be updated to generate and pass a consistent deduplication ID. For webhook relay pipelines where the producer is a function receiving inbound HTTP events, that deduplication ID needs to be derived from something in the payload — typically a provider-assigned event ID, if one exists. Providers that do not include stable event IDs in their payloads require generating a deduplication ID from a hash of the payload contents, which introduces its own edge cases around payload normalization.

The migration is manageable engineering. It is not seamless, and teams that start with Standard queues often do not budget for it when they later discover they need FIFO semantics.

The idempotency tax on Standard queues

Returning to the Standard queue case: the at-least-once delivery model creates what might be called an idempotency tax — every handler that modifies external state must implement it before the pipeline is safe. For a detailed breakdown of why teams keep rediscovering this, see why every team reinvents SQS idempotency. Every message handler in the pipeline must implement idempotency before the pipeline is safe to run in production for operations with side effects.

AWS documentation is explicit on this point. The requirement is not optional. For payment processing, order management, or any operation that mutates durable state, a handler that processes the same message twice and produces the same result as processing it once is a requirement, not an optimization.

Implementing idempotency in practice requires: a stable key per message that uniquely identifies the operation, a store that records completed operations (typically a database table or Redis key with a TTL), a check on every message before processing that consults the store, transaction-safe write semantics so that two concurrent consumers processing the same message do not both pass the idempotency check, and a decision about what "processed" means for operations that partially succeed before failing.

Each of these is a solved engineering problem individually. Together, they constitute a non-trivial system that sits alongside the message handler and must be maintained, monitored, and tested. The idempotency system is effectively a mini-infrastructure layer that the Standard queue model requires you to build and operate. Teams that choose Standard queues for their webhook processing pipelines are implicitly agreeing to build and maintain this layer.

When the complexity is clearly worth it

The complexity of SQS — whether Standard with its idempotency requirements or FIFO with its deduplication windows, throughput limits, and migration friction — is clearly worth it for a specific class of workloads.

High-volume event processing at AWS-native scale, where the throughput ceiling of purpose-built tools would be a real constraint, is the canonical case for SQS Standard. Financial systems processing thousands of payment events per second, e-commerce platforms processing order events at peak load, logistics platforms tracking hundreds of thousands of shipment updates per day — these workloads need what SQS offers, and the complexity cost is justified by the scale requirement.

Order-sensitive processing where business correctness depends on events being handled in sequence — and where the team has the engineering investment to implement FIFO correctly, including message group design and DLQ handling — is the right case for FIFO. Payment state machines, inventory reservation workflows, and subscription lifecycle management are all cases where ordering matters enough to pay the FIFO complexity tax.

For these use cases, SQS is the right tool and the complexity is the expected price of reliable operation at scale.

When a simpler capture model fits

For teams using inbound webhooks from providers like Stripe, GitHub, or Twilio primarily for visibility, debugging, development, and incident recovery — rather than high-volume production event processing at scale — the queue semantics of SQS add complexity without proportionate benefit.

The webhook capture problem at the HTTP edge is structurally different from the event processing problem that SQS is designed to solve. At the HTTP edge, the questions are: did the provider's request arrive? What did it contain exactly? When did it arrive? Can we replay it to a fixed or changed handler after a failure?

HookTunnel captures inbound HTTP requests at the edge and stores the complete payload — method, headers, body, timestamp — without requiring any queue semantics. There is no deduplication window to configure, no message group ID to choose, no idempotency layer to build for the capture step. The request arrives and is stored. Replay on Pro sends the original captured payload to any endpoint you specify. See HookTunnel pricing — a flat $19/month for Pro, with no per-message billing. Also relevant: the SQS 14-day retention limit that affects DLQ-based recovery strategies when choosing between queue types.

HookTunnel's Terms do not include uptime or delivery guarantees. It is not a substitute for the processing durability SQS provides at scale. For high-throughput production processing, SQS is the right layer. HookTunnel operates at the HTTP ingress layer — the point where the provider's request first arrives — and handles the capture, history, and replay that SQS was not designed to provide.

Pro at $19 per month includes 30 days of request history and replay to any endpoint. Free accounts retain 24 hours.

Know which problem you are solving

SQS Standard and FIFO are both excellent tools for the problems they were built to solve. The complexity they carry is the honest price of the guarantees they provide. Teams that need those guarantees — ordering, deduplication, throughput at scale, durability across AZ failures — are paying the right price for the right infrastructure.

The complexity tax becomes a poor deal when the team is not actually using what the guarantees provide. A webhook inspection and debugging pipeline that does not need millisecond throughput, that does not require ordered processing for correctness, and that primarily needs "what did the provider send and can I replay it" does not need the full SQS model.

FIFO is the right answer for some problems. Know which problem you are solving before you choose the queue type — and before you agree to maintain the idempotency layer, the deduplication ID strategy, the DLQ configuration, and the migration plan for the day you realize you chose the wrong queue type for your actual requirements.