Transactional SMS API Batch Sending: Bulk Requests, Payload Design, Partial Failures and Idempotency

Why batch APIs matter

Batch APIs reduce HTTP overhead when an application needs to submit many messages. They can improve network efficiency, but they also create larger failure domains and require careful result handling.

Batch size

A batch should have a documented maximum based on payload size, processing time and provider behaviour. Very large batches can increase latency and make partial failures difficult to recover.

Per-message identity

Every message in a batch should have its own logical ID or client reference. The batch ID identifies the request while message IDs identify individual business operations.

Partial success

A batch can contain accepted, rejected and duplicate messages simultaneously. The API response should therefore provide per-message results rather than one boolean success value.

Idempotency

Batch retries can accidentally resubmit successful messages. Per-message idempotency keys allow the platform to return the original result for already accepted operations.

Validation strategy

Validate the complete batch structure before processing, then perform per-message validation. Decide clearly whether one invalid item rejects the entire batch or only that item.

Rate limits

A batch endpoint still consumes messaging capacity. The platform should count actual messages or segments according to the documented policy rather than treating one HTTP request as one SMS.

Retry design

Retry only failed or uncertain items when the API contract permits it. Replaying the entire batch can create duplicates and unnecessary provider traffic.

Payload optimization

Use compact JSON and avoid repeating static metadata when the API can represent it at batch level. However, do not sacrifice clarity if customers need per-message customization.

Observability

Record batch ID, item count, accepted count, rejected count, processing duration and per-message identifiers. These fields simplify support investigations.

Security

Batch endpoints require the same authentication, authorization and tenant isolation as single-message endpoints.

Implementation checklist

Define batch limits, message identity, partial success semantics, idempotency, validation, rate counting, retry rules, observability and security.

Batch response contract

A batch response should make partial success unambiguous. Return the batch ID, total items, accepted count, rejected count and a result for each submitted item or a documented reference to retrieve those results.

Each item should contain a stable message ID or client reference, status, reason code where applicable and an idempotency outcome. The response should not force customers to infer which messages succeeded from HTTP status alone.

Atomic versus partial semantics

Some workflows may require all-or-nothing validation, while others benefit from accepting valid items and rejecting invalid ones. The API should explicitly choose one model.

A practical compromise is structural validation at batch level followed by independent per-message validation. This prevents malformed JSON from creating partial work while allowing one invalid phone number to fail without blocking thousands of valid messages.

Batch retry strategy

If a batch contains 980 accepted messages and 20 failures, the customer should be able to retry the 20 failed items without resubmitting the 980 successful ones. This is where per-message identifiers become essential.

For uncertain results, the customer should use the documented idempotency key rather than assuming a network timeout means every item failed.

Large-batch safeguards

Large batches should have payload, item-count and processing-time limits. If customers need to submit millions of messages, an asynchronous import or campaign API may be more appropriate than one enormous HTTP request.

Design principle

Batching should reduce transport overhead without turning one request into an uncontrollable failure domain.

Batch processing architecture

For large batches, the public API can persist a batch record and individual message records, then place a batch-processing job on a queue. This keeps the HTTP request short and allows the system to process items under normal provider limits.

The batch record can expose progress such as total, accepted, rejected, pending and completed counts. Customers can then query progress without keeping one HTTP request open for the entire operation.

Per-item status

Each item should have a deterministic status such as accepted, rejected, pending or failed. Provider submission and delivery states can then continue through the normal message state machine.

Do not use the batch status as a replacement for individual message status. A batch can be partially successful and may contain messages at very different lifecycle stages.

Batch cancellation

Cancellation of a large batch should stop future processing where possible but should not pretend that messages already submitted to a provider can be withdrawn. The API should expose the distinction between queued, cancelled and already-submitted items.

Operational checklist

Test mixed-validity batches, duplicate items, partial provider failures, client retries, oversized requests, cancellation races and large asynchronous batch progress updates.

Practical example

A customer submits 1,000 messages in one batch. Five contain invalid destinations, 10 encounter temporary provider uncertainty and 985 are accepted. The response should identify the 15 exceptions individually. The customer can then retry only the relevant items using their original idempotency keys.

If the platform instead returns one 'batch failed' response, the customer may retry all 1,000 and create duplicate traffic. Clear partial-success semantics therefore directly improve reliability.

Developer tooling

SDKs should expose a batch result object that separates accepted, rejected and uncertain items. Helper functions can construct retry payloads from failed items while preserving identifiers. This reduces application code and makes safe retry behaviour easier to implement.

What to document

Document maximum items, maximum payload size, per-item result schema, all-or-partial validation semantics, idempotency requirements, rate counting and batch cancellation behaviour.

Batch API anti-patterns

Avoid one HTTP transaction that performs synchronous provider submission for hundreds of messages. Avoid returning only one success flag. Avoid using a batch ID as the sole idempotency identity. Avoid retrying the complete batch when only a subset failed.

Testing at scale

Test batches at minimum size, normal size and maximum permitted size. Include mixed message lengths and Unicode content because payload size and SMS segmentation can change processing cost.

Final reference checklist

Confirm per-item identity, partial result semantics, safe retry, batch limits, asynchronous processing for large jobs and clear progress reporting.

Engineering takeaway

Batch APIs are most useful when they preserve individual message correctness. The batch is a transport convenience; message identity, status, billing and idempotency still belong to each individual SMS operation.

Operational recovery

If a batch-processing worker fails midway, resume from durable per-item state rather than restarting the entire batch. This prevents already accepted items from being submitted again.

Security and compliance

Apply the same tenant authorization and sensitive-data controls to batch result retrieval as to single-message APIs. A batch can contain many recipients, so an accidental access-control error has a larger blast radius.

Implementation note

For high-volume batch submission, separate batch orchestration from individual message processing. The batch service should create durable item records, while the ordinary message pipeline remains responsible for validation, routing, provider submission and lifecycle updates. This architecture keeps one reliable path for single and batch messages and makes operational behaviour easier to test.

Reference standard

A batch is complete only when every item has reached a documented terminal or recoverable state. The batch summary should never hide individual message outcomes.

Final engineering guidance

The safest batch implementation treats the batch as an orchestration object and every SMS as an independent durable operation. This allows partial success, individual retries, precise billing and reliable recovery. It also means the same provider adapters, state machine and idempotency controls can be reused rather than creating a separate 'bulk' sending engine with different correctness rules.

Reference example

If a customer needs to retry only rejected batch items, the API should make that possible without reconstructing the original request manually. A downloadable result or status endpoint can expose the message IDs and failure reasons. The retry request can then use those IDs and the same idempotency scope, reducing the risk of duplicate accepted messages.

Final test

Confirm batch progress remains correct after worker restart, partial provider failure and client retry.