The Claude Message Batches API processes many Messages API requests asynchronously as one batch. Current API documentation allows up to 100,000 requests in one batch, requires a unique developer-provided custom_id for each request, begins processing immediately, and can take up to 24 hours. Results are retrieved separately and may be returned out of input order. Anthropic currently prices batch input and output at a 50% discount versus standard API rates.
Within Claude Engineering, batches are the right primitive for offline work where throughput and cost matter more than per-request latency: evaluation, classification, enrichment, summarization, extraction and large backfills.
Use a stable custom_id for every request
Results can arrive in a different order from the submitted request list.
Use a unique custom_id tied to the application’s work item so results can be reconciled deterministically.
Do not use array position as identity.
Persist the batch manifest before submission
Store batch ID, workspace, creation time, request count, custom IDs, model/prompt version and source dataset version.
This allows the job to resume after worker restart or deployment.
The API should be polled from durable job state rather than one in-memory loop.
Batches can take up to 24 hours
Design the user/business SLA accordingly.
Do not put interactive customer requests into a batch and then poll every second hoping for real-time behavior.
Use ordinary Messages API for latency-sensitive work and batch for asynchronous queues.
Results should be treated individually
A completed batch can contain successful, errored, canceled or otherwise non-successful individual requests.
Reconcile every custom_id and store per-item status.
Retry only failed/eligible items instead of resubmitting the whole batch.
Cancellation is not instantaneous
Current API supports canceling a batch before processing ends.
After cancellation starts, some in-progress non-interruptible requests may still finish.
Your job controller should accept late results and avoid assuming zero further cost/work after the cancel request.
Batch pricing changes evaluation economics
Current Claude pricing gives Message Batches a 50% discount on standard input/output rates.
This makes nightly regression suites, bulk judge passes and data enrichment substantially cheaper.
Claude Batch Cost Optimization provides the deeper cost architecture.
Prompt caching can still matter inside batch workloads
Large repeated system prompts, schemas or documents can benefit from prompt caching where the batch/request configuration supports it.
Measure cache writes/reads rather than assuming batch discount alone makes inefficient prompts cheap.
Stable prefixes should still be designed for reuse.
Batch jobs need rate and spend capacity
Large batches can consume significant monthly budget even at discounted pricing.
Use workspace spend limits and internal job budgets.
Claude Cost Controls should include batch queue volume and expected completion cost.
Data retention and privacy still apply
Offline does not mean lower governance.
Classify input datasets, minimize sensitive data, choose the appropriate Claude platform/compliance arrangement and define how batch request/results are retained in your own systems.
Large batch outputs can become a new data store if no deletion policy exists.
Observability should track queue-to-complete lifecycle
Monitor batch age, request count, processing status, success/error/cancel ratios, cost, model version and source dataset.
Alert when a batch approaches the expected completion window or when error rates jump after a prompt/model change.
This turns batch processing into a managed data pipeline rather than a fire-and-forget API call.
Claude Message Batches succeed when asynchronous work is idempotent and resumable
The mature system uses stable custom IDs, durable manifests, per-item result reconciliation, bounded retries, cancel-aware logic, spend controls, privacy retention and clear operational metrics.
Batching is valuable because it reduces cost and supports massive offline throughput, but only if the application can safely resume and explain every work item after hours of asynchronous processing.
Batch sizing should balance operational recovery with throughput. A single 100,000-request batch maximizes grouping efficiency but can make one configuration mistake expensive. Split massive backfills into logical shards by dataset, customer, date, or prompt version so bad output can be isolated and selectively rerun.
Input validation should happen before submission. Check required fields, token estimates, model availability, schema validity, custom ID uniqueness, and internal authorization. Reject broken records locally rather than paying to submit thousands of requests that will fail for the same predictable reason.
Batch manifests should be immutable after submission. If source data or prompt version changes, create a new batch/shard rather than editing the meaning of an existing batch ID. Reproducibility depends on knowing exactly which inputs and configuration produced each result.
Polling should use a reasonable cadence and exponential backoff rather than tight loops. Batch processing can take hours, so second-by-second polling wastes API/network resources without reducing completion time. Persist the next-check time in the job system so workers can sleep between polls.
Result ingestion should be idempotent. A worker may download the result stream twice after restart or timeout. Use `custom_id` plus batch ID as a unique key and upsert the processed outcome so duplicate downloads never create duplicate database rows or repeated downstream actions.
Batch outputs should pass the same semantic/business validators as synchronous responses. A discounted asynchronous result is not inherently more trustworthy. Apply structured-output parsing, safety checks, data-quality rules, and human review thresholds before writing the result into production systems.
Backfills should be rate-controlled against downstream consumers. A batch can complete thousands of records quickly, and the result processor might overwhelm a database, search index, ticket API, or notification service. Decouple result ingestion from downstream writes with queues and per-service limits.
Workspace isolation is useful for large batch programs. Put recurring evals, customer backfills, and experimentation in separate workspaces or internal budget categories so one offline job cannot exhaust production spend/rate capacity. Use the batch’s workspace attribution consistently.
Retention should include raw input only as long as needed. If the source dataset already exists in an authoritative store, the batch job may need only source IDs and derived outputs after processing. Avoid duplicating sensitive source text into another permanent batch-results database without purpose.
Batch operational reviews should compare error rates and cost by model/prompt version. A small prompt change that adds one invalid tool/schema path can fail tens of thousands of records. Canary a small shard first, then expand only after quality and cost look normal.
Model/version changes should be canaried with a small batch before the main backfill. Batch economics make it tempting to submit all 100,000 items immediately, but one prompt/schema/model incompatibility can waste the whole job. Run a representative 100–1,000 item shard and inspect results before scaling.
Custom IDs should be stable but non-sensitive. Use internal opaque identifiers rather than email addresses, names or other PII where the ID appears in logs or operational dashboards. Map the opaque ID back to source data inside the trusted application database.
Batch result files should be checksum- or version-tracked if they feed regulated data pipelines. Preserve when the result was downloaded and which batch/request configuration produced it. This makes later reconciliation possible if the same source item is processed again under a newer model.
Data-quality pipelines should separate model failure from source-data failure. If the input record is malformed or missing required fields, mark that separately from API errors or low-quality model output. This prevents retry logic from repeatedly resubmitting source records that can never succeed until upstream data is fixed.
Batch SLA should include downstream processing time. A batch finishing in six hours does not mean the business job is done if parsing, validation and indexing require another four hours. Track end-to-end age from source-ready to final committed result.
Batch-processing pipelines should produce a completion manifest that records every custom ID and final state. Compare submitted count, result count, errors, cancellations and retries before declaring the batch complete. Missing one result in a million-row enrichment job can be difficult to discover later if completion is based only on the batch-level status.
Large offline workloads should be separated from interactive production at both budget and operational layers. A batch backfill can be scheduled during quieter periods, use its own workspace and consume a controlled daily budget. This prevents offline jobs from competing with customer-facing traffic for organizational limits.
Use Batch Inference and Scheduled Scoring to design the surrounding queue, storage and reconciliation architecture. The Anthropic Batch API solves model execution; the application still needs dependable ingestion, validation and downstream commit stages.
Sensitive result files should be encrypted and access-controlled in the application storage used after download. Batch output can contain the same regulated or confidential information as interactive responses, but its large scale makes accidental bulk export more damaging. Restrict who can retrieve or reprocess archived results.
Lifecycle policies should delete completed batch artifacts once downstream systems have committed the result and audit requirements are satisfied. Keeping every request and response forever because storage is cheap creates unnecessary privacy and breach exposure.
Batch governance should include a data-owner signoff for very large jobs. The source owner should confirm the dataset version, permitted processing purpose, expected record count and downstream destination before submission so an inexpensive batch cannot accidentally process the wrong snapshot at massive scale.
Batch workflows should preserve the request payload, batch identifier, submission time, and result location so retries are deliberate. If a client or network fails mid-process, the operator should be able to resume or reconcile work without guessing which prompts were processed.
