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Use batch LLM jobs when summaries can wait and requests are independent; use synchronous or realtime calls when a person or downstream transaction needs an answer now. OpenAI, Anthropic and Google each document a 50% batch price reduction against their standard or synchronous rates. That percentage is not a promise that your total bill will fall by half: model mix, input and output tokens, caching, retries, failed work, tools and negotiated rates determine the effective result.
For finance and engineering teams, the harder problem is attribution. A discounted batch invoice is useful only if every request can be tied to a team, feature, tenant or source dataset, then reconciled with returned usage and provider billing. The guide below compares the two execution modes and shows how to build that accounting trail.
Batch or realtime: the decision in one minute
Choose a batch job if all of these conditions are true:
- The summarization workload is offline, such as a document archive, evaluation set or overnight digest.
- Each request can run independently and can be retried without an interactive session.
- A provider-defined asynchronous window—OpenAI documents a 24-hour completion window and Google a 24-hour target—fits your service-level objective.
- You can store request identifiers and reconcile results after the job finishes.
Choose synchronous or realtime calls when a user is waiting, a workflow must continue immediately, or a missed deadline costs more than the discount. Realtime responses are consumed in the request flow, although exact latency and streaming behavior depend on the model and service tier. A hybrid design is common: send the backlog to batch and reserve synchronous capacity for urgent or failed items.
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What actually changes between the two modes
| Decision factor | Batch job | Synchronous or realtime request |
|---|---|---|
| Execution | Requests are uploaded or submitted as a batch object, processed asynchronously and collected later. | The caller waits for each response in the same interaction or workflow. |
| Latency | OpenAI describes a 24-hour completion window; Google’s Gemini documentation describes a 24-hour target. Timing and guarantees are provider-specific. | Designed for interactive use; latency varies by model, load and service tier. |
| Published price | OpenAI, Anthropic and Google each document a 50% discount versus their standard or synchronous prices. | Standard model rates are the baseline. Google also documents Flex and Priority synchronous tiers with different reliability and prices. |
| Preemption | Policies differ. Google describes Batch as sheddable, with queuing and retry behavior. | Google describes Priority as non-sheddable and more expensive; Standard is its default synchronous tier. |
| Result handling | Results can arrive out of order and must be matched to submitted identifiers. | The response is normally available in the request context; streaming support depends on the API. |
| Accounting | Requires a durable mapping from submitted ID to internal workload and returned usage. | Still requires workload tags and usage reconciliation, even though the response is immediate. |
These are service-level distinctions from provider documentation, not a claim that identical prompts, models or limits are available across every endpoint. Confirm current model eligibility and endpoint constraints before moving production traffic.
How much cheaper is a batch LLM API?
The published headline is 50% off the comparable standard or synchronous price at each of the three reviewed providers:
- OpenAI’s Batch API guide states a 50% cost discount.
- Anthropic’s batch-processing documentation states that batch requests cost 50% of standard API prices.
- Google’s Gemini optimization guide describes Batch at 50% of standard cost and a target turnaround of up to 24 hours.
The discount applies to qualifying token usage, not automatically to every line on an invoice. Your effective saving can be smaller when a workload has a high retry rate, expensive output tokens, uncached repeated context, tool calls or separately billed features. It can also differ when you use a negotiated enterprise rate or a cloud platform’s billing arrangement.
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effective cost = (billable input tokens × input rate) + (billable output tokens × output rate) + cache charges + tool charges + retry charges + other applicable fees
Run that calculation twice—once with the batch rate and once with the synchronous rate—using the same observed token mix. Report savings as (synchronous equivalent − batch actual) ÷ synchronous equivalent, and state which requests, retries and ancillary features were included.
Provider-specific terms you must check
OpenAI Batch API
OpenAI’s guide describes a file-based workflow: collect requests into one file, start a batch, poll its status and retrieve the collected results. The current guide lists Responses, Chat Completions, Embeddings, Completions, Moderations and image endpoints. A single input file must use one model, and model availability is not universal, so verify the model reference before submission.
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OpenAI documents separate batch rate limits and no streaming. Its support guidance says the completion window is 24 hours and that completed work remains billable if a batch is canceled or expires. That means cancellation is an operational control, not a guaranteed way to avoid charges. The FAQ also says zero-data-retention terms do not apply to the Batch API; organizations with retention obligations should review the current policy before adoption.
Each request needs a unique custom_id. Keep that value unchanged in your own database alongside team, feature, tenant, dataset and run identifiers. The complete technical workflow is in OpenAI’s Batch API documentation; billing and model rates are in OpenAI’s pricing documentation; operational questions are covered in the Batch API FAQ.
Anthropic Message Batches
Anthropic says most Messages API requests can be batched independently and that batch processing offers significant cost savings. Its documentation recommends meaningful custom IDs because returned results are not guaranteed to preserve submission order. The same documentation advises monitoring status, retrying failed requests appropriately and dry-running a request shape before sending a large job.
Some stateful or synchronous-only parameters are unsupported in a batch. Check the current list in Anthropic’s batch-processing documentation and compare current rates in Anthropic’s pricing documentation. Treat each batch item as an independent request: do not assume conversation state, ordering or an interactive tool session will carry from one item to another.
Google Gemini service tiers
Google’s optimization guide distinguishes three relevant choices. Standard is the default synchronous tier. Flex is synchronous, sheddable and documented with a 50% discount. Batch is asynchronous, sheddable and has a target turnaround of up to 24 hours. Priority is synchronous and non-sheddable; the same guide describes it as priced 75–100% above Standard. These terms can change, so verify the live guide and model-specific pricing before budgeting.
Google describes queuing and retry behavior for sheddable work. Build your own retry and reconciliation logic anyway: a provider retry can affect timing and usage, while your accounting system must still decide whether an attempt was successful, duplicated or billable.
Build cost attribution as a data pipeline
Neither a batch discount nor an invoice automatically allocates shared spend to your internal teams. Attribution is an application responsibility. Use a request-level ledger that survives submission, processing and reconciliation.
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1. Define the dimensions finance needs
Agree on a controlled vocabulary before the first production run. Typical dimensions are team_id, product, feature, tenant_id, dataset_id, environment, model, service_tier and run_id. Keep dimensions separate from the provider’s request identifier so a provider ID can change without losing your internal reporting keys.
2. Generate an immutable request record
For every summary, write a record before submission containing:
- Your internal request UUID and the provider’s
custom_idor equivalent. - Provider, model, endpoint, API or service tier and batch-run identifier.
- Team, feature, tenant, source document and dataset metadata.
- Prompt-template version, content hash and an estimate of input and output tokens.
- Creation time, submission time, priority and data-retention classification.
Do not put sensitive document content into accounting labels. Store a reference or hash, and apply the provider’s retention and access controls to the underlying text.
3. Persist the submission-to-result mapping
Batch results can arrive in a different order from the input. Join on the stable request ID, never on row position or completion time. Store the raw result location and a normalized status such as succeeded, failed, canceled, expired or retried. Preserve duplicate attempts rather than overwriting them; otherwise a retry can disappear from the cost picture.
4. Capture usage at request level
Record input tokens, output tokens, cached input or output usage where exposed, tool or image charges, provider error codes, latency, attempt number and the final outcome. Keep both the provider-reported usage and the rate-card version used to calculate cost. Rates and model names change, so a later audit must be able to reproduce the original calculation.
5. Reconcile with provider usage and invoices
On a regular schedule, compare the ledger’s sum of billable usage with provider usage exports and the invoice. Investigate unmatched records, partial failures and canceled or expired jobs. OpenAI specifically states that completed requests in a canceled or expired batch remain chargeable; your ledger should therefore distinguish “not returned” from “not billed.” No reviewed provider states that its invoice automatically allocates shared spend to your teams or product features.
6. Publish an allocation policy
Decide how shared costs are assigned. Directly attribute request-level usage where possible. For shared prompts, preprocessing or platform overhead, use a documented rule such as proportional input tokens or equal allocation by successful summaries. Keep the rule versioned and report allocated overhead separately from direct model charges so product owners can see what is controllable.
Submission and reconciliation workflow
- Classify the work. Mark each item as interactive, deadline-sensitive or offline. Route interactive items to synchronous calls; group independent offline items into a batch candidate.
- Validate model and feature support. Check the provider’s current model reference, endpoint limits, context window, tool requirements, streaming needs and retention terms. For OpenAI, remember that one input file uses one model and that Batch does not stream. For Anthropic, remove unsupported stateful or synchronous-only parameters.
- Dry-run a small sample. Submit representative prompts, including long documents, empty text, malformed records and multilingual content. Verify output schema, token usage, error handling and identifier preservation before creating a large job.
- Write the ledger first. Generate a unique provider request ID and internal metadata for every item. Use idempotency controls or a content hash so a network retry does not silently create a second billable request.
- Submit and store the batch receipt. Persist the batch ID, file or object location, submission timestamp, selected model, tier and expected completion window.
- Monitor status. Poll according to the provider’s limits, record status transitions and alert before your business deadline. Do not treat “submitted” as “completed.”
- Download and validate results. Join by request ID, verify that every expected item has exactly one final outcome, validate the summary schema and quarantine malformed or duplicated responses.
- Retry selectively. Retry transient failures with a new attempt ID linked to the original. Do not blindly resubmit the entire file; that can duplicate successful work and obscure attribution.
- Close the run. Mark successful, failed, canceled and expired items, attach final usage, calculate cost and reconcile totals against provider reporting and the invoice.
- Review service-level performance. Compare completion time, failure rate, retry volume and effective cost with the synchronous baseline before expanding batch volume.
When the 50% discount does not produce 50% savings
Token mix changes
A batch migration can change prompts, truncation behavior or output length. If output tokens rise, the discounted bill may still exceed the old workload’s cost. Compare input and output tokens per completed summary, not only the number of documents.
Retries and partial processing
Provider retries, client retries and validation retries are separate attempts. Count each billable attempt and report the reason. A batch that finishes cheaply but requires a high percentage of reprocessing may have a higher effective cost than a stable synchronous path.
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Caching and repeated context
Cached tokens can have different rates from uncached tokens. Preserve cache usage in the ledger and calculate the batch and synchronous scenarios with the same cache assumptions.
Tools and multimodal inputs
Search, tool calls, images and other features can carry charges beyond ordinary text tokens. Include them in the request-level record and confirm whether the selected batch endpoint supports them.
Capacity and opportunity cost
Batch has separate limits and may wait in a queue. If a delayed summary blocks revenue, customer support or a downstream data refresh, the economic cost of waiting belongs in the decision even though it is absent from the token invoice.
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Deadlines and service levels
Write a deadline in business terms, such as “all nightly summaries available by 06:00 UTC,” then reserve enough time for validation and selective retries. A provider’s 24-hour window or target is not the same as a guarantee that every item will be usable at the end of that period.
Sheddable versus protected capacity
Google’s documentation labels Batch and Flex sheddable and Priority non-sheddable. Sheddable capacity can be appropriate for backlog work, but a user-facing escalation should use a tier whose reliability matches the consequence of delay. Other providers expose different controls; do not transfer Google’s labels to another API.
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Data retention
Review retention, logging and deletion terms for the exact endpoint and account configuration. OpenAI’s Batch FAQ says zero data retention does not apply to Batch. A discount cannot override a contractual or regulatory requirement.
Idempotency and duplicate summaries
Use a deterministic source-document hash plus a run ID to detect duplicate submissions. Keep the original and retry IDs linked, and require a human or automated rule to choose which valid summary becomes canonical.
A practical routing policy
Implement routing as a policy rather than an ad hoc developer choice:
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors| Workload signal | Recommended route | Reason |
|---|---|---|
| User is waiting or a transaction cannot proceed | Synchronous or realtime | Latency has direct product impact. |
| Millions of independent documents with a 24-hour window | Batch | Asynchronous execution and the documented 50% rate can reduce token cost. |
| Overnight job with a firm morning deadline | Batch with an early submission and synchronous fallback | Captures the discount while protecting the deadline. |
| Highly stateful conversation or live tool session | Synchronous unless the provider explicitly supports the required state | Batch items are generally independent and may omit synchronous-only features. |
| Compliance-sensitive content under a zero-retention requirement | Only an endpoint whose current terms satisfy the requirement | Batch retention terms can differ from synchronous terms. |
Failure modes and fixes
Results are assigned to the wrong document
Cause: joining by file order. Fix: join by the provider’s stable request ID, validate one final result per expected ID and quarantine unknown IDs.
The invoice is lower, but no team sees the saving
Cause: a shared account without internal dimensions. Fix: require team, feature and dataset metadata before submission, then publish direct and allocated costs separately.
A canceled job still incurs charges
Cause: completed items were processed before cancellation. Fix: treat cancellation as a stop for unstarted work, reconcile completed usage and avoid relying on cancellation as a cost reversal.
A batch request is rejected
Cause: unsupported model, endpoint, parameter or request shape. Fix: dry-run a representative item, check current provider documentation and maintain a compatibility test in deployment.
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Cause: resubmitting successful items after a timeout or lost result file. Fix: make retries item-level and idempotent, retain attempt history and reconcile before resubmitting.
Metrics to review each month
- Effective cost per completed summary, split by model and tier.
- Input, output and cached tokens per source document.
- Batch discount captured versus synchronous-equivalent cost.
- Submission-to-result time at median and high percentiles.
- Failure, retry, cancellation and expiration rates.
- Percentage of spend with complete team, feature and tenant attribution.
- Unmatched usage or invoice variance after reconciliation.
Track these metrics by workload class. A single blended average can hide an inexpensive archive job and an expensive, retry-heavy customer workflow.
Bottom line: optimize for effective cost, not the headline discount
Batch is the financially sensible default for independent summarization that can wait, and the three reviewed providers document a 50% reduction against their comparable standard or synchronous rates. Realtime remains the right tool for interactive latency, stateful behavior or deadlines that cannot tolerate queueing. The winning architecture routes work deliberately, records a durable request identity, captures usage and retries, and reconciles the ledger to provider billing. Only that evidence shows whether your workload—not merely the rate card—actually became cheaper.
Frequently Asked Questions
Can I send a batch job for a conversation that depends on earlier turns?
Treat batch items as independent unless the provider’s current endpoint explicitly supports the required state. For a live, stateful conversation, synchronous execution is usually the safer design because submission order and interactive context are not guaranteed in a batch.
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Should a failed batch item be retried in another batch or synchronously?
Choose based on the failure and deadline. Retry a transient, non-urgent item in batch with a new linked attempt ID; route an urgent item synchronously. In both cases, preserve the original ID and count every billable attempt.
How do I prove that an internal team was charged the right amount?
Keep a request-level ledger containing stable IDs, team and feature dimensions, model and tier, token usage, cache or tool usage, outcome and retry history. Reconcile its totals with provider usage exports and the invoice, then document any shared-cost allocation rule.
Is a provider’s 24-hour batch target a guaranteed completion deadline?
No. OpenAI documents a 24-hour completion window and Google a 24-hour target, but timing and operational guarantees are provider-specific. Set your own submission cutoff, validation buffer and synchronous fallback for business-critical deadlines.
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