Global Araç
Batch Api Savings Calculator
| Provider | Real-time | Batch | SLA | Savings |
|---|---|---|---|---|
| Claude (Anthropic) | $18,750 | $9,375 | 24h | $9,375 |
| OpenAI (GPT-5) | $13,750 | $6,875 | 24h | $6,875 |
| Gemini 2.5 Pro | $6,875 | $3,437.5 | 24h | $3,437.5 |
| DeepSeek (off-peak) | $750 | $375 | 8h | $375 |
The major LLM providers — Anthropic, OpenAI, Google, DeepSeek — all offer a Batch API variant that trades synchronous response time for a flat 50% discount on input and output tokens. The economic logic: batch jobs let providers schedule inference opportunistically across cluster capacity, packing requests into otherwise-idle GPU slots and amortizing infrastructure differently than the real-time path. For customers, the tradeoff is response time — batch jobs typically return in 1-6 hours, with a 24-hour SLA cap. So the question for any workload is: do you actually need the response in the next second, or could you accept “sometime within 24 hours” for half the cost?
The calculator takes your monthly token volume (input + output, per provider) and shows the dollar savings of switching eligible workloads to batch. For a workload spending $5,000/month on Sonnet at standard rates, batching the asynchronous portions would save up to $2,500/month — meaningful for any AI-heavy product. Workloads that batch well: bulk classification or labeling (every record is independent, doesn’t need live response), nightly summarization of documents/conversations/transactions, embedding generation for vector indexes, prompt evals and benchmarks (you’re testing across hundreds of variants), training-data synthesis, and content moderation queues where 1-6 hour latency is acceptable.
What does NOT batch: any user-facing synchronous interaction (chat, search, completion-as-you-type), real-time agents, streaming responses, anything triggered by a user click and showing a loading spinner. Most production LLM apps split into hot and cold paths: hot path uses real-time API for user-facing requests, cold path uses batch for asynchronous work. Done right, this can cut overall AI costs by 30-60% with no UX degradation. Provider-specific notes: Anthropic batch caps at 100K requests per batch, returns within 24h; OpenAI batch returns within 24h; Google batch returns within 24h; DeepSeek batch is similar with slightly tighter SLAs.
Nasıl Kullanılır
- Enter your monthly input + output token volume per provider.
- Mark which workloads can tolerate 1-24h latency (bulk classification, embeddings, summarization, evals).
- Read the 50% savings calculation across all four providers.
- Compare to current spend — split-path architectures (hot real-time + cold batch) typically save 30-60% overall.
- Plan the migration: tag your async workloads, queue them through the batch endpoint instead of streaming API.
Ne Zaman Kullanılır
- Estimating savings before adopting Batch API for cold-path workloads.
- Justifying a batch-pipeline architecture to engineering leadership with concrete dollar numbers.
- Comparing batch economics across the 4 major providers (Anthropic, OpenAI, Google, DeepSeek).
- Annual budget planning — projecting AI spend with split hot/cold architecture.
Ne Zaman Kullanılmaz
- Real-time user-facing workloads — never batch what users wait for in a UI.
- Streaming responses (chat) — batch endpoints don’t support streaming output.
- Workloads requiring tool use / function calling with multiple synchronous turns — batch is single-request only.
- Tiny token volumes (<$50/month) — savings are real but operational complexity often isn’t worth it for small spend.
Yaygın Kullanım Senaryoları
- Onboarding a colleague who needs the same calculation/conversion
- Verifying a number or output before passing it on
- Quick calculation during a typical workday
- Pre-decision sanity-check on inputs and outputs
Sık Sorulan Sorular
What's the actual SLA on Batch API?
All four major providers (Anthropic, OpenAI, Google, DeepSeek) commit to 24-hour completion. Most actual returns are 1-6 hours; spikes during peak demand can push toward the 24h cap. If you need guaranteed faster turnaround, you must use real-time API at full price.
Are all model variants supported in batch?
Most are, but check provider docs. Anthropic supports Sonnet, Haiku, Opus in batch. OpenAI supports GPT-4o, GPT-4o-mini, o1, o3-mini in batch. Google supports Gemini 1.5/2.x Pro and Flash in batch. DeepSeek supports V3 and R1 in batch. Some specialty endpoints (Anthropic’s computer-use, OpenAI’s real-time API, vision-only models) are not batchable.
Does the 50% discount apply to cached input?
Provider-dependent. Anthropic prompt-caching pricing remains separate from batch — you can stack cache + batch in some cases for compounded savings. OpenAI’s Batch + cached input give similar layered discounts. Read the per-provider pricing pages carefully; the savings can be substantial when stacked.
How do I switch a workload to batch?
Three steps: (1) tag your async workloads — anything that doesn't need a live response. (2) Modify the API endpoint URL — instead of POSTing to /v1/messages or /v1/chat/completions, you upload a JSONL file of requests to /v1/batches. (3) Poll for completion or set up a webhook. Most SDKs (Anthropic Python, OpenAI Python) have built-in batch helpers.
Are there minimum batch sizes?
No strict minimums, but the per-batch overhead means very small batches (1-10 requests) don’t save much in operational time. Sweet spot is 100-10,000 requests per batch. Anthropic caps at 100,000 per batch; OpenAI/Google have similar high caps. Split larger workloads across multiple batches.
What about rate limits?
Batch API has separate rate limits from real-time API at all four providers — typically much higher daily token caps because the workload is async. Anthropic publishes batch-specific rate limits in their console. Plan accordingly: batch is great for huge volumes that would exceed real-time RPM/TPM caps.