Synthetic data generation cost calculator
Estimate the cost of generating synthetic text and structured datasets with a general- purpose LLM — training examples, question-answer pairs, test cases, evaluation datasets, customer-support examples, structured JSON records, and instruction datasets. Enter how many records you need, how many a single request generates, and record complexity to get a Low / Likely / High monthly cost estimate. Everything runs locally in your browser — no data is sent to any server.
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- No credit card
- No API calls
- Estimate only
Configure your dataset generation
Estimated monthly cost
This estimates only the LLM inference cost of generating the dataset. It does not include model training or fine-tuning cost, data-storage costs, or any human review/curation of the generated data.
This is an estimate, not a billing guarantee. Actual costs depend on your provider's exact tokeniser, your real schema and examples, and your billing tier.
How this estimate works
- Generation instructions/schema
- Optional seed/example context
Generates one or more records per request, following your schema.
The generated records themselves — usually the larger share of the cost.
This is an output-heavy workload — the opposite emphasis from classification. The instructions and any seed examples are sent once per request; everything generated comes back as output, and one request can produce many records at once.
Why batching changes the math
The number of requests this workload actually makes is ceil(records required ÷
records per request) — never one request per record. Generating 1,000 records in
batches of 10 makes 100 requests, not 1,000; each of those 100 requests pays the
instruction/seed overhead once and returns 10 records' worth of output. Batching more
records into fewer requests is usually the single biggest lever on total cost, since it
amortizes the fixed per-request input overhead across more output.
Why seed examples increase input only
Seed/example context — a handful of reference examples showing the model what you want — is sent as input on every request, the same way retrieved context works in a RAG workload. It never affects how much a request generates as output.
Why record length and format complexity drive output cost
A short Q&A pair costs little to generate; a long, detailed record costs proportionally more. A structured or deeply nested output format (real JSON with multiple fields, or nested objects/arrays) adds a small amount of formatting overhead on top of each record's own content, the same way it does for the data extraction calculator's output.
What's not included: training and fine-tuning
This estimates only the generation calls that produce a dataset. It does not model what happens to that dataset afterward — model training, fine-tuning compute, data-storage costs, or human review/curation of the generated data are all out of scope.
Low / Likely / High explained
A minimal instruction, no seed examples, short records.
A standard schema, a light seed set, typical record length. Plan against this number.
A detailed schema, a heavy seed set, long records. Budget against this.
Cut your cost
Example monthly synthetic data generation costs
These worked examples use the same default assumptions as the calculator above — 10 records per request, a standard schema, light seed context, medium record length, structured JSON output — with GPT-5.4 Mini. Only the number of records required per month changes between rows.
| Usage level | Records / month | Low | Likely | High |
|---|---|---|---|---|
| Light usage | 500 | $0.4725 | $0.7181 | $1.08 |
| Typical usage | 5,000 | $4.72 | $7.18 | $10.83 |
| Heavy usage | 50,000 | $47.25 | $71.81 | $108.30 |
And here's the monthly cost for three realistic dataset sizes — 1,000 Q&A pairs, 10,000 structured records, and a large 100,000-record synthetic dataset — with GPT-5.4 Mini, each with its own batching, record length, and output format:
| Dataset | Requests | Low | Likely | High |
|---|---|---|---|---|
| 1,000 Q&A pairs | 200 | $0.3150 | $0.5325 | $0.7950 |
| 10,000 structured records | 1,000 | $9.45 | $14.36 | $21.66 |
| Large synthetic dataset (100,000 records) | 5,000 | $232.88 | $330.56 | $430.50 |
Caveats
- These estimates use the same reference instruction-prompt and answer-length token ranges as the site's chatbot calculator, and the same retrieved-context ranges as its knowledge- base setting for seed examples, accurate to ±10–15% for plain English text.
- Output-format overhead figures (plain text / structured JSON / complex nested JSON) are this project's own descriptive assumption, not a provider-published figure — no provider publishes a per-record formatting-overhead spec, since it depends entirely on the exact schema.
- When the requested record count doesn't divide evenly by records-per-request, the final request is priced at the full per-request output size — a small, deliberately conservative (never underestimating) simplification, disclosed here rather than modeled to exact last-batch precision.
- Each provider uses a different tokeniser. Actual token counts may differ ±5–10% between providers for the same content.
- Model training, fine-tuning, data storage, and human review/curation costs are not included. This tool only estimates the generation calls themselves.
- Prices shown are manually verified against each provider's official pricing page as of 2026-09-08. AI provider pricing changes frequently — verify at your provider's official pricing page before making budget decisions.
- The context-window warning above appears when a request's combined tokens exceed 80% of the selected model's context window — a safety margin before truncation or failure risk, not a hard limit at 100%.
This is an estimate, not a billing guarantee. Always confirm against your provider's usage dashboard and official pricing before committing a budget.