Credits connect plan economics to actual API and export work: routes, depth, replay, concurrency, and file delivery can carry different cost profiles. Credits are a capacity and planning concept. They help teams size API workloads, Data Catalog exports, and heavier historical jobs before production code starts running broad loops. For current account terms, pricing, and checkout details, use the live pricing page and your dashboard. For client design, use this page with Rate Limits.Documentation Index
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Credit Surfaces
| Surface | What credits help plan | Design implication |
|---|---|---|
| REST API | Request volume, heavy route usage, and historical pulls | Start with one route, then batch by symbol and window |
| WebSocket replay | Replay speed, channel count, and historical event volume | Bound windows and store replay configuration |
| Data Catalog | File-style exports and subscriber credit offsets | Use export credits for purchase-style workflows, not infinite API loops |
| L3/L4 depth | Larger payloads and reconstruction-heavy workflows | Separate high-depth jobs from simple freshness checks |
| Automation workflows | Scripts and agent-assisted jobs that can accidentally widen too fast | Require route, symbol, limit, and concurrency before execution |
How To Think About Credits
Credits should make the workflow more explicit. A job should know the route family, symbol set, data family, time window, page size, concurrency, retry budget, and output destination before it starts. If the job cannot state those values, it is not ready to spend broadly. Do not use credits as a substitute for correctness. A job can be under budget and still use the wrong route family. A job can have enough credits and still produce bad research if it ignores freshness or incidents. Treat credits, rate limits, and data quality as separate gates.Workflow Rules
Probe before spend
Run one bounded request, inspect the response envelope, and preserve
meta.request_id.Estimate by route family
Separate Hyperliquid core, Hyperliquid Spot, HIP-3, HIP-4, and Lighter work instead of summing them into one vague market-data job.
Split heavy jobs
Batch historical pulls and replay windows so work can pause, resume, and retry without duplicating output.