HyperLake — LLM API vs self-hosted cost calculator
FREE PLANNING CHECKLIST

Compare API spend with self-hosted inference using your own prices, request volume and GPU assumptions.

Prepare these inputs
[ ] Actual API rates and request/token volume
[ ] Candidate model, GPU price and measured throughput
[ ] Expected utilization, latency target and operating effort

Your first workflow
[ ] Enter your real workload and prices in the calculator.
[ ] Test representative requests on the candidate model.
[ ] Compare low, expected and high utilization scenarios.
[ ] Pilot at production-like load and review the measured break-even assumptions.

Acceptance checks
[ ] Confirm output quality on representative tasks.
[ ] Measure throughput and tail latency at target concurrency.
[ ] Compare actual billed cost with the model after the pilot.

Decision criteria
[ ] Compare model quality and required latency before treating API and self-hosted requests as interchangeable.
[ ] GPU utilization, batch size and idle capacity can change self-hosted economics materially.
[ ] Include platform, storage, networking, operations and migration costs alongside inference compute.

Cost and scope
Use your actual API rates, cloud prices and expected utilization. Include software and operational costs when making the buying decision.

Before switching
Verify throughput, model quality, latency and operational requirements before replacing an API service.

Sources
https://hyperlake.cloud/tools/llm-api-vs-self-hosted-cost-calculator
https://hyperlake.cloud/

Next step
No free cloud capacity is promised. Deployment scope and charges are agreed separately; AWS and Google Cloud are available today.
https://hyperlake.cloud/solutions/inference-cost

This is a planning template, not a completed assessment or a quote. No personal data is required to download it.