API Cost Calculator for OpenClaw Workloads
Estimate OpenClaw API costs before you deploy. Compare daily, monthly, and yearly token spend across model options.
Quick orientation
When to use this tool
Use this calculator when you need a fast, realistic answer to the question that usually shows up right before launch: what will this workload cost once real usage starts?
- 1
Before picking a default model for a new assistant or agent team
- 2
When comparing free, budget, and premium model mixes
- 3
When setting client budgets, rate limits, or monthly usage caps
Gemma 3 27B
FreeDaily
$0.0000
Monthly
$0.0000
Yearly
$0.0000
Per 1K requests
$0.0000
Export
Interpret the numbers
What to do with the output
The raw estimate matters less than the decision it helps you make. These are the figures that usually deserve the most attention.
Per request
$0.0000
Useful for sanity-checking whether a single interaction is cheap enough to scale.
Monthly projection
$0.0000
Usually the most practical number for a budget, client proposal, or internal cap.
Yearly projection
$0.0000
Helpful when a workflow is likely to become a permanent part of the stack.
What this tool helps you decide
A rough cost estimate is often enough to rule out a bad model choice early. This page is meant to shorten that decision loop before you build the rest of the workflow around the wrong assumptions.
It also helps when you need to explain cost tradeoffs to a teammate or client in plain language instead of raw token math.
- Check whether a higher-quality model is still affordable at your expected volume
- Pressure-test the impact of longer prompts, tool calls, or bigger outputs
- Turn an experiment into a usable monthly budget estimate
How to interpret the estimate
The best number to focus on depends on the decision in front of you. Per-request cost is useful when you are thinking about margins or heavy usage. Monthly cost is usually the number that matters for budgeting and approval.
If two models are close on cost, the cheaper one is not automatically the right choice. Reliability, output quality, and how much prompt cleanup you need afterwards all change the real economics.
- Use daily cost for fast experimentation and short campaign planning
- Use monthly cost when setting caps, package pricing, or internal budgets
- Use yearly cost when a workflow is likely to become permanent infrastructure
Good next step after estimating cost
Once the numbers look sane, pair this with prompt trimming and model routing decisions. Cost problems usually start in prompt size or an overly expensive default model, not in billing surprises alone.
That is also the point where it helps to compare against your real prompt sizes instead of guessed averages. Small prompt inflation repeated thousands of times is where budgets quietly drift.
Common planning mistakes
What usually throws off API cost estimates
Most bad estimates are not math errors. They come from optimistic assumptions about prompts, usage, or the number of calls hidden inside one user action.
Related tools
Keep the decision moving
Most tool decisions connect to a second task right away. These are the next pages worth opening if you want fewer surprises later.
Token Counter
Count prompt and response tokens so you can trim prompts and predict spend before you send.
API Tester
Probe OpenClaw and related APIs quickly so you can debug payloads, auth, and response shape in one place.
Env Formatter
Clean up and convert environment variables between formats so config handoffs stop being error-prone.
Learn next
Turn the estimate into a better setup
If the tool solved the immediate question, this is the next place to go for the broader workflow, tradeoffs, and implementation detail.
Read the OpenClaw cost optimization guideFAQ
Is this calculator good enough for production budgeting?
It is useful for planning and comparing options, but you should still confirm live pricing and your real prompt sizes before making a final production commitment.
Why compare cost before building the workflow?
Because model choice affects everything after it, including prompt design, guardrails, throughput, and whether the finished workflow is commercially viable.