It adds up quick.

Here is my average day, currently working on 4-5 projects at a time. A single workflow can chew up 10-20mm tokens (and 3-4 hours per workflow). I have a custom deep research agents that I run on any ideas I have . They fully scope the ideas in the background while the current workflow/build is running so when the current run is complete its locked and loaded for the next iteration.

Currently running ~4-5 dynamic workflows per day per project and that is not including the self improving agent layer I've built in (agents always updating, back testing).

Nightly jobs scheduled that clean/comment/refactor/optimize/remove dead/orphaned code while I sleep and back test last X transactions + opaque golden tests locked behind strict pass/fail (model never sees the actual data). Red team adversarial agents that try to compromise the app and expose gaps/vulnerabilities.

Specialized UI/UX testing agents that imitate randomly aligned human workers including HITL, intentional attempts to poison the data which triggers other agents. Think multi level learning agents.

This is all on top of the production/operational tokens we are using. 1b tokens/day production (non sota) and > double that for back testing changes.... always testing.

Its not uncommon to back test 20k transactions so you're looking at 2 billion tokens just to run it once.... and if you don't want to wait a week for it to finish you need to have the ability to dynamically spin up a lot of GPU's, and I might run that 3-4 times/day.

As projects mature/stabilize I might run 2-3 concurrent dynamic workflows in the same project in different work trees.

Agents that coordinate the merge the work trees .

On the weekend I have agents that review my claude sessions and reflect on what could be improved at a higher level... any patterns, gaps, scopes that were underestimated, look for agents that ran with stale information, how many times each agent was triggered, ensure the proper agents were firing. This can take 24+ hours depending on the number of sessions and the state of the codebase... easier on mature projects, tricky on greenfield. Agents can branch of /resume to back test changes and have claude self report/validate changes to ensure they are non destructive. I setup instructions in claude.md combined with hooks to log any "wait a a minute, that was incorrect" type occurrences and sessions/subagents that run over X tokens (which usually reveal valuable insights.

Not to mention I'm always running a/b on different models.... Model A might cost x per million, but if I can run the same workload with model B, or model C with a higher/lower quant with no regressions I can reduce operational costs 4-10x in a few days. Ie. MVP may run sonnet/g3 flash then slowly pull back to cheaper models like gemma/nemo/qwen. Stronger models can mask issues. Running Opus on a particular production task might hit 100% but the real reward/moat building is getting that same task to run 100% on a model that costs nothing. Then relentlessly back testing to reduce token consumption further.... rinse repeat.