Prompt caching saved me $47 last month
I run ralph-starter a lot. Multiple tasks per day, 3 to 7 loops each. Last month I checked my Anthropic dashboard and the total was $62, almost scrolled past it, but then I looked at the prompt caching line and did the math. Without caching? $109. I saved $47 by literally doing nothing.
Ok so let me explain why this blew my mind.
Every time ralph-starter runs Claude Code in a loop, that first iteration ships everything over: system prompt, project files, the spec, the implementation plan. All fresh tokens, expensive. But Claude's prompt caching stores all of that server-side, so on loop 2, 3, 4 those same tokens cost 90% less. You basically get a discount for repeating yourself.
Regular input: $3.00 per million tokens
Cache write: $3.75 per million (first time, slight premium)
Cache read: $0.30 per million (90% off)
Output: $15.00 per million tokens
Think about a 5-loop task. Something like 80% of the input tokens are identical every single iteration. The system prompt, the spec, your project context -- none of that changes between loops. The only new stuff is validation feedback, like test errors or lint output. So after loop 1, you are paying $0.30/M instead of $3.00/M on most of your input. Ten times cheaper, for free.
ralph-starter tracks all of this automatically. After every run you see exactly what you spent, no extra setup:
$ ralph-starter run "add pagination to /api/users" --loops 5 --test --commit
Loop 1/5
Writing code with Claude Code...
Running tests... 4 passed, 1 failed
Loop 2/5
Fixing: off-by-one in page calculation...
Running tests... 5 passed
Committing changes...
Done in 1m 48s | Cost: $0.31 | Tokens: 28,412
Cost Breakdown:
Loop 1: $0.22 (cache write)
Loop 2: $0.09 (cache read, 90% savings on 22K input tokens)
Cache savings: $0.18
31 cents for pagination, tests, and a commit. I used to spend more than that on a single ChatGPT message and still have to copy-paste the code myself.
What affects your costs
More loops = more cache hits. Simple math. A 1-loop task barely benefits because the context gets cached but never reused. A 5-loop task though? Loops 2 through 5 all ride on that cached context. This is why the ralph loop design matters -- iteration is basically free once you have paid for the first pass.
Bigger specs also mean more tokens getting cached, so the absolute savings on subsequent loops go up. A detailed spec with acceptance criteria costs more on loop 1, sure, but the savings compound after that.
Oh and the Batch API is a completely different beast. ralph-starter auto --batch uses the Anthropic Batch API at a flat 50% discount on all tokens. Catch is it takes up to 24 hours and no tool use, but for straightforward tasks where you do not need results right now? Worth it. I wrote about that in the batch processing post.
What actually helped me cut costs
My first week came in at $35 and I panicked a little. Looked at the cost tracker and immediately saw the problem: I was setting --loops 10 on everything. A task that completes in 2 loops does not need 10. I was being lazy with the flag and paying for it.
Now I keep it simple: --loops 3 for easy stuff, --loops 5 for complex things. I almost never go higher.
But the biggest cost optimization is not a caching trick -- it is writing better specs. A clear spec means the agent gets it right in fewer loops, which means fewer API calls. A spec that lands in 2 iterations instead of 7 saves you way more than any caching ever could.
Want to see your own cost breakdown?
npx ralph-starter init
Every run shows token usage and cost automatically. No extra config needed.