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Tell HN: I cut Claude API costs from $70/month to pennies

40 points by ok_orco ↗ HN
The first time I pulled usage costs after running Chatter.Plus - a tool I'm building that aggregates community feedback from Discord/GitHub/forums - for a day hours, I saw $2.30. Did the math. $70/month. $840/year. For one instance. Felt sick.

I'd done napkin math beforehand, so I knew it was probably a bug, but still. Turns out it was only partially a bug. The rest was me needing to rethink how I built this thing. Spent the next couple days ripping it apart. Making tweaks, testing with live data, checking results, trying again. What I found was I was sending API requests too often and not optimizing what I was sending and receiving.

Here's what moved the needle, roughly big to small (besides that bug that was costin me a buck a day alone):

- Dropped Claude Sonnet entirely - tested both models on the same data, Haiku actually performed better at a third of the cost

- Started batching everything - hourly calls were a money fire

- Filter before the AI - "lol" and "thanks" are a lot of online chatter. I was paying AI to tell me that's not feedback. That said, I still process agreements like "+1" and "me too."

- Shorter outputs - "H/M/L" instead of "high/medium/low", 40-char title recommendation

- Strip code snippets before processing - just reiterating the issue and bloating the call

End of the week: pennies a day. Same quality.

I'm not building a VC-backed app that can run at a loss for years. I'm unemployed, trying to build something that might also pay rent. The math has to work from day one.

The upside: these savings let me 3x my pricing tier limits and add intermittent quality checks. Headroom I wouldn't have had otherwise.

Happy to answer questions.

12 comments

[ 3.1 ms ] story [ 32.5 ms ] thread
arthurcolle
Can you discuss a bit more of the architecture?
dezgeg
Are you also adding the proper prompt cache control attributes? I think Anthropic API still doesn't do it automatically
ok_orco
No I need to look into this!
gandalfar
Consider using z.ai as model provider to further lower your costs.
andai
Do you mean with the coding plan?

I haven't tested it extensively but I found that when I used Claude Code with it, it was reasonably fast (but actual Claude was way faster), but when I tried to use the API itself manually, it would be super slow.

My guess would be think they're filtering the traffic and prioritizing certain types. On my own script, I ran into a rate limit after 7 requests!

LTL_FTC
It sounds like you don’t need immediate llm responses and can batch process your data nightly? Have you considered running a local llm? May not need to pay for api calls. Today’s local models are quite good. I started off with cpu and even that was fine for my pipelines.
44za12
This is the way. I actually mapped out the decision tree for this exact process and more here:

https://github.com/NehmeAILabs/llm-sanity-checks

andai
>Before you reach for a frontier model, ask yourself: does this actually need a trillion-parameter model?

>Most tasks don't. This repo helps you figure out which ones.

About a year ago I was testing Gemini 2.5 Pro and Gemini 2.5 Flash for agentic coding. I found they could both do the same task, but Gemini Pro was way slower and more expensive.

This blew my mind because I'd previously been obsessed with "best/smartest model", and suddenly realized what I actually wanted was "fastest/dumbest/cheapest model that can handle my task!"

DeathArrow
You also can try to use cheaper models like GLM, Deepseek, Qwen,at least partially.
deepsummer
As much as I like the Claude models, they are expensive. I wouldn't use them to process large volumes of data. Gemini 2.5 Flash-Lite is $0.10 per million tokens. Grok 4.1 Fast is really good and only $0.20. They will work just as well for most simple tasks.