Tell HN: I cut Claude API costs from $70/month to pennies
27 pointsby ok_orco8 hours ago7 comments
  • LTL_FTCan hour ago
    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.
  • 44za12an hour ago
    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

  • gandalfaran hour ago
    Consider using z.ai as model provider to further lower your costs.
    • viraptor44 minutes ago
      Or minimax - m2.1 release didn't make a big splash in the news, but it's really capable.
    • tehlikean hour ago
      This is what i was going to suggest too.
    • DANmodean hour ago
      Do they or any other providers offer any improvements on the often-chronicled variability of quality/effort from the major two services e.g. during peak hours?
  • dezgegan hour ago
    Are you also adding the proper prompt cache control attributes? I think Anthropic API still doesn't do it automatically
  • DeathArrow21 minutes ago
    You also can try to use cheaper models like GLM, Deepseek, Qwen,at least partially.
  • arthurcolle8 hours ago
    Can you discuss a bit more of the architecture?
    • ok_orco7 hours ago
      Pretty straightforward. Sources dump into a queue throughout the day, regex filters the obvious junk ("lol", "thanks", bot messages never hit the LLM), then everything gets batched overnight through Anthropic's Batch API for classification. Feedback gets clustered against existing pain points or creates new ones.

      Most of the cost savings came from not sending stuff to the LLM that didn't need to go there, plus the batch API is half the price of real-time calls.