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Project ideas from Hacker News discussions.

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

Hacker News Discussion

📝 Discussion Summary (Click to expand)

1. Cost-Optimization and Architecture Strategy There was a strong emphasis on reducing expenses through strategic architecture and model selection. Users suggested batching, using cheaper or local models, and optimizing API calls.

"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." — ok_orco "Today's local models are quite good. I started off with cpu and even that was fine for my pipelines." — LTL_FTC

2. Alternative Model Providers for Cost and Quality Users recommended various alternative LLM providers (other than the major two) to lower costs or improve reliability, specifically mentioning z.ai, minimax, and cheaper Chinese models.

"Consider using z.ai as model provider to further lower your costs." — gandalfar "Or minimax - m2.1 release didn't make a big splash in the news, but it's really capable." — viraptor "You also can try to use cheaper models like GLM, Deepseek, Qwen,at least partially." — DeathArrow

3. Implementation and Optimization Best Practices The discussion highlighted technical tweaks to improve the system, such as using prompt caching, specialized topic modeling libraries, and implementing sanity checks.

"Are you also adding the proper prompt cache control attributes? I think Anthropic API still doesn't do it automatically" — dezgeg "Have you looked into BERTopic?" — joshribakoff

🚀 Project Ideas

AutoModelCost Optimizer

Summary

  • A SaaS that automatically routes user prompts to the cheapest, most suitable LLM based on task type, with batch scheduling and prompt caching.
  • Reduces API spend, mitigates rate limits, and improves consistency by avoiding peak‑hour variability.

Details

Key Value
Target Audience Developers, data scientists, and small teams needing cost‑effective LLM inference.
Core Feature Intelligent model selector + nightly batch queue + prompt cache control.
Tech Stack Node.js + Express, Redis for queue & cache, OpenAI/Anthropic/Vertex AI SDKs, Docker, Terraform for infra.
Difficulty Medium
Monetization Revenue‑ready: $10/month per user tier, free tier with 10k tokens/month.

Notes

  • HN users lament “rate limit after 7 requests!” and “cost savings from batch API.” This tool directly addresses those pain points.
  • The “prompt cache control” issue raised by dezgeg is handled automatically, eliminating manual header tweaks.
  • Discussion potential: “Do they or any other providers offer any improvements on the often‑chronicled variability of quality/effort from the major two services?” → Our selector can switch to a more stable model during peak hours.

LocalLLM Manager

Summary

  • A desktop application that guides users through setting up CPU‑friendly LLMs (e.g., GPT‑OSS‑20B, Qwen‑3‑8B) and runs nightly batch inference.
  • Provides a GUI cheat sheet mapping tasks to models, memory allocation, and performance tuning.

Details

Key Value
Target Audience Hobbyists, researchers, and small teams with local hardware (CPU/GPU).
Core Feature Model selection wizard, memory‑aware inference runner, batch scheduler, and performance dashboard.
Tech Stack Electron, Python (FastAPI backend), Hugging Face Transformers, ONNX Runtime, Docker for isolated environments.
Difficulty High
Monetization Hobby (open source) with optional paid plugins for advanced profiling.

Notes

  • Users like LTL_FTC and queenkjuul “amazed at what I can do locally with an AMD 6700XT and 32GB of RAM.” This tool lowers the barrier to entry.
  • The “Model Selection Cheat Sheet” from 44za12 is integrated, so users don’t need to manually map tasks.
  • Practical utility: “I started off using gpt‑oss‑120b on cpu… 60‑65GB memory.” The manager can auto‑suggest the 20B variant if RAM is limited.

BatchInference‑as‑a‑Service

Summary

  • A cloud‑agnostic batch inference platform that abstracts Vertex AI, Anthropic, and other providers’ batch APIs, handling prompt caching, rate limits, and cost estimation.
  • Offers a unified UI for scheduling, monitoring, and optimizing batch jobs.

Details

Key Value
Target Audience Enterprises and teams that need large‑scale, cost‑controlled LLM inference.
Core Feature Unified batch API wrapper, prompt cache enforcement, cost dashboard, auto‑retry on throttling.
Tech Stack Go for backend, gRPC, Kubernetes, Prometheus, Grafana, Terraform, provider SDKs.
Difficulty Medium
Monetization Revenue‑ready: $0.02 per 1k tokens processed, with enterprise tier for custom SLAs.

Notes

  • “Consider using z.ai as model provider to further lower your costs.” This service can plug in any provider, including z.ai, to keep costs minimal.
  • The “batch API is half the price of real‑time calls” comment is directly leveraged; the platform automates batch usage.
  • Discussion hook: “Do you also add the proper prompt cache control attributes?” → The service enforces cache headers automatically, solving a common friction point.

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