Pearlite
Stop paying for the same LLM call twice.
What it does
Every LLM API call costs money even when you've answered the exact same question 1,000 times before. Pearlite sits between your app and any LLM, intercepts semantically similar queries, and returns cached responses in <12ms instead of burning another API call. Not exact-match caching, semantic caching. "What's the price?" and "how much does it cost?" are the same question. Drop in 2 lines of code, keep your existing provider, and cut LLM spend by up to 60%.
Does a similar job
all alternatives →- WWWhy write code if the LLM can just do the thing? (web app experiment)Nov 2025 · github.com · ▲436
I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
- PPPrice Per Token – LLM API Pricing Data2025 · pricepertoken.com · ▲339
The LLM providers are constantly adding new models and updating their API prices. Anyone building AI applications knows that these prices are very important to their bottom line. The only place I am aware of is going to these provider's individual website pages to check the price per token. To solve this inconvenience I spent a few hours making pricepertoken.com which has the latest model's up-to-date prices all in one place. Thinking about adding image models too especially since you have multiple options (fal, replicate) to use the same model and the prices are not always the same.
- OSOpen-source LLM provider price comparison2024 · github.com · ▲125
Looking for the cheapest place to deploy llama 3.1 model? Don't worry we have found it so you don't have to.
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Hello HN! We're building a caching solution for LLMs (ChatGPT, Claude). By combining cutting-edge approaches, such as edge computing, prompt compression, vectorization, and others - it can reduce your AI bills by up to 10x and significantly lower response times. Key Features: - cost efficiency: our system stores frequent queries, reducing the number of upstream (paid) API calls - fast responses: with various nodes globally, we reduce latency by serving data from the nearest location - scalability: designed to handle increasing loads and data sizes without degrading performance. The cache…
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