AI · alternatives · 2026
24 alternatives to GitHub
Cut LLM token costs 30–60% — local, instant, zero API calls
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites.
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Paritok▲258Spend up to 85% less and run 3× longer coding agent sessions
Aug 2026 · paritok.com · its alternatives →
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Cut LLM token costs 40-70% with offline prompt compression
Jul 2026 · llmslim.app · its alternatives →
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Hey HN! Tokencost is a utility library for estimating LLM costs. There are hundreds of different models now, and they all have their own pricing schemes. It’s difficult to keep up with the pricing changes, and it’s even more difficult to estimate how much your prompts and completions will cost until you see the bill. Tokencost works by counting the number of tokens in prompt and completion messages and multiplying that number by the corresponding model cost. Under the hood, it’s really just a simple cost dictionary and some utility functions for getting the prices right. It also accounts for…
2024 · github.com · its alternatives →
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Tokenwise▲143A smart LLM proxy that shows where you're overpaying
Jun 2026 · tokenwisehq.com · its alternatives →
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Hi HN, I was once given the advice: Don't waste expensive frontier model credits (GPT/Claude/etc.) on bulk work. Send the boring, repetitive, high-volume jobs to a smaller model, and save the expensive prompts for when you actually need frontier-level reasoning. I complained and told my manager that I shouldnt have to think about using certain models for certain coding tasks, and that one model should handle everything. Well, here we are anyway. If anyone needs a place to absolutely abuse an LLM with high-volume tasks, come beat ours up at https://yolo-auto.com. Here are…
Jul 2026 · yolo-auto.com · its alternatives →
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LLM Pricing▲178LLMs price comparison tool developed and updated by LLM
2024 · ailesson.io · its alternatives →
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Hi HN! Token cost has started to become a high topic of concern to all of us. I tried a few (awesome) tools such as rtk, caveman, and the recent (hillarious but effective) ponytail. What they usually do, is in-line token reduction, e.g. try to compress requests / responses as much as possible. But then it hit me (and I’m sure others had similar ideas) - just like we have routers that pick the right model, why not have something that will also narrow down the amount of available tools, skills and mcps based on repo/context? People usually accumulate skills, agents, MCP servers,…
Jun 2026 · github.com · its alternatives →
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Your AI agent is burning 6x more tokens than it needs to just to browse the web. We built OpenBrowser MCP to fix that. Most browser MCPs give the LLM dozens of tools: click, scroll, type, extract, navigate. Each call dumps the entire page accessibility tree into the context window. One Wikipedia page? 124K+ tokens. Every. Single. Call. OpenBrowser works differently. It exposes one tool. Your agent writes Python code, and OpenBrowser executes it in a persistent runtime with full browser access. The agent controls what comes back. No bloated page dumps. No wasted tokens. Just the data your…
Apr 2026 · openbrowser.me · its alternatives →
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Hi HN, I'm building Librarian (https://uselibrarian.dev/), an open-source (MIT) context management tool that stops AI agents from burning tokens by blindly re-reading their entire conversation history on every turn. The Problem: If you're building agentic loops in frameworks like LangGraph or OpenClaw, you hit two walls fast: Financial Cost: Token usage scales quadratically over long conversations. Passing the whole history every time gets incredibly expensive. Context Rot: As the context window fills up, the LLM suffers from the "Lost in the Middle" effect. Response latency…
Feb 2026 · uselibrarian.dev · its alternatives →
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I code on Claude's pay as you go API and started budgeting my token usage with my own CLI wrapper. Basically I set a budget for a task that I'm working on in my project: Task: "Fix mobile responsiveness" Budget: $3.00 and the budget updates after each prompt: Task: "Fix mobile responsiveness" Budget: $2.34 / $3.00 78% It helps keep an eye on my spending when working on projects.
May 2026 · github.com · its alternatives →
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Compress markdown for LLMs. Cut token usage drastically.
Feb 2026 · compress.new · its alternatives →
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Free real-time, low latency lossless LLM prompt compression
2025 · promptfold.web.app · its alternatives →
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Cuts your LLM API costs by 40-70%. One line of code.
May 2026 · semanticguard.dev · its alternatives →
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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…
2024 · edgematic.dev · its alternatives →
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Cut LLM costs. Free audit, pay only if it works.
Jun 2026 · decomp-ai.vercel.app · its alternatives →
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One API. Lowest token prices.
Jul 2026 · videorouter.sh · its alternatives →
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Cut AI token costs 30-60% with smarter JSON encoding
Nov 2025 · toonifyit.com · its alternatives →
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Hi HN, While building RAG agents, I noticed a lot of token budget was wasted on formatting overhead (HTML tags, JSON structure, whitespace). Existing solutions felt too heavy (often requiring torch/transformers), so I wrote this lightweight, zero-dependency library to solve it. It includes strategies for context packing, PII redaction, and tool output compression. Benchmarks show it can save ~15% of tokens with negligible latency overhead (<0.5ms). Happy to answer any questions!
Dec 2025 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →