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Products that do what TokenAtlas does

Real-time control of AI infrastructure costs

  1. 1TP

    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

  2. 2

    Count tokens and estimate costs for any AI model

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    Discover, optimize & regulate your digital infrastructure

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    See your LLM token bill before you hit send.

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    Edgee196

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    AI unit economics platform for AI companies

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    Self-hosted AI proxy. Your data never leaves your network.

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  18. 18IN

    Tl;dr: I trained a classifier to route to the least expensive model and reasoning depth to complete the request. Coupling that with additional automated token efficiency techniques has yielded 3x usage for the same spend. For anyone interested in trying it themselves: https://nerfguard.com Various teammates and I switched over to Codex from Claude Code recently. We still bounce between the tools, but Codex’s speed and steerability coupled with performance gains were hard to ignore. One of the downsides was that the per token pricing kicked in way sooner. This is happening across…

    Jun 2026

  19. 19

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    Enterprise AIOS Gateway & Semantic Cache to Stop Token Bleed

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  21. 21

    Cut your AI costs by 63% — before you hit send

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  22. 22LC

    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

  23. 23

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  24. 24

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