AI cost optimization 4

AI API router optimizing spend by dynamically routing prompts based on difficulty OrcaRouter is an AI API gateway and multi-model LLM router that dynamically routes user prompts to the most cost-effective and appropriate model across OpenAI, Anthropic, Gemini, and over 200 other models. By instantly grading every prompt for difficulty, OrcaRouter directs complex reasoning tasks to frontier models while routing routine requests to cheaper, open-source alternatives. This process allows developers to slash their AI spending by approximately 40% while preserving high-quality results. It features an OpenAI-compatible API, meaning integration requires changing just a single line of code to swap the base URL and API key. OrcaRouter provides transparent 'glass-box' routing by logging the difficulty grade, model selection, and provider for each request with zero token markup, passing the original provider costs directly to the user.

AI token calculator and cost estimator for LLMs. Tokenomy is an advanced AI token calculator and cost estimator for Large Language Models (LLMs) such as GPT-4o and Claude. It predicts token usage and dollar costs before API calls, providing estimates and cost-saving tips through a VS Code sidebar, CLI, and LangChain callback. This helps development teams ship confidently without surprise bills by optimizing AI prompts, analyzing token usage, and saving money on various LLM APIs, including OpenAI and Anthropic.

AI API load balancer for optimal performance and cost savings. MakeHub is a universal API load balancer designed to dynamically route AI model requests (such as GPT-4, Claude, and Llama) to the best providers (including OpenAI, Anthropic, and Together.ai) in real-time. It offers an OpenAI-compatible endpoint, a single unified API for both closed and open LLMs, and runs continuous background benchmarks for price, latency, and load. This system ensures optimal performance, significant cost savings, smart arbitrage, instant failovers, and live performance tracking for AI agents and applications.

AI token optimization stack for reducing agent costs and context Caveman is an AI efficiency operating stack that reduces the cost and context size of agent-native development. It provides an MIT-licensed skill, a free local proxy, an Agent SDK, and planned cloud and enterprise services. Caveman compresses logs, JSON, code, diffs, tables, tool outputs, and files before provider calls while preserving recoverable original bytes. It also supports caching, eval-gated model routing, token usage visibility, savings estimates, and verification workflows. The local wrap works with users' own provider keys and does not require a Caveman account.