AI & Automation

AI Token & API Cost Calculator

Calculate precise token usage and API costs for GPT-4o, Claude Opus, Gemini, and DeepSeek. Compare billing across models in real time with a live cost breakdown.

Context Window Estimator

Estimate your prompt payload size by segment — system prompt, chat history, RAG chunks, and output reservation. Detect context overflow before it hits production.

Embedding Cost Calculator

Calculate the cost of embedding your documents for RAG or semantic search. Compare rates across OpenAI, Google, Cohere, and DeepSeek embedding models.

Fine-Tuning Cost Calculator

Calculate the training cost of fine-tuning an LLM on your own dataset. Compare rates across fine-tunable model tiers by example count, length, and epochs.

LLMs.txt Generator

Generate a free llms.txt file for any website instantly. Enter your URL, we crawl your site and output a ready-to-download llms.txt in seconds. No signup.

Vector Database Cost Calculator

Estimate the monthly cost of storing and querying your vectors across Pinecone, Weaviate, and Qdrant. Compare storage and query costs by vector count and dimension.

GPT vs. Claude vs. Gemini: API Pricing Compared

Compare GPT-5.2, Claude 4.6, and Gemini 3.1 API pricing side by side — input cost, output cost, and which workloads each is cheapest for.

Pinecone vs. Weaviate vs. Qdrant: Vector Database Pricing Compared

Compare Pinecone, Weaviate, and Qdrant storage and query pricing side by side, at the same vector count and dimension, to see which is cheapest for your workload.

AI API Token Costs Explained: How GPT, Claude & Gemini Pricing Actually Works

A practical breakdown of how LLM API pricing works, what actually drives your bill up, and how to estimate and cut costs across GPT, Claude, Gemini, and DeepSeek.

LLM Context Windows Explained: How Much Can You Actually Fit in a Prompt?

What a context window really measures, how system prompts, chat history, and RAG chunks eat into it, and how to avoid silent truncation and overflow errors.

Best AI Tools for SaaS Startups in 2025: A Practical Guide

From LLM cost tracking and context window planning to AI coding assistants and customer support automation — the AI tools that actually move the needle for resource-constrained SaaS startups.

How to Reduce Your AI API Costs

Six concrete ways to cut your AI API bill without sacrificing output quality — model selection, prompt caching, and output budgeting, grounded in real per-token pricing.

llms.txt vs. robots.txt: What's the Difference?

robots.txt blocks crawlers; llms.txt describes your site for AI systems. Here's exactly how the two files differ, and why you likely need both.

Token

A token is the basic unit of text an LLM processes — roughly ¾ of a word for common English, though punctuation, numbers, and rare or technical words can each become their own token. Every API request is priced and limited by token count, not word or character count.

Context Window

The context window is the maximum number of tokens a model can process in a single request — the system prompt, conversation history, retrieved documents, the current message, and the model's own output all draw from this same shared budget. Content beyond the limit is truncated or the request is rejected.

System Prompt

A system prompt is a set of instructions sent with every request that defines how a model should behave — its role, tone, constraints, and output format — separate from the user's actual message. It counts against the same token budget as everything else in the request, so a longer system prompt leaves less room for conversation and output.

llms.txt

llms.txt is a plain markdown file placed at a website's root that gives AI systems a structured summary of the site's pages, similar to how a sitemap helps search engines. It was proposed by Jeremy Howard of Answer.AI as a lightweight convention AI crawlers can read instead of parsing full HTML.

Embedding

An embedding is a numerical vector representation of a piece of text, produced by an embedding model, that captures its meaning in a form that can be compared mathematically. Texts with similar meaning produce vectors that are close together, which is what makes semantic search and RAG retrieval possible.

Fine-Tuning

Fine-tuning is the process of further training an existing model on your own dataset of examples so it internalizes a task, format, or style without needing those instructions repeated in every prompt. It's billed separately from regular API usage, as a one-time training cost per token processed.

Vector Database

A vector database is a database purpose-built for storing embeddings and searching them by similarity rather than exact match — given a query vector, it returns the stored vectors closest to it in meaning. This is what makes semantic search and RAG retrieval fast at scale, where comparing a query against millions of vectors one by one would be too slow.