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15 Free Tools

AI Tools & Calculators

Built for AI developers and product teams. 15 tools covering API costs, agent calculators, agentic loop estimation, memory costs, automation comparisons, and full stack cost building — instant results, no sign-up.

AI Token Utilization Calculator
Estimate token count and context window usage across Claude, GPT, and Gemini.
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AI API Cost Calculator
Compare API costs across all major AI providers for any usage volume.
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AI ROI Calculator
ROI for AI implementations — labor savings, productivity gains, and payback period.
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AI Subscription ROI Calculator
Is ChatGPT Plus, Claude Pro, or Copilot worth it? See exact ROI by hourly rate.
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RAG Pipeline Cost Calculator
Full cost breakdown for RAG systems — embeddings, vector DB, retrieval, LLM inference.
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LLM Margin Calculator
Gross margin, break-even price, and pricing sensitivity for AI-powered products.
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Agent Calculators

True cost of running AI agents at scale — per task, per loop, per month.

AI Agent Cost Calculator
Model, steps per run, runs per day → cost per task, day, month, and year with context accumulation math.
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Agent vs Human Cost Calculator
Compare annual cost of an AI agent vs a human employee for the same task — with ROI and breakeven timeline.
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Agentic Loop Cost Estimator
See how context accumulation makes multi-step agent costs grow non-linearly — with per-step breakdown and caching savings.
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Agent Memory Cost Calculator
Embedding + vector DB storage + query costs for persistent agent memory across Pinecone, Weaviate, and Qdrant.
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Multi-Agent Orchestration Cost
Orchestrator + specialist sub-agents on different models — per-tier cost breakdown, context accumulation, and model routing savings.
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Codex vs Claude Code Cost
Compare Codex, Claude Code, Gemini Code Assist, and Qwen 3.7 monthly cost by task type, usage volume, and team size.
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Webhook & Automation Cost Calculator
Compare Make.com vs Zapier vs n8n vs raw API for your operation volume — find the cheapest platform instantly.
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Stack Builder & Comparisons

Build your full AI product stack and compare automation platforms side by side.

AI Stack Cost Builder
Pick your LLM, embeddings, vector DB, hosting, and monitoring — see your total monthly cost with preset stack templates.
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n8n vs Make vs Zapier — Full Comparison
Feature matrix, pricing deep-dive, and interactive cost estimator for all three automation platforms. Updated May 2026.
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Why AI costs surprise people

Nearly every unexpectedly large AI bill traces back to the same misunderstanding: people price a feature using the input cost and forget that output tokens typically cost three to five times more, and that conversational applications resend the entire history on every single turn.

That second point is the expensive one. In a chat interface, turn 20 does not send one message — it sends all twenty. Token consumption grows roughly with the square of conversation length, which is why a prototype that costs pennies in testing can cost hundreds of dollars a day once real users hold long conversations with it.

The cost levers that actually move the bill

LeverTypical impactTrade-off
Prompt cachingUp to ~90% off repeated input tokensOnly helps with a stable prefix (system prompt, docs)
Batch processingAround 50% offResults arrive within hours, not seconds
Routing simple calls to a smaller model10–20× cheaper per tokenRequires classifying difficulty up front
Trimming conversation historyScales with how aggressively you trimThe model loses earlier context
Capping max output tokensDirectly bounds the expensive sideLong answers get truncated
Retrieval instead of long contextOften 5–10× cheaperAdds retrieval infrastructure to build and maintain

How to estimate before you build

A workable estimate needs four numbers, and most teams only guess at the third and fourth:

A rough rule for English text: one token is about four characters, or roughly 0.75 words. A 500-word document is therefore about 665 tokens. Use the token calculator for a real count rather than that approximation when the numbers matter, and the API cost calculator to turn those counts into a monthly figure.

Agents are a different cost model entirely

A single completion is one request. An agent that plans, calls tools, reads results, and iterates might make fifteen model calls to complete one user-visible task — and each of those calls carries the accumulated context of every previous step. Budgeting an agent as though it were a chatbot understates the cost by an order of magnitude, which is what the agent cost calculator and agentic loop estimator exist to correct.

Pricing changes

Model pricing moves frequently, and providers add and retire models regularly. We update these calculators when published rates change, but always confirm current pricing on the provider's own pricing page before committing to a budget or signing a contract.

Frequently asked questions

What is a token, in practical terms?

A token is the chunk of text a model actually processes — usually a word fragment rather than a whole word. In English, one token averages about four characters or 0.75 words, so 1,000 tokens is roughly 750 words. Code, punctuation-heavy text, and non-English languages tokenize less efficiently and consume more tokens per word.

Why is output more expensive than input?

Input tokens are processed in parallel in a single forward pass. Output tokens are generated one at a time, each requiring a full pass through the model that depends on every token before it. That sequential generation is far more computationally expensive, and pricing reflects it — output typically runs three to five times the input rate.

How much does prompt caching actually save?

It depends entirely on how much of your prompt is stable. If a long system prompt or document set is identical on every request, caching can cut that portion of input cost by up to about 90%. If each request is unique, caching saves nothing and may add a small write cost. It works best for document Q&A and agents with large fixed instructions.

Is a subscription or the API cheaper for my use case?

Subscriptions win for interactive human use with unpredictable volume; the API wins for programmatic, high-volume, or automated work. The crossover depends on your actual token throughput — the subscription ROI calculator compares both against your real numbers rather than a generic assumption.

Do these calculators use live pricing?

No — rates are hardcoded from providers' published pricing pages and updated when those change. This keeps the tools fast and fully client-side, but it means you should verify against the provider's current pricing before making a financial commitment.

Why did my costs jump without traffic increasing?

The usual causes are longer conversations (context grows with each turn), a change in system prompt length that applies to every request, retrieval returning more documents than before, or a model swap to a higher-priced tier. Check average input tokens per request over time — that metric usually reveals it immediately.

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