Free Tools/Cost Estimator

AI Agent Cost Estimator

Calculate the real monthly cost of running an AI agent. 18 models, tool calls, embeddings, and per-conversation breakdowns — all instant, all free.

Model

Context: 128K tokensSpeed: fast

Usage

Conversations per day50

11,000

Avg input tokens/conversation500

504,000

Avg output tokens/conversation300

504,000

Tool Calls

Tool calls per conversation2

020

Tokens per tool call (input+output)200

502,000

Embeddings / RAG

Documents indexed per day100

Avg tokens per document500

Estimated Monthly Cost

$10.90

$0.0073 per conversation

$0.363

Daily

$10.90

Monthly

$132.68

Annual

Cost Breakdown (daily)

LLM API (GPT-4o)$0.362

Embeddings (text-embedding-3-small)$0.0010

Daily Token Usage

Input tokens

45,000

Output tokens

25,000

Estimates based on published API pricing as of April 2026. Actual costs vary with caching, batching, prompt optimization, and volume discounts. Does not include infrastructure hosting costs.

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Frequently asked questions

How accurate are these cost estimates?+

The estimates are based on published API pricing as of April 2026. Actual costs may vary due to prompt caching (can reduce costs 50-90%), batching discounts, token optimization, and volume pricing tiers offered by some providers.

What are tool calls and why do they add cost?+

Tool calls happen when an AI agent uses external tools (search, database queries, API calls, code execution). Each tool call adds tokens to the conversation — the tool definition, the call request, and the response all consume tokens and increase costs.

When should I use embeddings / RAG?+

RAG (Retrieval-Augmented Generation) is needed when your agent needs to reference large document collections, knowledge bases, or frequently updated data. The embedding cost is for converting documents into searchable vectors. If your agent only needs the information in its prompt, you can skip embeddings.

How can I reduce AI agent costs?+

Key strategies: use prompt caching (Anthropic, OpenAI both support it), batch requests where possible, use smaller models for simple tasks (routing, classification), minimize system prompt size, implement token-aware context management, and use streaming to reduce time-to-first-token perception.

Which model should I choose?+

For customer support: GPT-4o mini or Claude Haiku for speed and cost. For complex reasoning: Claude Sonnet, GPT-4o, or Gemini 2.5 Pro. For budget: Gemini Flash, GPT-4.1 nano, or DeepSeek models. For long context: Gemini 2.5 Pro or GPT-4.1 (1M+ tokens).