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
Usage
Tool Calls
Embeddings / RAG
Cost Breakdown (daily)
Daily Token Usage
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.
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).