Estimate LLM API spend from traffic, token volume, caching, embeddings, retries, and enterprise operating assumptions.
TechMamba AI Cost Calculator estimates monthly AI spend using input tokens, cached input tokens, output tokens, model pricing, batch processing, embeddings, RAG storage, retry overhead, and production operating buffers.
The calculator runs locally in your browser. Pricing presets are editable because enterprise contracts, reserved capacity, volume discounts, and regional billing can change the final number.
Most LLM bills are based on input tokens, cached input tokens, output tokens, model rates, embedding tokens, tool usage, retry traffic, and production overhead such as observability, infrastructure, support, and contingency.
An AI cost calculator helps estimate the monthly and annual cost of running AI features such as chatbots, copilots, SaaS assistants, support agents, RAG search, document analysis, and workflow automation before they are deployed.
Input tokens are the text, instructions, chat history, and retrieved context sent to the model. Cached input tokens are repeated input segments billed at a discounted rate when provider caching applies. Output tokens are the model response tokens, including generated text and sometimes reasoning or tool-related output depending on the provider.
Prompt caching can reduce cost when the same instructions, system prompt, long context, or retrieved content is reused across requests. Cached tokens are usually cheaper than uncached input tokens, so higher cache hit rates can lower the monthly bill.
Use simple mode for quick planning when you only need traffic, token, model, cache, and retry assumptions. Use enterprise mode when budgeting a production system that needs observability, support, maintenance, compliance review, contingency, and operational buffers.
Production AI systems usually need logging, monitoring, fallback handling, staged environments, human review, support, compliance work, and a budget buffer. Enterprise mode includes those operating costs so the estimate is closer to a real implementation budget.
Enterprise AI budgets often include custom provider rates, volume discounts, compliance requirements, observability, fallback logic, dedicated support, staging traffic, human review, and contingency buffers.
AI model pricing changes frequently and enterprise contracts may include private rates, regional differences, reserved capacity, batch discounts, or volume commitments. Admin-managed pricing keeps the calculator current without code changes.
Yes. Retrieval-augmented generation can add embedding costs, vector database or file-search storage, retrieval operations, and more input tokens because retrieved context is sent to the model with each request.
Some providers discount batch or asynchronous requests because they do not need immediate response latency. Batch processing can reduce cost for jobs such as offline classification, enrichment, summaries, evaluations, and report generation.
No. It calculates locally in your browser from editable pricing presets and the assumptions you enter.
Yes. The calculator includes editable pricing fields and the website admin can manage the default pricing catalog. This is useful for negotiated contracts, private model deployments, and internal chargeback models.
The estimate is as accurate as the usage assumptions and prices entered. For production budgeting, validate the token assumptions against logs and use contract-specific prices when available.
Use real application logs for request count, input tokens, output tokens, cache hit rate, retry rate, and embedding usage. If you have provider contract pricing, enter those rates in the advanced pricing fields.