AI cost guides

A plain-English guide to how LLM API pricing works — input vs output tokens, caching, batch discounts and how to estimate your real bill.

AI model prices and new models change constantly. Here's why, what it means for your budget, and how to keep your cost estimates current.

Tokens — not words — decide your AI bill. Here is how tokenization works and how to count tokens accurately for prose, code and other languages.

Output tokens usually cost several times more than input tokens. Learn which side dominates your bill and how to tell if you are input- or output-bound.

A large context window only helps if you can afford to fill it. Learn what context windows are, what fits, and what filling one actually costs per call.

Reasoning models generate hidden thinking tokens billed as output. Learn why they make per-task cost much higher than the visible answer suggests.

Comparing models on headline price is misleading. Tokens per dollar shows how much work each model actually buys you — here is how to use it.

Everything that drives the cost of an AI chatbot — message length, conversation history, model choice — and how to estimate and reduce your monthly bill.

Turn active users, messages and model choice into a clear per-user chatbot cost — the number you actually need to price a plan or set a budget.

Practical ways to cut an AI chatbot's bill — history windowing, caching, routing and answer limits — with the calculators to size each saving.

A flat ChatGPT subscription or pay-as-you-go API? Compare the break-even point based on how much you actually use it.

A long system prompt is billed on every single request. Learn how much that adds up to and how caching removes most of it.

A guide to what multi-step AI agents cost — step counts, growing context, tool calls — and how to keep agent workflows affordable.

Function and tool calling adds tokens you may not expect — schemas, arguments and follow-up turns. Learn where the cost hides and how to size it.

Multiple cooperating agents multiply token cost through message passing and duplicated context. Learn the drivers and how to keep a crew affordable.

As an agent remembers more, it sends more context each step. Learn how memory strategy drives agent cost and how to bound it.

Model Context Protocol servers expose tools and resources to your agent — and each adds tokens. Learn how to estimate the cost of an MCP setup.

How retrieval-augmented generation is priced — one-time embedding, ongoing vector storage and per-query generation — and how to keep RAG affordable.

Embedding a corpus is mostly a one-time cost, but re-embedding and dimensions change the math. Learn how to estimate embedding spend accurately.

Vector storage scales with vector count and dimensions, plus query and index overhead. Learn what drives vector DB cost and how to keep it lean.

Stuff everything into a big context window, or retrieve just what you need? Compare the cost of RAG vs long context for your query pattern.

Chunk size quietly controls both retrieval quality and per-query cost. Learn how to pick a size that keeps answers good and tokens low.

Semantic caching serves repeated or similar questions from a cache instead of the model. Learn how much it can save on high-traffic AI apps.

If you send the same context repeatedly, prompt caching bills it at a fraction of the input rate. Learn when it applies and how much it saves.

Batch APIs typically halve token prices in exchange for delayed results. Learn which workloads qualify and how much you can save.

Prompt compression trims redundant tokens before they hit the model. Learn the techniques and how much input cost they realistically remove.

A newer or smaller model may do the same job for far less. Learn how to quantify the savings of switching before you commit.

A prioritized checklist for cutting AI API costs — from caching and batching to routing and answer limits — with calculators to size each move.

Fine-tuning has an upfront cost but can cut per-request tokens. Learn the break-even against long prompts and few-shot examples.

OpenAI GPT and Anthropic Claude price input and output differently. Learn which comes out cheaper depending on your prompt and output mix.

Google Gemini and OpenAI GPT differ on price and context. Compare their cost on your token volume and see which fits your budget.

The cheapest LLM API changes as prices move and models launch. Learn how to find today's cheapest option for your specific workload.

Estimate what the OpenAI API will cost for your usage — by model, tokens and volume — including caching and batch discounts.

Estimate the cost of Anthropic's Claude models for your workload, including prompt caching on repeated context.

DeepSeek is known for aggressive pricing and off-peak discounts. Learn what it really costs and how it compares to GPT on your workload.

Price is only one axis. Compare models on context window, vision, reasoning and tool use so you pick the cheapest one that can actually do the job.

How to price an AI SaaS feature so it covers variable AI costs and protects your margin — flat vs usage-based, free tiers and heavy users.

Set a price for an AI feature that covers its variable cost and hits your margin target — with the math for markup and heavy users.

Compute the gross margin on an AI-powered request or plan so you know whether each sale is profitable after model costs.

Selling AI by the seat is simple until heavy seats blow your margin. Learn how to set a seat price that survives real usage.

If you wrap a provider's API and resell it, your markup has to cover cost, overhead and risk. Learn how to set it so you stay profitable.

Image generation is priced per image by size and quality, not by tokens. Learn how to estimate cost and compare models for your volume.

AI video is typically billed per second of output by resolution. Learn how quickly it adds up and how to estimate a project budget.

TTS is usually billed per character or per million characters of input. Learn how to estimate audio generation cost and compare voices.

Speech-to-text is billed per minute of audio. Learn how to estimate transcription cost for meetings, calls and media libraries.

A voice agent stacks three costs — transcription, the language model and speech synthesis. Learn how to estimate the full per-minute cost.

Renting GPUs to self-host a model has high fixed cost but flat marginal cost. Learn the break-even against pay-per-token APIs.

VRAM decides which models you can run and on what hardware. Learn how to size VRAM for a model and how quantization cuts the requirement.

Throughput and latency decide how much hardware or rate limit you need. Learn to plan capacity for your concurrency and traffic.

Training cost comes down to GPU hours: hardware rate times how long the run takes across how many GPUs. Learn to estimate a training budget.

Justify AI spend by comparing its cost against the time or money it saves. Learn a simple ROI framework with the calculators to back it up.

An AI support bot earns its keep by deflecting tickets from human agents. Learn how to compute the savings against its running cost.