30% Less AI Costs: A Token Savings Checklist
You pay for tokens (like characters in SMS). Here’s a checklist to cut your bills by ~30% in one day: shorter prompts, response limits, batching, smaller models, thread shortening, fewer images/videos, JSON, RAG, and measuring 'successful task cost'. Simple and no system overhaul
Key takeaways
- Tokens are like characters in SMS; you pay for both input and output.
- First, shorten prompts and set response limits for quick savings.
- Use a cheaper, smaller model for simple tasks; reserve the large one for complex ones.
- Implement batching, shorten threads, limit images/videos, and enforce a concise format (like JSON).
- Measure 'successful task cost' instead of just the cost per 1k tokens.
With AI, you pay by the word—similar to how you pay for characters in SMS. As automation and agents (programs that work in the background) increase, bills can skyrocket. The good news is that you can cut costs by about 30% and speed up responses in just one day. Here’s a simple checklist that requires no tool overhauls.
Tokens Explained: What Are You Really Paying For?
A token is a small piece of text—think of it like a character in an SMS. The model counts tokens both ways: for what you send it (input) and what it replies with (output). The more characters, the higher the bill.
Costs can quietly rise as agents (programs that perform tasks automatically in the background) work. Repeated long prompts and endless threads can add hundreds of thousands of tokens each month.
Conclusion: Before optimizing anything, identify three processes that generate the most queries or the longest responses. That’s where you’ll save the most quickly.
One-Day Checklist: 30% Fewer Tokens Without Overhauling
Choose 5–7 items from the list and implement them today. Focus on making short changes to the content of prompts (the text you use to ask AI for results) and settings for response length. The result is usually fewer tokens, faster responses, and fewer revisions.
- Shorten prompts to the essentials: state your goal, context in 1–2 sentences, and expected format. Remove pleasantries and digressions. Simple rule: 50–100 words instead of 300.
- Set a response limit: define the maximum length. Example: 'Be concise, up to 120 words' or 'max 10 points'. Shorter responses = fewer tokens and faster delivery.
- Enforce a concise format: ask for a list or JSON (a simple key:value format, like a mini-table). Less fluff, easier to paste into a spreadsheet.
- Choose a smaller model for simple tasks: classification, extraction, deduplication—use a cheaper/'mini' model. Reserve the large model for complex, creative tasks.
- Batching: instead of sending 100 separate requests, send one batch of similar records with one common instruction. Less repetition = fewer tokens overall.
- Shorten threads: every few steps, ask the model for a brief summary and replace the earlier history with it. You pay for the summary, saving on each subsequent response in the thread. Limit images and videos: visual
Done. This checklist reduces token usage and usually shortens response times without changing providers. If you’d like, we can go through it together using your processes and numbers—schedule a short, free consultation. One hour often suffices to identify 2–3 major 'leaks' in your AI budget.
Frequently asked questions
Will shortening prompts hurt quality?
If you clearly state the goal, context, and format of the response, quality usually improves. The model talks less and gets to the point faster. If the task is very complex, consider a larger model—but first, test a shorter version on a sample.
What is batching and when does it make sense?
Batching is combining multiple similar tasks into one package, allowing you to send one instruction and headers at once. It makes sense for classifications, extractions, and ordered data. Ensure the batch size doesn’t exceed what the model can reasonably handle.
Do I need to know JSON to enforce a short format?
No. Just ask for a list of fields, e.g., 'category: …; reason: …; status: …'. JSON is simply a standard key:value format. A rigid format limits length and makes it easier to paste results into a spreadsheet.
When should I use a large model?
A large model is useful for creating longer content, summarizing multiple sources, planning, and tasks requiring 'reasoning'. For simple tasks, use a smaller model—you’ll pay less and get results faster.