128 ways to spend fewer tokens
The 22 Beginner tips are free to read. The 106 advanced tactics unlock with Pro — plus a fresh tip in your inbox every morning.
Move Every Non-Urgent Job to the Batch API and Pay Half Price
If a job doesn't need an answer in the next few seconds, send it through the Batch API instead of the live endpoint. The exact same request costs half as much.
Classify a Whole List in One Call, Not One Row at a Time
Send 20-50 items as a numbered list and get back a JSON array of labels, instead of paying for the same instruction prompt on every single row.
Deduplicate and Cache Identical Requests Before They Ever Hit the API
Real-world batches are full of repeats. Hash each request, send each unique prompt once, and fan the answer back out to every duplicate.
Fetch the Data the Agent Always Needs Before the Loop Starts
When an agent will always need the same data, making it fetch that data through a tool call costs tokens for the decision, the tool schema, and the round-trip. Gather it with a plain script before the agent starts and hand it the file.
Add a Relevance Gate: The Cheapest LLM Call Is the One You Don't Make
A cheaper model and a tighter prompt still pay for a call that never needed to happen. Put a deterministic, non-LLM precheck in front of the agent and skip the whole invocation when the input plainly doesn't need reasoning.
Use Targeted Retries with Backoff Instead of Blindly Re-Sending
Distinguish retryable errors from real failures, back off on rate limits, and resend only the failed items so you stop paying for accidental duplicate generations.
Stack Prompt Caching on Top of Your Batch Jobs for Compounding Savings
Batch requests support prompt caching. When every request shares a big instruction block or document, cache it once and the per-request cost collapses.
Run an Async Queue with a Concurrency Cap Instead of Firing All at Once
Push jobs through a bounded worker pool so you saturate your rate limit without tripping it, eliminating the retry storms and tier upgrades that quietly inflate cost.
Compile a Repeated Skill's Stable Steps Into Code, Keep the Model Only for Judgment
Take a working natural-language agent skill, collect its execution traces, and permanently move the steps that have stabilized into a fixed procedure (search, filter, dedupe, format) into plain code, reserving model calls only for the genuinely semantic steps.
Collapse Many Tiny Calls into One Structured Request
Ten one-item calls re-send your instructions ten times. Batch the items into a single request with a structured-output schema and pay the overhead once.
Record a Successful Agent Run Once, Then Replay the Tool Trace With Zero Inference
A scheduled agent re-plans the same fixed workflow every run, paying the model to re-decide a tool sequence it already decided. Record the trace of one successful run, template the params that vary, and replay the steps deterministically without calling the LLM.
Run the open-ended search phase last, over a frozen list of claims
Putting the unbounded research step first lets agents fan out over open questions and mint work faster than the pipeline can retire it — one team burned a full monthly allowance in 30 minutes and shipped nothing. Move it last, after a bounded step has produced a finite claim list, so your agent count is known before you spend rather than discovered when the budget dies.
Make "Nothing To Do" Cost Zero Tokens: Terminal Dispositions and Idempotent Wake IDs
A cron-woken agent that loads its whole context before discovering there is no work pays full price to conclude nothing happened — and if the run never records a terminal outcome, the scheduler wakes it again. Answer both questions deterministically, before any model call.
Wake the Agent on a File Cursor, Not on a Heartbeat
Anything that isn't a directly pushed message — cron output, log lines, inbox changes, external feeds — is only noticed at heartbeat granularity, and every heartbeat burns a full LLM turn even when nothing happened. Hold byte-offset cursors on the plaintext files those sources already write, wake the agent only on an actual append, and coalesce bursts into one turn. A polling loop's cost scales with wall-clock time; an event loop's cost scales with real events.
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