Prompt template cost optimizer
A prompt that is twice as long costs twice as much on input — and at scale that difference compounds into real money. This tool lets you paste several versions of the same template and compare them head to head on token count, cost per call, projected daily spend, and a rough completeness score, so you can trim verbosity without dropping the instructions that actually matter.
How it works
Each variant’s character length is converted to an estimated token count (about four characters per token for English) and priced at the selected model’s input rate. Your expected completion tokens are priced at the output rate and added, giving a realistic cost per call. Multiplying by your daily call volume projects daily spend. Separately, a completeness heuristic scans for instruction cues — a defined role, explicit constraints, output-format directions, and examples — and scores each variant so you can spot a cheaper prompt that is also under-specified.
When template cost optimisation matters most
For low-volume use — a few hundred calls per day — the difference between a 200-token and 400-token template is negligible. Template cost optimisation becomes meaningful when the product scales to tens or hundreds of thousands of daily calls, or when the template itself is unusually long (system prompts with extensive background context, multi-shot examples embedded directly in the prompt, detailed policy documents).
A concrete way to think about the threshold: if the daily cost of your prompt template is less than the cost of one hour of an engineer’s time, optimising the template length is probably not the best use of your time. The tool is most valuable when you can see a clear dollar figure in the daily spend projection that justifies the work of creating and testing alternative variants.
The most common sources of prompt bloat
When reviewing long templates for trimming, the same categories of bloat appear repeatedly:
Redundant restatement — instructions that repeat the same constraint in multiple ways (“be concise,” “keep your response brief,” “do not write more than necessary”). The model only needs to be told once; repetition adds tokens without improving compliance.
Excessive hedging — qualifications added defensively that the model already handles correctly by default (“try to,” “where possible,” “if you can”). These phrases add length without changing behavior.
Narrative preambles — long explanations of why the task exists, the company background, the user context, given in paragraph form when a structured header would be shorter and more readable.
Embedded raw data — reference tables, policy documents, or FAQ text pasted directly into the system prompt when they could be retrieved dynamically (RAG) or summarised more tightly.
Multi-shot examples that are too long — examples that use full realistic-length outputs where shorter representative outputs would demonstrate the same format.
Reading the completeness score alongside cost
The completeness score is a guardrail against over-trimming. A variant can save tokens by cutting an output format instruction, a role definition, or an example — and the cost number will look better — but the quality of outputs at scale will likely deteriorate, leading to increased retries, manual correction, and user dissatisfaction.
When comparing variants, the optimal choice is usually not the cheapest variant but the cheapest variant with a completeness score at or above the original. If a significantly cheaper variant has a notably lower completeness score, read the diff carefully to see what was removed before trusting it.
Tips and notes
- Cheapest is not always best. Use the completeness score as a guardrail: a variant that saves tokens but loses a format instruction may cost you in retries and bad outputs.
- Trim the static boilerplate. Long fixed preambles repeat on every call — the biggest savings usually come from compressing them, not the variables.
- Measure output length too. If completions are long, switching to a cheaper generation model often beats shaving the prompt.
- Estimates, not exact counts. For precise billing, confirm with your provider’s tokenizer; the heuristic here is for fast comparison.