TokenOps · Professional Hub

Calculators, glossary, and the operating manual for LLM token spend

Six interactive tools to size the prize, compare models, score your maturity, and learn the vocabulary — all backed by the same playbook used across the rest of the TokenOps Atlas.

Optimization savings calculator

Provider preset

Requests per month3.0M

Avg input tokens / request2,000

Avg output tokens / request500

Cache hit rate (%)40%

Batch processing share (%)25%

Prompt compression (%)30%

Model tiering share (%)50%

Output constraint savings (%)25%

Baseline monthly

$135K

Optimized monthly

$47K

Monthly savings

$88K

Savings rate

65.3%

Payback (months)

2.0

Annual savings

$1.1M

Savings breakdown by lever

Semantic caching

61%

Model tiering

14%

Batch API routing

11%

Output constraints

Prompt compression

Lever	Monthly saving	% of total	Complexity
Semantic caching	$54K	61%	High ROI
Model tiering	$12K	14%	Lower ROI
Batch API routing	$10K	11%	Lower ROI
Output constraints	$7K	8%	Lower ROI
Prompt compression	$5K	6%	Lower ROI

Summary: At 3.0M monthly requests on OpenAI (Astra + Luna), baseline is $135K/month. Applying all levers reduces this to $47K — a 65% reduction saving $1.1M annually. Biggest lever: Semantic caching.