Semantic Caching
Semantic Caching stores similar LLM prompt responses in a vector database to serve future requests locally, eliminating redundant API costs.
“Serving a redundant LLM prompt from an API is an unforced error in unit economics. Semantic Caching reclaims gross margins by treating prompt similarity as a cache hit.”
Engineers who route every prompt to commercial APIs subject their organization to the AI Volatility Tax. Semantic Caching intercepts redundant queries, restoring software gross margins to historic norms by serving results from local infrastructure.
Semantic Cache & Edge Filter Execution Loop
Reverse Citations: Implemented & Audited Across Platform
Richard Ewing’s Research Thesis
We cannot build profitable SaaS platforms when every user interaction incurs a variable API toll. Architecture must aggressively cache inference state based on semantic intent.
Why This Specification Exists
AI application gross margins degrade because every user interaction triggers an expensive API call to OpenAI or Anthropic.
Relying on exact string matching for caching, which fails on minor prompt variations.
Standard caching cannot handle natural language permutations.
Implemented vector-based similarity checks and sub-millisecond edge code filters to intercept queries before they reach expensive models.
What Changes If You Believe This?
Place semantic caching and edge filtering middleware in front of frontier models to route, dedupe, and solve routine logic with code rather than tokens.
Cut runtime API spend by 50%+ without degrading response quality, halting software margin erosion as user base scales.
Offer higher usage tiers by lowering the unit cost of redundant interactions.
Isolate sensitive query responses within local infrastructure boundaries.
Specification Maturity & Ecosystem Spread
Recommended Action by Role
Place semantic caching and edge filtering in front of models; never pay a generative model to handle a task traditional code or caching can solve.
Exogram Margin Calculator
Calculate gross margin recovery through semantic caching.
Latest Publications & Research Activity
How to Reduce LLM API Token Costs in Production
How to Reduce LLM Costs in Production: The Inference Dividend Model
Growth Is Not Your Cost Problem - Your Architecture Is
Frequently Asked Questions
Q:What is Semantic Caching?
Using vector embeddings to find similar previous queries and serve cached responses without calling an external AI model.
Inspectable Evidence Ledger
Classified evidence items supporting, extending, or refining this canonical research specification.
| Evidence Item | Publisher | Evidence Type | Strength | Role | Action |
|---|---|---|---|---|---|
| How to Reduce LLM API Token Costs in Production | Beehiiv | Architecture Guide | ★★★★★ | Extends | Inspect ↗ |
| How to Reduce LLM Costs in Production: The Inference Dividend Model | Production Telemetry | ★★★★★ | Extends | Inspect ↗ | |
| Growth Is Not Your Cost Problem - Your Architecture Is | Executable | ★★★★★ | Extends | Inspect ↗ | |
| Semantic Caching Playbook | Beehiiv | Framework Module | ★★★★ | Origin | Inspect ↗ |
Recommended Citation
Ewing, R. (2026). "Semantic Caching." Richard Ewing Research Canon. Available at: https://www.richardewing.io/concepts/semantic-caching
@article{ewing_semantic_caching,
author = {Ewing, Richard},
title = {Semantic Caching},
journal = {Richard Ewing Research Canon},
year = {2026},
url = {https://www.richardewing.io/concepts/semantic-caching}
}