What is Vector Vault?
Vector Vault is a semantic proxy network that sits between enterprise AI agents and frontier LLMs. It intercepts incoming agent queries, searches a locally stored Known Good Answer library using cosine similarity, and serves validated responses in under 10 milliseconds without the query reaching the frontier model. Cache misses route dynamically to the most cost-effective capable model. The result: 42% blended token cost reduction on Day 1, 312× latency improvement, full model portability, and data sovereignty.
How does Vector Vault reduce LLM token costs?
Vector Vault reduces LLM token costs by intercepting semantically redundant queries before they reach the frontier model. Enterprise AI agent workflows loop repeatedly — the same or semantically equivalent queries are sent to frontier LLMs at full price on every cycle. Vector Vault embeds incoming queries as high-dimensional vectors, performs cosine similarity search against a Known Good Answer library, and serves validated responses locally at $0.002 per million tokens when semantic equivalence exceeds threshold. This delivers a 42% blended cost reduction on Day 1 compared to routing all queries directly to frontier LLMs at $15+ per million tokens.
How is Vector Vault different from RAG (Retrieval-Augmented Generation)?
Vector Vault and RAG are fundamentally different. RAG retrieves context documents to augment a query before sending it to an LLM — the LLM call still happens and is still billed. Vector Vault intercepts before the LLM is invoked entirely. When a semantically equivalent query has been answered before, Vector Vault serves the validated response locally in under 10ms with no LLM call, no token spend, and no data transmission to external APIs. RAG improves answer quality. Vector Vault eliminates redundant spend before it occurs.
How is Vector Vault different from CloudZero or other AI FinOps tools?
CloudZero, Apptio, and similar AI FinOps tools operate as reporting and visibility layers — they show you what you spent after billing has occurred. Vector Vault is an upstream infrastructure layer that prevents token spend before it happens. The distinction: visibility tools close the reporting gap. Vector Vault closes the spend gap. Vector Vault also produces native operating metrics — CPT (cost per token), CPR (cost per response), and CPAM (cost per agent minute) — as direct proxy-layer outputs, not post-hoc reporting add-ons.
Does Vector Vault require re-architecting existing AI agent deployments?
No. Vector Vault is a drop-in proxy that requires zero re-architecture. Enterprises point their existing AI agents at the Vector Vault endpoint instead of directly at the frontier LLM API. No model migration, no agent rebuild, no workflow changes. Deployment is measured in hours, not months.
How does Vector Vault keep enterprise data secure?
Vector Vault protects enterprise data through local vector embeddings. On cache hits, zero bytes of proprietary data are transmitted to external LLM APIs. The query resolves entirely within the enterprise perimeter — protecting proprietary workflows, pricing models, customer records, PII, and competitive intelligence. Vector Vault's local cache architecture satisfies data residency requirements under GDPR, HIPAA, SOC 2, and the EU AI Act.
Is Vector Vault model-agnostic?
Yes. Vector Vault routes to any LLM — OpenAI GPT-4o, Anthropic Claude, Google Gemini, Meta Llama, Mistral, or any fine-tuned or open-source model. Cache misses are routed to the most cost-effective capable model based on the sensitivity tier of the query payload. Enterprises can run multiple LLMs simultaneously and switch providers without rearchitecting their agent stack.
What is a Semantic Cache Hit Rate (SCHR)?
Semantic Cache Hit Rate (SCHR) is the percentage of incoming AI agent queries resolved from the local Known Good Answer library without reaching the frontier LLM. Vector Vault targets a 30–50% SCHR range, with an enterprise benchmark of 42%. A 42% SCHR means 42 out of every 100 agent queries are served locally — eliminating their token cost and latency entirely. SCHR grows over time as the Known Good Answer library accumulates more validated responses.
Can OpenAI or Anthropic just build this feature themselves?
No frontier LLM provider can replicate Vector Vault without fundamentally undermining their own inference revenue. The value Vector Vault creates requires routing across multiple competing LLMs simultaneously — caching responses from OpenAI to avoid future calls to Anthropic, and vice versa. Building this would require a frontier provider to actively commoditize their own token billing. Additionally, Vector Vault's Known Good Answer library is a proprietary enterprise asset owned by the customer — a structural moat no single model provider can replicate.
Who is the Vector Vault founding team?
Vector Vault was co-founded by Tony Wenzel (CEO), Mark Ackerman (CTO), and Brent Christensen (VP Engineering). Tony is a serial operator: SVP Sales at STRATACACHE, CRO at AgilePoint, CEO of DaNoraAI, and co-founder of Brandometry (NYSE ARCA ETF). Mark and Brent spent 15 years designing and operating some of the highest-performance sovereign cache infrastructure ever deployed — 100Gbps+ per node across retail, telecom, and government. Vector Vault is a ground-up rebuild of that proven architecture for the AI token economy. All three co-founders share a 20+ year personal and professional history.