AI Savings | Kyvos

Every AI question bills you
on two fronts

The cost compounds as AI scales

Bigger data

Larger datasets increase the compute cost of every query

Higher concurrency

More simultaneous usage drives warehouse autoscaling

Harder questions

Complex analysis requires more reasoning and compute

Kyvos changes AI economics

Kyvos brings both meters down—fewer tokens spent reasoning, less compute spent answering.
One lower bill instead of two runaway ones

Rich context

The model gets the complete business context up front, so it needs fewer reasoning cycles—and fewer tokens plus icon

Smart orchestration

Answers served straight from the semantic layer—reducing warehouse re-scans, lowering compute spend

Proven at real
enterprise scale

Kyvos delivers measurable cost savings as AI scales
across the enterprise.

60%+ fewer tokens

per question, on billion-row analytics workloads

50%+ lower compute

for fetching data from warehouse

50%+ savings on enterprise AI workloads

The savings compound with scale

As AI reaches more users, analyzes larger datasets and supports more complex workloads, enterprises can realize even greater savings.

The foundation for AI savings at scale

Every layer of the architecture works together to give AI a governed understanding of the business—so agents reach answers with less reasoning and less warehouse work, and cost stays low as usage grows

Under the Hood

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Workspace to assemble and govern
the context and tools AI use cases need

AI Space

Context-aware agent,
purpose-built for enterprise data scale

Dialogs

Kyvos Joins
Apache Ossie

An open, interoperable semantic standard for enterprise AI

COMING SOONBusiness
Knowledge Graphs

Maps KPIs, entities, concepts and knowledge with their rules, weights and causal paths

AI Space

Governed workspace to assemble the context & tools an AI use case needs

Model
Routing

The best-fit, most cost-efficient AI model for each task

FAQs

Why is running AI on enterprise data so expensive?

Every AI question runs two costs. The model spends tokens to reason, and the warehouse spends compute. Both land on your cloud bill, usually as one number.

What are token costs and compute costs in enterprise AI?

Token costs are what you pay the AI model to reason toward an answer. Compute costs are what you pay the warehouse to run the queries behind that answer. They are two separate bills.

How does Kyvos reduce AI costs?

Kyvos is a semantic layer that gives AI governed business context. The model then reasons in fewer tokens and the warehouse runs less compute. On an industry-standard TPC-DS benchmark run against a billion-row Snowflake warehouse, Kyvos reduced AI token consumption by over 60% and warehouse compute by over 50% compared to the same workload without Kyvos.

How much can enterprises save with Kyvos on AI workloads?

Kyvos lowers the cost of every AI question by cutting both token use and query compute. As enterprises grow their AI footprint with more users, more questions and larger datasets, these per-question savings compound fast. On a conservative benchmark, it delivered savings of ~$3.2M a year. Larger deployments save considerably more.

Does Kyvos replace my data warehouse or AI model?

No. Kyvos sits between your AI and your data. The same model and warehouse stay in place. Only the path to the answer changes.

Are Kyvos AI savings proven or estimated?

Proven. They come from an industry-standard TPC-DS semantic model on a billion-row Snowflake warehouse, run with Kyvos and without on identical infrastructure.

Run more AI for less

Every AI question bills you
on two fronts

The cost compounds as AI scales