Audyense
Kyvos
VerifiedAnalytics & BI · www.kyvosinsights.com/
Overview
Kyvos is a universal semantic layer that sits between enterprise data platforms and the BI tools, chatbots and AI agents that consume them, defining metrics, dimensions and hierarchies once so every tool returns the same answer. It builds and manages aggregated semantic data models on the customer's data lake or warehouse to deliver sub-second query response at billion-row scale and high concurrency without pushing every query down to expensive compute. It is commonly used to replace legacy OLAP engines such as SSAS, Essbase and TM1 with a cloud-native equivalent.
The problem Kyvos solves
Enterprises with billions of rows in a cloud warehouse hit a wall where interactive dashboards either time out or drive runaway compute bills, and every BI tool and AI agent ends up with its own slightly different definition of the same metric. Kyvos solves that by defining metrics, dimensions and hierarchies once in a semantic layer and serving queries from pre-built aggregates, so Power BI, Tableau, Excel and AI agents all get the same governed answer in under a second without pushing each query down to metered warehouse compute.
Decision context
Use these points to test whether the product fits your operation, not just whether it has a long feature list.
- Published starting price: Free. Confirm user, usage, and feature limits for the plan you would actually buy.
- Deployment: cloud, on_premise, hybrid. Check security, data-residency, and access requirements for every team that will use it.
- Verified integrations include Microsoft Power BI, Tableau, Microsoft Excel, Looker, Strategy (MicroStrategy), Snowflake. Validate sync direction and plan limits for the connections that matter.
- This record was last checked on 7/31/2026; pricing and features can change.
How to evaluate Kyvos
A listing helps create a shortlist; a trial with the team’s real workflow decides whether the tool fits. Use this reading with the structured facts and confirm changes with the vendor.
Workflow fit
The record describes it as a fit for Enterprises running interactive BI on billions of rows in Snowflake, Databricks, BigQuery or Redshift where warehouse compute costs are escalating, Teams migrating legacy OLAP cubes off SSAS, Essbase, TM1 or Azure Analysis Services to a cloud-native multidimensional engine, Organizations grounding AI agents and conversational analytics in governed enterprise metrics to reduce hallucination and metric drift. Check that this context matches the volume, roles, and processes your team needs it to support.
Pilot questions
- Can Kyvos complete the critical workflow without manual work outside the product?
- Do the recorded connections (Microsoft Power BI, Tableau, Microsoft Excel, Looker) support the sync direction, permissions, and volume we need?
- What user, usage, storage, support, or security limits appear after the headline starting price?
Evidence and freshness
This record was checked on 7/31/2026. That date tells you when the record was reviewed, not that the vendor has left its terms unchanged since then.
Best for
- Enterprises running interactive BI on billions of rows in Snowflake, Databricks, BigQuery or Redshift where warehouse compute costs are escalating
- Teams migrating legacy OLAP cubes off SSAS, Essbase, TM1 or Azure Analysis Services to a cloud-native multidimensional engine
- Organizations grounding AI agents and conversational analytics in governed enterprise metrics to reduce hallucination and metric drift
Not a fit if
- Small teams or startups with modest data volumes, where the modeling effort and infrastructure footprint far exceed the benefit of a dedicated semantic layer
- Buyers wanting an out-of-the-box BI front end with rich visualization and mobile apps, since Kyvos is an acceleration and semantics layer that assumes you bring your own BI tool
Why it’s listed
- One of the few semantic layers that pre-aggregates data rather than relying purely on query pushdown, which is what lets it hold sub-second response on billion-row datasets at high concurrency.
- Serves both BI and AI consumers from the same governed model, exposing an MCP server and LangChain connectivity alongside SQL, MDX and DAX for traditional BI tools.
- Published, transparent per-core-hour pricing plus a genuinely free forever tier, which is unusual in the enterprise semantic layer category.
Pricing
Kyvos Free
FreeA no-cost, full-featured single-node edition intended for smaller datasets and evaluation, available via AWS Marketplace with no end date.
- Access to advanced product features at no software cost
- Practical up to roughly 2 billion records
- Maximum 50 million cardinality per dimension
- Single-node deployment (8-core / 32 GB minimum)
- Underlying cloud infrastructure billed separately
Kyvos on Cloud Marketplace
$0 per core hourSelf-deployed into your own AWS, Azure or Google Cloud account, billed per second through your existing cloud bill.
- $0.41 per core hour, billed per second
- No fixed monthly or minimum charges
- Data stays in your own cloud account
- Choose your own region and availability zone
- Spend counts toward your cloud commit
Kyvos Managed Service
$1 per core hourVendor-managed deployment on dedicated infrastructure, with the license, infrastructure and ongoing management bundled together.
- $0.61 per core hour, billed per second
- Includes infrastructure and management
- Dedicated infrastructure on your chosen cloud and region
- Connects to the data lake of your choice
- SOC 2 Type II compliant
- Discounts for committed bulk usage
Kyvos On Premise
$48,000 per yearDeployed into your own environment on AWS, Azure, Google Cloud or Cloudera, with data never leaving your infrastructure.
- Annual subscription starting at $48,000
- Deploy on infrastructure of your choice
- Data stays entirely in your infrastructure
- Pay-per-use option also available
- Discounts for committed bulk usage
Features
Integrations
Security & compliance
Pros & cons
Pros
- Query performance on very large datasets is the standout theme, with reviewers reporting reports that previously took minutes returning in seconds
- Meaningful reduction in cloud warehouse spend because aggregates absorb query load instead of pushing it down to metered compute
- Works through existing BI tools via native connectors, so analysts keep using Power BI, Tableau or Excel rather than learning a new interface
- Strong enterprise security and governance depth: row and column-level security, RBAC, SSO, column masking and visual audit logs
Cons
- Steep learning curve; effective use assumes solid grounding in multidimensional data modeling and big data environments
- MDX expertise is needed for advanced work, which slows down teams without prior OLAP experience
- Initial setup, cube design and build cycles require significant upfront investment before value is realized
- The admin and modeling UI is described as less intuitive than expected, and cube-scoped models can make cross-dataset exploration feel siloed
What we found
Reviewers consistently rate query speed at scale and cloud cost reduction very highly, while flagging a steep initial learning curve around multidimensional modeling and MDX.
Ratings and review counts come from public review platforms. We link to the original source and keep the underlying review text out of this profile.
User reviews
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