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Universal Semantic Layer : The foundation for instant, actionable, agentic analytics
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>50% Savings

Massive Analytics Savings
on the Cloud

Price-performant analytics even as data volumes,
users and complexity grow

Key Takeaways

  • Predictable analytics spend as users, data and demand scale
  • Lower warehouse and cloud compute costs
  • No overprovisioning to meet peak demand
  • BI interactions reuse optimized results from a shared semantic layer
  • Deeper analytics without driving higher compute costs
Annual Cloud Cost
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The Cost Challenge in Enterprise Analytics

Enterprise analytics becomes expensive under real-world usage. Cloud costs rise rapidly as data volumes grow, users increase and AI and analytics workloads become more complex—forcing enterprises to limit usage, restrict access or trade analytical depth for budget control.

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    Cost rises as AI and BI use cases become wider and deeper

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    Every analytics interaction triggers fresh compute

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    Concurrency multiplies spend as usage increases

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    Warehouses are over-scaled to handle peak loads

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    Drill-downs and ad hoc analysis drive expensive compute

Control analytics spend as users, data and demand grow

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    No cost spikes as data volumes and refresh frequency grow

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    Stable analytics spend as user concurrency increases

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    Predictable costs as query volume and analytical demand intensify

Data Platform Benchmark

Lower the cloud spend by 65% on GBQ even for 100 users running 500 queries concurrently

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Break the link between performance and warehouse size

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    No query pushdown to warehouse, reducing compute consumption

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    Avoid scaling warehouses solely to maintain dashboard speed

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    Eliminate long-term capacity investments to tackle temporary spikes

Solution Brief

Reduce warehouse querying costs by over 50% across 5-million-row datasets

Read more 10,000+ KPIs on a single model

No overprovisioning as analytics scale

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    No expensive data imports into BI tools

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    No premium capacity stacking to maintain dashboard speed

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    No infrastructure expansion as data volumes grow

Case Study

How a retail services powerhouse slashed MS Fabric costs by 20%+ with Kyvos

Read more How a retail services powerhouse slashed MS fabric costs by 20 with Kyvos

Stop paying for inefficiency at the BI layer

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    Dashboards interactions no longer trigger heavy compute

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    No duplicated semantic models or extracts across BI

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    Minimize spend by serving all BI tools from a shared semantic layer

Case study

Large-scale analytics without exponential cost growth

Read more Large-scale analytics without exponential cost growth

High-grain analytics without runaway costs

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    Multidimensional analysis without exponential compute growth

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    Granular, transaction-level analysis without cost explosion

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    Roll-ups and drill-downs without incremental cost

Case Study

Analyze 4+ years of transactional-level data, without cost explosion

Read more Analyze 4+ years of transactional-level data, without cost explosion

How Kyvos Fits in Your
Analytics Stack

How Kyvos Delivers Enterprise-Scale Savings

Kyvos works alongside existing BI tools and data platforms without requiring re-architecture.

  • Cloud-native scalable architecture

    Built to scale seamlessly on modern cloud platforms without infrastructure rework

    Cloud-native scalable architecture

  • No per-query cost

    Users can run unlimited queries without triggering incremental compute charges.

    No per-query cost

  • Automatic load-based scaling

    Dynamically scales compute up or down based on query volume and user demand.

    Automatic load-based scaling

  • No pushdown

    Reduces warehouse workload by avoiding per-query execution on expensive cloud compute.

    No pushdown

Our Impact

Responses in less than 5 seconds on 500 billion transactions at a global bank
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Global sports betting company migrates SSAS to Kyvos for deeper analytics
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Global fintech ends analytical silos with Kyvos
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Pharmacy chain delivers self-service analytics for 20,000+ suppliers
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FAQs

How does Kyvos reduce cloud analytics costs by over 50%?
Kyvos removes repeated and unnecessary compute from the analytics. Instead of running every dashboard interaction, drill-down or ad-hoc query on expensive cloud warehouses, Kyvos serves analytics from its semantic layer. This significantly lowers warehouse scans, compute consumption and over-provisioning—resulting in over 50% cost reduction.
Do analytics costs still increase as data volumes grow?
No. With Kyvos, analytics costs are not directly tied to raw data size. As data volumes grow, Kyvos ensures analytics reuse optimized results instead of reprocessing raw data repeatedly. This keeps costs controlled even as datasets expand into billions of rows.
How does Kyvos prevent cost spikes during peak usage or reporting cycles?
Kyvos isolates analytics execution from warehouse contention and peak workload pressure. Because dashboards and ad-hoc queries don’t hit the warehouse directly, cost spikes from user surges, reporting cycles or month-end analysis are avoided.
Can Kyvos support more users without increasing costs linearly?
Yes. Kyvos is designed to handle high concurrency efficiently. As more users access analytics simultaneously, costs do not scale linearly because analytics execution is decoupled from per-user warehouse compute.
How does Kyvos help control costs for ad-hoc and exploratory analysis?
Ad-hoc queries and deep drill-downs typically trigger expensive warehouse compute. Kyvos enables these analysis without repeatedly scanning raw data, allowing teams to explore data freely without worrying about budget overruns.
Does Kyvos require over-provisioning infrastructure for peak demand?
No. Kyvos eliminates the need to size infrastructure for peak usage. Its distributed, elastic architecture aligns resource usage with actual demand, so enterprises don’t pay for idle capacity during non-peak periods.
Does Kyvos replace the data warehouse or lakehouse?
No. Existing data warehouses and lakehouses remain the system of record. Kyvos sits above them as a semantic layer, reducing warehouse load while preserving existing data platforms and investments.
Which cloud data platforms does Kyvos help reduce costs on?
Kyvos works with all major cloud data platforms, including Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse and data lakes. Enterprises consistently see reduced compute consumption and lower cloud analytics spend across these platforms.

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