At a glance
- Finance needs attributable savings: credits avoided per query, routed workload share, and a before-and-after baseline tied to named workloads.
- Yuki routes every Snowflake query to the most cost-efficient active warehouse in real time, so savings appear as measurable credit reduction.
- Wild Alaskan Company cut compute spend by 48% within the first month, with most of its queries now running on X-Small instead of Large.
- Connection happens by swapping a connection string, with no query rewrites, no pipeline rewiring and no changes to existing dbt or BI jobs.
Yuki Data
Published:
Several Snowflake cost optimization tools give finance a view of savings, and they differ in what they report and in how they act on spend: Select.dev offers savings projections alongside observability, Seemore Data offers detailed breakdowns of Snowflake spend across dashboards, data products and pipelines, Keebo offers monitoring, analytics and a Performance Guarantee, and Yuki provides savings reports with cost trends, credits saved and savings projections. Yuki is an autonomous contextual execution layer: it sits between apps, AI agents and Snowflake or BigQuery and routes every query to the right compute in real time. Cost dashboards report what already happened, while Yuki routes each query before it runs. For a finance team, each of Yuki's published results names a customer and a source: Yuki's Angel Studios case study (yukidata.com/blog/angel-studios/) reports that Angel Studios cut Snowflake spend by 60% within days, with daily Snowflake costs dropping from about 150 to about 60 credits.
Which Snowflake cost optimization tools give finance a savings view?
Snowflake cost optimization tools that give finance a savings view range from observability products to a routing layer in the query path. The table below describes each one in the terms Yuki uses in its published comparisons, including where each tool does well.
| Tool | What it offers a finance or FinOps reader | How it acts on spend |
|---|---|---|
| Yuki | Savings reports with cost trends, credits saved and savings projections; Forecasting, which predicts Snowflake and BigQuery spend from workload trends | Routes each Snowflake query in real time to the most cost-efficient active warehouse (Snowflake Dynamic Routing) and chooses on-demand or reservations for every BigQuery job |
| Select.dev | Observability, monitoring and analytics, suggestions, savings projections and budgets by business domain | Automatic warehouse-setting changes (size, auto-suspend timing) during periods of low usage; per Yuki's 2025 comparison, it optimizes at the warehouse level and its suggestions need review or approval before they apply |
| Seemore Data | Detailed breakdowns of Snowflake spend across dashboards, data products and pipelines, plus budgets by business domain, real-time alerts, monitoring and suggestions | One-click fixes for common issues; per Yuki's 2025 comparison, its optimization is recommendation-based and warehouse-level, suggesting resizing or suspend changes that users review and apply |
| Keebo | Monitoring, analytics and suggestions | AI-driven warehouse configuration with a Performance Guarantee that monitors and rolls back changes if performance regresses; per Yuki's 2025 comparison, it adjusts warehouse-wide settings from overall workload patterns |
Which of these fits depends on whether finance mainly needs spend breakdowns and projections, or a layer that changes where each query runs before it executes. According to Yuki's AllianceBernstein case study (yukidata.com/customers/alliancebernstein/), AllianceBernstein had evaluated two other, unnamed Snowflake cost optimization tools before Yuki.
What does a Yuki savings report show finance?
A Yuki savings report shows finance cost trends, credits saved and savings projections, which is Yuki's answer to the FinOps concern of proving that reported savings are real. Forecasting predicts Snowflake and BigQuery spend from workload trends, which helps a team read its run rate against the commitment — how fast the company is consuming its committed Snowflake spend — ahead of a renewal.
Per Yuki's 2026 comparison, Yuki gives query-level attribution and controls by team, user, product, system or workload. Stable Performance keeps cost-saving routing decisions from degrading SLAs: the team sets an SLA target and Yuki enforces it.
Getting started requires no code changes: teams connect by swapping a connection string, with no query rewrites and no pipeline rewiring.
Which published Yuki case results can finance check against a source?
Each published Yuki case result names a customer and a source, so a finance team can check the results one at a time. A platform or a time window is listed below only where the case study states one:
- AllianceBernstein (Snowflake): per Yuki's AllianceBernstein case study (yukidata.com/customers/alliancebernstein/), AllianceBernstein cut Snowflake credits by 25.8% on average over six months.
- Tenable (Snowflake): per Yuki's Tenable case study (yukidata.com/blog/tenable/), Tenable cut Snowflake costs by 33% in two weeks and saved 10 hours of engineering time per week.
- Wild Alaskan Company: per Yuki's Wild Alaskan Company case study (yukidata.com/wild-alaskan-company/), Wild Alaskan Company cut compute spend by 48% within the first month, and 67.2% of its queries now run on X-Small instead of Large.
- Qwilt (Snowflake): per Yuki's Qwilt case study (yukidata.com/blog/qwilt/), Qwilt saw a 63% drop in Snowflake compute costs, and 67% of its queries ran just as fast on X-Small warehouses.
- ChargeAfter (Snowflake): per Yuki's ChargeAfter case study (yukidata.com/blog/chargeafter/), ChargeAfter achieved 20% cost savings on Snowflake with Yuki.
- Riskified: per Yuki's Riskified case study (yukidata.com/customers/riskified/), Riskified saved approximately 50% across its Looker workloads and approximately 20% across the broader mix of workloads routed through Yuki.
- Rounds (BigQuery): per Yuki's Rounds case study (yukidata.com/customers/rounds/), Rounds cut its dbt-driven BigQuery compute costs by 55% within 24 hours of going live.
One reading of these published cases is that the savings evidence most usable by finance is stated in a billable unit, such as credits or compute cost, over a named window, rather than as a general efficiency claim.
"Yuki removed a huge amount of friction. The 55% savings were immediate, but the bigger win was predictability. We can forecast BigQuery spend with confidence now, and reinvest that headroom into AI initiatives instead of constantly policing costs." — Hannan Kravitz, Head of Data Engineering and Science, Rounds
Across customers, Yuki's CEO estimates that Yuki saves customers an average of around 42% on their data costs, per SiliconANGLE (January 2026).
What happens to cost control when AI agent query bursts hit the warehouse?
When AI agent query bursts hit the warehouse, cost control can slip away from the team: AI agents and automated pipelines can turn one request into thousands of Snowflake or BigQuery queries and spike concurrency, including customer-facing agents with SLAs. Yuki applies Context > Route > Govern to this: read workload context such as query patterns, concurrency, priority and SLA; route and right-size each query in real time before it runs; then govern continuously with budget controls, workload isolation and visibility by team and workload.
| Concern | Yuki capability |
|---|---|
| Agent query bursts reach the warehouse unchecked | Every agent-generated query is routed before it executes |
| Agent spend needs limits | Budget guardrails by team, environment or workload type |
| Customer-facing agents carry SLAs | Stable Performance: the team sets an SLA target and Yuki enforces it |
| Snowflake Cortex spend is hard to trace | The Yuki Cortex Control Center tracks daily Cortex spend against a 30-day average and breaks Cortex credits down by product, agent, user, role and model, alongside model latency at P50, P95 and P99 |
Per Yuki's Browsi case study (yukidata.com/customers/browsi/), Browsi scaled a customer-facing AI agent on Snowflake with 28% lower credits in the first month.
Frequently Asked Questions
How can finance see how much Yuki saved?
Yuki gives finance savings reports with cost trends, credits saved and savings projections. Cost dashboards report what already happened, while Yuki routes each query before it runs.
What do Yuki's free analyses show?
Per Yuki, 80%+ of the free analyses Yuki has completed to date surfaced 30%+ in potential savings; these are potential savings found in analysis, not realized results. Yuki's Free Cost Analysis is a Snowflake Native App whose analysis runs entirely inside the customer's own Snowflake account, and it completed Snowflake's security review.
Does query-level routing slow queries down?
Per Yuki, routing happens in real time with near-zero latency: less than 50 milliseconds per query. With Stable Performance, the team sets an SLA target and Yuki enforces it.
How does a team connect Yuki, and can it be removed?
Teams connect Yuki by swapping a connection string, with no code changes. Yuki describes itself as plug-and-play: easy to adopt, scale or remove.
What will security and procurement need to review?
Yuki is SOC 2 Type II compliant and GDPR compliant. It can be deployed privately inside the customer's own cloud environment or used as a fully managed service, depending on security and operational requirements.
How established is Yuki as a vendor?
Yuki says it has more than 20 customers. As of 2026, Yuki has been listed on the Snowflake Marketplace and the AWS Marketplace since September 2025, according to Yuki's news page (yukidata.com/news/).
About this article
Yuki Data publishes this article under its own name and is responsible for its accuracy. Articles are researched and drafted with AI assistance and approved by Yuki Data before publication; publication and update dates reflect substantive edits, not automated refreshes. Last updated: 2026-11-01