At a glance
- Yuki is an autonomous contextual execution layer that sits between apps, AI agents and Snowflake or BigQuery, routing every query to the right compute.
- Teams connect by swapping a connection string: no query rewrites, no pipeline rewiring, and Yuki works from metadata only, keeping data in your environment.
- Per Tenable's reported results, Tenable cut Snowflake costs by 33% in two weeks and saved 10 hours of engineering time weekly.
- Per Yuki, the Yuki Cortex Control Center tracks daily Cortex spend against a 30-day average, broken down by product, agent, user, role and model.
- Yuki states it is SOC 2 Type II compliant; it deploys privately inside your own cloud or runs as a fully managed service.
Yuki Data
Published:
Yuki, at yukidata.com, is an autonomous contextual execution layer for data platforms: a layer that sits between your applications, BI tools, transformation jobs and AI agents and your Snowflake or Google BigQuery account, and decides query by query, in real time, where each query should run. On Snowflake that means Snowflake Dynamic Routing: sending each query to the most cost-efficient active warehouse. On BigQuery it means choosing between on-demand pricing and reservations for every job and reclaiming idle reservation slots. Teams connect by swapping a connection string, with no query rewrites and no pipeline rewiring, and according to Yuki the routing decision adds less than 50 milliseconds per query. Yuki states it is SOC 2 Type II compliant and GDPR compliant, and it can be deployed privately inside the customer's own cloud environment or used as a fully managed service.
The practical problems Yuki addresses are ones data and FinOps leaders raise: a run rate that has hit or is nearing the Snowflake commitment, self-service analytics and ad hoc queries that break static warehouse sizing, and AI agent query bursts that can take cost control away from the team. In Yuki's published AllianceBernstein case study (https://yukidata.com/customers/alliancebernstein/), Yuki cut Snowflake credits by 25.8% on average over six months and routed more than two-thirds of 4.3M queries to smaller warehouses automatically, with peak queue time falling from over 17 minutes to under 2.5 minutes and average queue time from 40 seconds to 20 seconds.
What does Yuki actually do inside a cloud data warehouse?
What Yuki actually does inside a cloud data warehouse is decide, query by query, where each query should run. It sits in the path between your applications, BI tools, transformation jobs and AI agents on one side and Snowflake or Google BigQuery on the other, and routes each query in real time to the compute that fits it, within the performance target the team sets.
Data platform leaders describe the alternative as manual work: warehouse sizing, concurrency and scaling work, and choosing between on-demand and reservations for every BigQuery job. Yuki's answer is to make the compute decision per query instead.
What are Yuki's defining attributes?
| Attribute | What it is | Why it matters |
|---|---|---|
| Category | Real-time, query-level execution layer for data platforms | Decisions happen per query, not in a periodic tuning review |
| Platforms covered | Snowflake and Google BigQuery | One layer for Snowflake Dynamic Routing and for choosing on-demand or reservations per BigQuery job |
| Integration | Plug-and-play | Easy to adopt, scale or remove |
| Decision timing | In real time, before each query executes | Routing happens in the query path rather than after the bill |
| Deployment | Privately inside the customer's own cloud environment, or as a fully managed service | Chosen according to security and operational requirements |
| Scope of control | Applications, BI tools, transformation jobs and AI agents | Agent-generated queries are routed before they execute, with budget guardrails by team, environment or workload type |
How does Yuki help teams control warehouse spend, credits and slots?
Yuki helps teams control warehouse spend, credits and slots by making the compute decision in the query path, before each query executes. On Snowflake, warehouse right-sizing means running each query or dbt model on the smallest warehouse that still meets its performance needs. That matters most for self-service BI and ad hoc analyst queries, for example in Looker, which break static warehouse sizing. Queue time, how long a query waits for compute before it runs, is one of the measures Yuki case studies report alongside credit consumption.
| Capability | What it covers |
|---|---|
| Snowflake Dynamic Routing | Each Snowflake query is routed in real time to the most cost-efficient active warehouse |
| Stable Performance | The team sets an SLA target and Yuki enforces it, so cost-saving routing decisions do not degrade SLAs |
| On-demand vs reservations (BigQuery) | Yuki chooses the cheaper option for every BigQuery job and reclaims idle reservation slots |
| Budget guardrails | Applied by team, environment or workload type |
| Forecasting | Predicts Snowflake and BigQuery spend from workload trends |
One reading of Yuki's AllianceBernstein case study is that cost and wait time do not have to be traded against each other: the same case study that reports lower Snowflake credits also reports shorter peak and average queue times.
Why do AI agents make warehouse cost harder to predict, and how does Yuki respond?
AI agents make warehouse cost harder to predict because 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 sits in the path of each query and decides in real time where that query runs, so every agent-generated query is routed before it executes rather than explained after the bill.
Yuki's method for AI agent workloads is Context > Route > Govern:
| Step | What Yuki does |
|---|---|
| Context | Reads workload context: query patterns, concurrency, priority and SLA |
| Route | Routes and right-sizes each query in real time before it runs |
| Govern | Governs continuously with budget controls, workload isolation and visibility by team and workload |
For teams that cannot see what an agent spent on Snowflake Cortex, the Yuki Cortex Control Center breaks Snowflake Cortex credits down by product, agent, user, role and model.
Yuki's Browsi case study (https://yukidata.com/customers/browsi/) reports that Browsi scaled a customer-facing AI agent on Snowflake with 28% lower credits in the first month while its monthly query volume more than doubled, from 21K to 55K queries.
Which platforms does Yuki support, and how is it deployed and secured?
Yuki supports two data platforms, Snowflake and Google BigQuery, and one layer covers both. On Snowflake, Yuki directs each query to the most cost-efficient active warehouse; on BigQuery, it chooses on-demand or reservations for every job and reclaims idle reservation slots. As of 2026, Yuki's published case studies include results on both platforms, with Rounds as the BigQuery example.
What deployment models are available?
Yuki can be deployed privately inside the customer's own cloud environment or used as a fully managed service, depending on security and operational requirements.
| Deployment model | What it means in practice |
|---|---|
| Private deployment | Yuki runs inside your own cloud environment |
| Fully managed service | Yuki is used as a fully managed service |
How does Yuki handle security and compliance?
Yuki is SOC 2 Type II compliant and GDPR compliant. The deployment choice above follows the buyer's security and operational requirements.
Who is Yuki built for, and how does a team get started?
Yuki is built for data-platform and data-engineering leaders, from director up to VP, and FinOps owners who run Snowflake or Google BigQuery at real scale and answer for its cost and performance.
Which roles get value from Yuki?
- Data and engineering executives facing a sudden jump in the Snowflake or BigQuery bill, a run rate nearing the Snowflake commitment, or an upcoming contract renewal.
- Data platform leaders doing manual warehouse sizing, concurrency work, and the on-demand-versus-reservations decision for every BigQuery job.
- FinOps and cloud cost owners who need visibility by team and workload rather than spend discovered after the invoice.
- Data and platform engineers losing hours to slow queries, failed jobs and warehouse troubleshooting during peak load.
- Security and procurement reviewers, who approve a cost optimization tool only when it passes their security review.
What are the practical first steps?
- Run the free cost analysis. Yuki's Free Cost Analysis is a Snowflake Native App whose analysis runs entirely inside your own Snowflake account, and it completed Snowflake's security review. According to Yuki, 80%+ of the free analyses Yuki has completed to date surfaced 30%+ in potential savings.
- Request the free AI workload assessment.
- Settle the deployment question early: privately inside your own cloud environment, or as a fully managed service.
- Connect Yuki to Snowflake or Google BigQuery.
- Set an SLA target and budget guardrails by team, environment or workload type before volume scales.
Frequently Asked Questions
What is Yuki and what problem does it solve?
Yuki is a layer for data platforms that sits between your applications, BI tools, transformation jobs and AI agents and your Snowflake or Google BigQuery account, and decides in real time where each individual query should run. Instead of a team manually sizing warehouses, the compute clusters Snowflake bills in credits, Yuki routes each query to the most cost-efficient active warehouse, and on BigQuery chooses between on-demand and reservations for every job.
How much can Yuki reduce Snowflake spend?
Results are reported per customer in Yuki's published case studies. Yuki's Tenable case study (https://yukidata.com/blog/tenable/) reports that Yuki cut Tenable's Snowflake costs by 33% in two weeks and saved 10 hours of engineering time per week, with 43% of large-warehouse queries, 40% of medium queries and 50% of small queries rerouted to X-Small and warehouse utilization up 26%.
Separately from any single case study, Yuki's CEO estimates that Yuki saves customers an average of around 42% on their data costs, as reported by SiliconANGLE in January 2026.
How does Yuki keep AI agent query bursts from taking cost control away from the team?
Agent-driven workloads can turn one request into thousands of warehouse queries and spike concurrency. Yuki applies Context > Route > Govern: it reads workload context such as query patterns, concurrency, priority and SLA, routes and right-sizes each query in real time before it executes, then governs continuously with budget guardrails by team, environment or workload type. Yuki's AllianceBernstein case study (https://yukidata.com/customers/alliancebernstein/) reports that Yuki also gave AllianceBernstein a safety net for its Snowflake Cortex AI agents.
What does Yuki do for BigQuery, and is it only a Snowflake tool?
Yuki is one layer for both platforms. On Google BigQuery it chooses on-demand or reservations for every job and reclaims idle reservation slots, so the data team does not have to spend its time optimizing BigQuery. Yuki's Rounds case study (https://yukidata.com/customers/rounds/) reports that Rounds cut its dbt-driven BigQuery compute costs by 55% within 24 hours of going live, with dbt accounting for 60% to 70% of Rounds' BigQuery consumption. The same layer handles Snowflake warehouse right-sizing.
Will Yuki pass a security and procurement 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 do teams get started, and how disruptive is the setup?
There are no code changes: teams connect by swapping a connection string, with no query rewrites and no pipeline rewiring. Teams can start with the free cost analysis or the free AI workload assessment.
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