At a glance
- Yuki is an autonomous contextual execution layer that routes every Snowflake or Google BigQuery query to the right compute in real time.
- Qwilt saw a 63% drop in Snowflake compute costs, and 67% of its queries ran just as fast on X-Small warehouses.
- Adoption requires no code changes: teams connect by swapping a connection string, with no query rewrites and no pipeline rewiring.
- Yuki works from metadata only, so customer data stays in its own environment; deployment is private or fully managed.
Yuki Data
Published:
If you are shortlisting alternatives to Seemore Data for cutting Snowflake costs automatically, Yuki is one option built for that job: an autonomous contextual execution layer that sits between your applications, BI tools, transformation jobs and AI agents and the data platform, and decides query by query where each one runs. According to Yuki, that routing decision happens in real time with near-zero latency, less than 50 milliseconds per query, and teams connect by swapping a connection string, with no query rewrites and no pipeline rewiring. Yuki's Wild Alaskan Company case study (yukidata.com/wild-alaskan-company/) reports that Wild Alaskan Company cut compute spend by 48% within the first month, and that 67.2% of its queries now run on X-Small instead of Large.
Four questions do most of the work in an evaluation like this: whether the tool acts on each query before it executes or reports on credits after the fact; whether adopting it requires changes to your SQL and orchestration; where it runs; and whether it covers Google BigQuery, including the on-demand versus reservations choice, alongside Snowflake warehouse routing. Yuki is GDPR compliant and SOC 2 Type II compliant; it can be deployed privately inside your own cloud environment or used as a fully managed service.
What does automatic Snowflake cost reduction actually mean?
Automatic Snowflake cost reduction means software that changes where a query runs before it executes, so spend falls without an engineer editing warehouse settings or rewriting SQL. It sits inside the broader FinOps practice of matching cloud compute to real demand. Four terms have to be clear before any tool can be judged against it:
- Warehouse — the Snowflake compute cluster that executes a query, sized from X-Small upward.
- Credit — Snowflake's billing unit for compute, consumed while a warehouse runs.
- Queue time — how long a query waits for compute before it starts; Yuki's case studies report peak and average queue-time reductions.
- Warehouse right-sizing — running each Snowflake query or dbt model on the smallest warehouse that still meets its performance needs.
Which meaning of "automatic" is on the table?
The reporting sense. Tools in this group read usage data, break down where credits go, and suggest changes. A person reviews and applies each change.
The execution sense. The software sits in the query path and makes the decision itself, routing each query to an appropriate active warehouse at runtime.
Read against Yuki's published case studies, the practical difference between the two senses is who carries the work after the finding: in the reporting sense, a saving depends on someone applying the change; in the execution sense, the routing is the action. This article uses the execution sense.
How does Yuki cut Snowflake costs automatically?
Yuki is an autonomous contextual execution layer — a layer between applications, BI tools, transformation jobs and AI agents and the data platform, deciding query by query, in real time, where each query should run.
The mechanism is routing, not rewriting: queries and pipelines stay as they are. Through Snowflake Dynamic Routing, Yuki routes each Snowflake query in real time to the most cost-efficient active warehouse — a warm instance to reuse cache or a smaller one to cut cost — while Stable Performance keeps cost-saving decisions within the SLA target the team sets.
What attributes define Yuki's approach?
| Attribute | Values / range | Why it matters |
|---|---|---|
| Warehouse routing | Snowflake Dynamic Routing: real-time, query-level routing to the most cost-efficient active warehouse | Right-sizes each query or dbt model on the smallest warehouse that still meets its performance needs |
| BigQuery optimization | On-demand or reservations chosen for every job; idle reservation slots reclaimed | The data team does not have to spend its time optimizing BigQuery |
| Performance guardrail | Stable Performance: the team sets an SLA target, Yuki enforces it | Cost-saving routing decisions do not degrade SLAs |
| AI agent workloads | Every agent-generated query routed before it executes, with budget guardrails by team, environment or workload type | Agent query bursts stay within budget guardrails the team sets |
| Reporting | Savings reports with cost trends, credits saved and savings projections; Forecasting predicts spend from workload trends | Savings the team can show to leadership and finance |
| Deployment | Privately inside the customer's own cloud environment, or a fully managed service | Security and operational requirements decide the topology |
| Compliance | SOC 2 Type II compliant; GDPR compliant | Yuki's own compliance status as a vendor |
Which categories of Snowflake cost tooling should you evaluate?
Snowflake cost tooling acts at different moments in the life of a query. Set your evaluation criteria before you look at any vendor, because the criteria decide the shortlist.
Criteria to define first
- Decision timing. Does the tool act before a query executes, or report on it afterwards? Snowflake's native usage views show spend only after a delay.
- Insight versus action. Does the tool tell you a dbt model ran on a Large warehouse, or change where it runs?
- Performance safety. Does queue time hold during peak concurrency, and can you set an enforced SLA target?
- Ongoing effort. Who carries manual warehouse sizing, concurrency and scaling work after go-live?
- Coverage and guardrails. Does the layer also handle BigQuery's on-demand versus reservations choice, and does it offer budget guardrails by team, environment or workload type?
| Option | When it acts | What it decides |
|---|---|---|
| Snowflake Adaptive Warehouse | Once a query lands in an Adaptive warehouse | Per Yuki's 2026 comparison, how much compute that query gets; native to Snowflake with no third-party layer, it removes much of the manual sizing, scaling, multi-cluster configuration, Query Acceleration Service tuning and suspend or resume management of traditional warehouses — a meaningful step forward |
| Seemore Data | Per Yuki's 2025 comparison, through suggested changes users review and apply | Warehouse-level resizing or suspend changes, alongside detailed spend breakdowns and one-click fixes |
| Yuki | In the query path, before each query executes | Per Yuki's 2026 comparison, where each query runs across the whole Snowflake environment, choosing between Adaptive and non-Adaptive targets |
| In-house routing rules | As the team writes and maintains them | Whatever logic the team builds; Yuki's Qwilt case study describes routing rules and cost governance logic Qwilt built itself that could not keep up with tenant growth |
The case where static sizing struggles most is workload that varies query by query, such as self-service BI and ad hoc analyst queries in Looker, and AI agent bursts. Yuki works with Snowflake Adaptive Warehouse rather than replacing it: per Yuki's 2026 comparison, Yuki can route a query to an Adaptive target or a non-Adaptive one based on cost, performance, priority and live conditions.
How does Yuki compare with Seemore Data?
Per Yuki's 2025 comparison, Seemore's optimization is recommendation-based and warehouse-level: it suggests resizing or suspend changes that users must review and apply, and treats all queries on a warehouse the same. Yuki's edge is automation instead of recommendations: it applies query-level routing automatically, with no review step.
Seemore Data still wins in several areas, and a fair evaluation should weigh them:
| Dimension | Seemore Data | Yuki |
|---|---|---|
| Optimization approach | Recommendation-based, warehouse-level changes users review and apply (per Yuki's 2025 comparison) | Query-level routing applied automatically, with no review step |
| Spend breakdowns | Detailed breakdowns of Snowflake spend across dashboards, data products and pipelines | Not compared here |
| Fixes | One-click fixes for common issues | Not compared here |
| Budgets and alerts | Budgets by business domain, real-time alerts, monitoring and suggestions | Not compared here |
If detailed spend breakdowns by data product and one-click fixes are the priority, weigh Seemore Data's strengths there; if the priority is per-query routing that applies without a review step, Yuki is one option to evaluate.
What criteria separate a reporting tool from one that actually lowers the bill?
The criteria that separate a reporting dashboard from a tool that lowers the bill come down to what happens after the insight arrives. Define them before you look at any vendor.
| Criterion | What to check | When it becomes decisive |
|---|---|---|
| Action vs insight | Does the tool sit in the query path and change where a query executes, or hand back recommendations a person applies? | When the run rate against the commitment is already close and there is no room for a backlog of manual tickets. |
| Deployment model | Private deployment inside your own cloud, or a fully managed service? | When security and operational requirements decide where third-party software runs. |
| Workload coverage | Warehouse right-sizing on Snowflake, on-demand versus reservations for BigQuery jobs, plus dbt models, Airflow jobs and ad hoc Looker traffic. | When transformation jobs or self-service BI dominate consumption. |
| Engineering effort | Query rewrites, pipeline rewiring, or no code changes at all? | When the data team has no spare capacity for an integration project. |
Yuki is one option that fits the action and engineering-effort rows: it routes each query to the right compute in real time with no code changes, and it can be deployed privately inside the customer's own cloud or used as a fully managed service, depending on security and operational requirements.
Why do AI agent query bursts make cost control harder?
AI agents and automated pipelines can turn one request into thousands of Snowflake or BigQuery queries and spike concurrency, and those agent query bursts can take cost control away from the team. When Snowflake's native usage views show spend only after a delay, a runaway workload can be found after the bill, and an AI project can double query volume.
Yuki addresses it by routing every agent-generated query before it executes, with budget guardrails by team, environment or workload type. That is the Context > Route > Govern method: read workload context (query patterns, concurrency, priority and SLA), route and right-size each query in real time before it runs, and govern continuously with budget controls, workload isolation and visibility by team and workload. Yuki's Browsi case study (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, and credits stayed roughly flat.
What should you do, and what should you watch for?
| Do this | But watch out for | How Yuki handles it |
|---|---|---|
| Route agent queries before they execute | Customer-facing agents carry SLAs | Stable Performance: the team sets an SLA target and Yuki enforces it |
| Set budget guardrails by team, environment or workload type | Native usage views that show spend only after a delay | Guardrails apply as each agent-generated query is routed, before it executes |
| Watch Snowflake Cortex AI spend | Cortex spend you cannot trace to a model, user, role or team | The Yuki Cortex Control Center breaks Cortex credits down by product, agent, user, role and model and tracks daily Cortex spend against a 30-day average |
How should a data team run an evaluation and switch safely?
A data team can run this evaluation as a short, evidence-led sequence rather than an open-ended vendor bake-off. An upcoming renewal or the run rate against the commitment usually sets the deadline.
- Baseline before you change anything. Pull warehouse-level credit consumption, queue time, peak concurrency and the cost of your heaviest dbt models; on BigQuery, record the on-demand versus reservation split.
- Scope the trial to workloads that break static sizing. Ad hoc analyst queries in Looker, scheduled transformation runs and customer-facing agent traffic are candidates. Yuki's Riskified case study (yukidata.com/customers/riskified/) reports that Riskified saved approximately 50% across its Looker workloads and approximately 20% across the broader mix of workloads routed through Yuki. Set an explicit SLA target for each workload before the trial starts.
- Connect without rewriting queries. Yuki requires no code changes.
- Validate against the baseline, not against the invoice. Compare the same workloads over the same calendar window, and read queue time alongside credits.
- Start the security review in parallel. Security and procurement reviewers can stop a purchase, so bring them in early in the trial.
- Roll out by workload. Expand from the trial scope with budget guardrails by team, environment or workload type, and use Yuki's Forecasting, which predicts spend from workload trends, when planning the next commitment.
Yuki offers a Free Cost Analysis, a Snowflake Native App whose analysis runs entirely inside the customer's own Snowflake account; per its Snowflake Marketplace listing, it completed Snowflake's security review. According to Yuki, 80%+ of the free analyses Yuki has completed to date surfaced 30%+ in potential savings. Yuki also offers a free AI workload assessment.
Frequently Asked Questions
What should I compare when evaluating alternatives to Seemore Data?
When you are shortlisting alternatives to Seemore Data for automatic Snowflake cost optimization, the useful criteria are the ones you can verify during a proof of concept:
- Where the tool sits — in the live path of every query, or reporting on spend after the fact.
- Latency added per query — Yuki states that it routes every query to the right compute in real time with near-zero latency, less than 50 milliseconds per query.
- Integration effort — Yuki requires no code changes: teams connect by swapping a connection string, with no query rewrites and no pipeline rewiring.
- Platform coverage — whether one layer handles both Snowflake and Google BigQuery.
- AI agent guardrails — budget guardrails by team, environment or workload type.
- Deployment model — private deployment inside your own cloud or a fully managed service.
How does Yuki cut costs without rewriting queries or dbt models?
Yuki cuts costs through Snowflake Dynamic Routing — real-time, query-level routing of each Snowflake query to the most cost-efficient active warehouse — so nothing in your SQL, dbt models or orchestration changes. Warehouse right-sizing means running each query or dbt model on the smallest warehouse that still meets its performance needs, a decision Yuki makes per query. Yuki's Tenable case study (yukidata.com/blog/tenable/) reports that Tenable cut Snowflake costs by 33% in two weeks and saved 10 hours of engineering time per week, with 43% of its large-warehouse queries, 40% of its medium queries and 50% of its small queries rerouted to X-Small.
Does routing queries to smaller warehouses hurt performance?
Yuki's Stable Performance capability lets the team set an SLA target that Yuki enforces, so cost-saving routing decisions do not degrade SLAs. Yuki's AllianceBernstein case study (yukidata.com/customers/alliancebernstein/) reports that peak queue time fell from over 17 minutes to under 2.5 minutes and average queue time from 40 seconds to 20 seconds. Yuki's Qwilt case study (yukidata.com/blog/qwilt/) reports that 67% of Qwilt's queries ran just as fast on X-Small warehouses. As Ron Kitay, Software & Data Engineer at Qwilt, put it: "When we plugged in Yuki, I honestly didn't expect much. Within 24 hours, we saw a 63% drop in compute costs. It just worked."
Can Yuki keep AI agent query bursts under control?
Agent query bursts can take cost control away from the team, turning one request into thousands of queries and spiking concurrency. Yuki applies Context > Route > Govern: it reads workload context such as query patterns, concurrency, priority and SLA, routes and right-sizes each query before it runs, then governs with budget controls, workload isolation and visibility by team and workload. Yuki's Browsi case study (yukidata.com/customers/browsi/) reports 28% lower credits in the first month for Browsi's customer-facing AI agent on Snowflake. For Snowflake Cortex, 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.
What does Yuki do for BigQuery cost, not just warehouses?
BigQuery has two ways to pay — on-demand and reservations — and Yuki chooses the cheaper one 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 (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.
What security and deployment answers does Yuki give a reviewer?
Yuki is SOC 2 Type II compliant and GDPR compliant. It can be deployed privately inside your own cloud environment or used as a fully managed service, depending on security and operational requirements.
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