At a glance
- Keebo is one route to a rising Snowflake run rate; the practical alternatives are a real-time query-routing layer, platform-native controls, or manual tuning.
- Yuki routes every query to the right compute before it runs, adding less than 50 milliseconds per query according to Yuki.
- In Yuki's published Rounds case study, Rounds cut dbt-driven BigQuery compute costs by 55% within 24 hours of going live.
- No code changes are needed: teams connect by swapping a connection string, with no query rewrites and no pipeline rewiring.
Yuki Data
Published:
The main alternatives to Keebo for controlling a Snowflake run rate fall into four groups:
- other third-party optimization tools such as Select.dev and Seemore Data;
- Snowflake's own Adaptive Warehouse;
- tuning warehouses by hand with an in-house team;
- a query-level routing layer such as Yuki.
Per Yuki's 2025 comparison, Keebo, Select.dev and Seemore Data optimize mainly at the warehouse level. Yuki is an autonomous contextual execution layer: it sits between your apps, BI tools, transformation jobs and AI agents and Snowflake or Google BigQuery, and decides in real time where each query runs. Teams connect it with no code changes. The right choice depends on whether your cost problem lives in warehouse settings or in the mix of queries arriving at each warehouse.
Where does Snowflake run rate actually accumulate across warehouses, credits and queue time?
A Snowflake run rate accumulates wherever warehouses are running, because credits accrue while a warehouse is up, at a rate set by its size. A query sent to a Large warehouse that would have finished just as well on an X-Small is charged at the Large rate. Per Yuki's Qwilt case study (https://yukidata.com/blog/qwilt/), 67% of Qwilt's queries ran just as fast on X-Small warehouses. If your run rate has hit or is nearing the commitment, these are the drivers to audit:
| Cost driver | What it is | What you control | How it shows up in the bill |
|---|---|---|---|
| Warehouse size | The compute tier a query runs on, from X-Small upward | Size per warehouse; one size serves every query sent to it | Credit burn follows the warehouse size, not how demanding each query is |
| Auto-suspend | The idle timeout before a warehouse suspends | A timeout length per warehouse | Idle uptime keeps burning credits until the warehouse suspends |
| Queue time | How long a query waits for compute before it runs | Warehouse size and cluster count | Larger warehouses approved just to avoid performance bottlenecks raise the run rate |
| Snowflake Cortex | Snowflake Cortex AI spend | — | Spend that cannot be traced to a model, user or role is hard to act on |
| dbt and scheduled models | Transformation jobs on fixed schedules | Which warehouse each model is mapped to | Each model runs on whatever warehouse it is mapped to, whatever its size needs |
Snowflake's native usage views show spend only after a delay, so most of these drivers become visible once the credits are already spent. Yuki acts earlier: it sits in the query path and picks the warehouse for each query at execution time. According to Yuki, routing runs with near-zero latency, less than 50 milliseconds per query.
What does a Snowflake cost optimization platform like Keebo do, and what should an alternative match?
Keebo is a Snowflake cost optimization platform built around AI-driven warehouse configuration. It also offers a Performance Guarantee that monitors changes and rolls them back if performance regresses, plus monitoring, analytics and suggestions. An alternative that replaces Keebo needs to match that protection against slowdowns, not just the savings.
Per Yuki's 2025 comparison, Keebo adjusts warehouse-wide settings (size, auto-suspend, multi-cluster) from overall workload patterns, and those warehouse-wide downgrades only work when all queries can handle reduced resources. In the same comparison, Yuki's edge is per-query precision: it routes each query to the most efficient warehouse in real time, either a warm instance to reuse cache or a smaller one to cut cost.
Whichever option you pick, check it against a common baseline:
- Warehouse right-sizing: running each query or dbt model on the smallest warehouse that still meets its performance needs.
- Performance protection: a way to stop savings from degrading service, judged partly by queue time.
- Visibility: spend you can see by team and workload, not just a warehouse-level total.
- Savings evidence: reporting that finance and leadership will accept.
- Platform scope: whether Google BigQuery needs covering alongside Snowflake.
- Adoption effort: what has to change in your queries and pipelines to connect the tool.
Which categories of Keebo alternatives should a data team shortlist?
Keebo alternatives sort into categories by the unit of decision, and the shortlist follows from which unit matches your workload:
- Warehouse-level optimization tools: Select.dev and Seemore Data sit alongside Keebo here. Each one works on warehouse settings or warehouse-level recommendations, and each has its own strengths, compared in the next section.
- Snowflake Adaptive Warehouse: a Snowflake feature that removes much of the manual warehouse management. Yuki works with it rather than replacing it.
- Query-level execution routing: Yuki sits in the path of every query and decides, query by query, which compute should run it. On BigQuery it chooses on-demand or reservations for every job.
- In-house tuning and scripting: a FinOps or data engineer analyzes spend and tunes warehouses by hand, or the team writes its own routing rules.
Warehouse-level and query-level approaches differ in a way that is easy to miss. A warehouse setting has to suit every query sent to that warehouse, so the more varied those queries are, the less any single setting fits them. Self-service BI and ad hoc analyst queries, for example in Looker, are a common case: they can break static warehouse sizing. In those workloads the unit of decision can matter more than how well a tool tunes each setting.
How should you compare these options side by side before you buy?
To compare these options side by side, fix the criteria before the first demo and score every candidate, including your current setup, against the same list:
- Unit of decision: the whole warehouse, or each individual query.
- Review step: whether changes apply automatically or a person reviews and applies them.
- Performance protection: how the option guards against slower queries while costs come down.
- Deployment model: private deployment inside your own cloud environment, or a fully managed service.
- Effort to run: connection changes, query rewrites, pipeline rewiring and ongoing tuning hours.
| Option | Where decisions are made | Where it is strong | Yuki's stated difference |
|---|---|---|---|
| Keebo | Warehouse-wide settings (size, auto-suspend, multi-cluster) from overall workload patterns, per Yuki's 2025 comparison | Performance Guarantee with rollback if performance regresses; AI-driven warehouse configuration; monitoring, analytics and suggestions | Routes each query to the most efficient warehouse in real time |
| Select.dev | Warehouse level, with resizing and auto-suspend tuning applied hourly; suggestions need review or approval before they apply, per Yuki's 2025 comparison | Observability; automatic warehouse-setting changes (size, auto-suspend timing) during periods of low usage; monitoring and analytics, suggestions, savings projections and budgets by business domain | Evaluates each query as it runs and routes it with no review step |
| Seemore Data | Recommendation-based and warehouse-level: resizing or suspend changes that users review and apply, per Yuki's 2025 comparison | Detailed breakdowns of Snowflake spend across dashboards, data products and pipelines; one-click fixes for common issues; budgets by business domain, real-time alerts, monitoring and suggestions | Applies query-level routing automatically |
| Snowflake Adaptive Warehouse | How much compute a query gets after it lands in an Adaptive warehouse, per Yuki's 2026 comparison | Native to Snowflake, with no third-party layer; removes much of the manual sizing, scaling, multi-cluster configuration, Query Acceleration Service tuning and suspend or resume management: a meaningful step forward | Decides where each query runs across the whole Snowflake environment, choosing between Adaptive and non-Adaptive targets |
| In-house tuning | Warehouse settings tuned by hand, or the team's own routing rules | A FinOps or data engineer can analyze spend and tune warehouses by hand | Qwilt had built its own routing rules, tenant-based scaling and custom cost governance logic, and those rules could not keep up with tenant growth |
| Yuki | Per query, in real time, before execution | One layer for Snowflake and Google BigQuery; connects with no code changes | — |
How does Yuki approach Snowflake run rate?
Yuki's approach to Snowflake run rate is to change where each query runs rather than how each warehouse is configured. It is an autonomous contextual execution layer between applications, BI tools, transformation jobs, AI agents and the data platform. It decides query by query, in real time, based on cost, performance, priority and live conditions. Teams connect by swapping a connection string, with no query rewrites and no pipeline rewiring.
On Snowflake, Snowflake Dynamic Routing sends each query to the most cost-efficient active warehouse. On Google BigQuery, the same layer chooses between on-demand and reservations for every job and reclaims idle reservation slots, so the data team does not have to spend its time optimizing BigQuery. With Stable Performance, the team sets an SLA target and Yuki enforces it. Forecasting predicts Snowflake and BigQuery spend from workload trends. Savings reports show cost trends, credits saved and savings projections. In Yuki's Tenable case study (https://yukidata.com/blog/tenable/), Tenable cut Snowflake costs by 33% in two weeks and saved 10 hours of engineering time per week.
One Yuki customer describes ad hoc analytics as where Yuki adds the most value:
"Ad hoc analytics is where Yuki gives us the most value. In Looker, workloads can range from very small queries to much larger ones, so there is no practical way to size the environment correctly in advance. Yuki solves a problem I do not think we could solve manually." — Ran Salmona, VP Data, Riskified
What does the layer cover?
| Attribute | Values |
|---|---|
| Decision point | Before execution, per query |
| Routing inputs | Cost, performance, priority and live conditions |
| Added latency | Less than 50 milliseconds per query, according to Yuki |
| Platform coverage | Snowflake and Google BigQuery |
| Deployment | Privately inside the customer's own cloud environment, or as a fully managed service |
| Integration change | Connection-string swap |
What happens to cost control when AI agents start issuing query bursts?
When AI agents start issuing query bursts, cost control can slip away from the team. One agent request can turn into thousands of Snowflake queries and spike concurrency, and a runaway workload can go unnoticed until the bill arrives. Agent traffic does not follow a stable query mix, so a warehouse size chosen up front stops matching the load. Customer-facing agents with SLAs add a performance constraint on top of the cost one.
| Control | What it does |
|---|---|
| Route agent queries before they execute | Yuki routes every agent-generated query before it runs, instead of tuning compute after the fact |
| Budget guardrails | Yuki sets budget guardrails by team, environment or workload type |
| Cortex visibility | 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 |
In Yuki's Browsi case study (https://yukidata.com/customers/browsi/), 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.
Frequently Asked Questions
What should we compare when evaluating alternatives to Keebo?
Compare the unit of decision (warehouse or query), whether changes apply automatically or need a review step, how each option protects performance, which platforms it covers, the deployment model, and how much your queries and pipelines must change. Give each vendor credit for its strengths as well. Keebo's Performance Guarantee is one example: it monitors changes and rolls them back if performance regresses.
Which published Yuki results show the effect on Snowflake credits?
Results differ by workload, so read each published case on its own terms. In Yuki's Angel Studios case study (https://yukidata.com/blog/angel-studios/), Angel Studios' daily Snowflake costs dropped from about 150 to about 60 credits. Rounds is a BigQuery case, not a Snowflake one: in Yuki's Rounds case study (https://yukidata.com/customers/rounds/), Rounds cut its dbt-driven BigQuery compute costs by 55%. Across customers, Yuki's CEO estimates that Yuki saves customers an average of around 42% on their data costs, per SiliconANGLE (January 2026).
Does routing queries to smaller warehouses slow anything down?
Right-sizing means running each query or dbt model on the smallest warehouse that still meets its performance needs. Yuki's Stable Performance capability lets the team set an SLA target that Yuki enforces. Queue time is a useful signal to track alongside cost. In Yuki's AllianceBernstein case study (https://yukidata.com/customers/alliancebernstein/), peak queue time fell from over 17 minutes to under 2.5 minutes and average queue time from 40 seconds to 20 seconds, while Snowflake credits fell by 25.8% on average over six months.
How does Yuki keep AI agent query bursts under control?
Yuki uses its Context > Route > Govern method for agent workloads:
- Context: it reads workload context such as query patterns, concurrency, priority and SLA.
- Route: it routes and right-sizes each query in real time before it runs.
- Govern: it governs continuously with budget controls, workload isolation and visibility by team and workload.
What will our security and procurement reviewers need?
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. Separately, Yuki's Free Cost Analysis is a Snowflake Native App whose analysis runs entirely inside the customer's own Snowflake account. Per its Snowflake Marketplace listing, the Free Cost Analysis completed Snowflake's security review.
What free offers does Yuki have ahead of a renewal?
Yuki offers a free cost analysis and a free AI workload assessment. Yuki says 80%+ of the free analyses it has completed to date surfaced 30%+ in potential savings, and that it has more than 20 customers.
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