Seemore vs SELECT: what is the difference?

Short answer: SELECT, acquired by DoiT in January 2026, is a cost intelligence product for Snowflake and Databricks, with BigQuery in private preview.[X1][X4] It gives data teams cost attribution, lineage, recommendations for queries and warehouse sizing, and an Automated Savings feature.[X2][X6] On Snowflake, Automated Savings makes short-lived configuration changes, and SELECT’s documentation says it does not resize warehouses or reduce the maximum cluster count.[X3] Seemore Data is built for Snowflake and does act on size: it plans each warehouse’s size, and in some cases its generation (Gen1 or Gen2), hour by hour, scales up through load spikes, suspends idle warehouses and scales idle clusters back in, and connects each cost to column-level lineage.[S1][S2][S3][S4][S6] Choose SELECT for cost visibility across several data platforms, with Snowflake automation that stays clear of warehouse sizing. Choose Seemore if you own a large Snowflake bill and want oversizing handled through a schedule you approve, rather than left as a recommendation, with each cost tied to the jobs and dashboards behind it.

Last reviewed: 14 October 2026. Details about SELECT come from SELECT’s own pages and documentation, and from DoiT’s press release, listed with their dates in the sources at the end. This market changes fast, so check SELECT’s site before you decide.

At a glance

  Seemore SELECT
Platforms Snowflake; Databricks in Early Access[S10] Snowflake; Databricks (generally available since June 2026); BigQuery (private preview since 20 July 2026)[X4]
Cost visibility Cost by domain, user, warehouse and job; most expensive data products[S6] Cost attribution by workload, including dbt tags and resource names; custom dashboards[X2][X9]
Lineage Down to column level, with cost, frequency and duration on every node[S6][S8] Table-to-table lineage with workload cost on the connections; Tableau assets can appear in lineage (beta)[X7][X8]
Recommendations Prioritized by effort and savings[S6] Query optimization and warehouse sizing recommendations[X2]
Automatic warehouse changes on Snowflake An hourly size plan and, in some cases, generation (Gen1 or Gen2) that you approve; extra capacity during load spikes; suspends idle warehouses; scales idle clusters back in; timed manual changes that revert on their own[S1][S2][S3][S4][S5] Automated Savings makes short-lived configuration changes; SELECT says it does not resize warehouses or reduce the maximum cluster count. On Databricks it adjusts worker counts and auto-scaling[X3][X4]
Control over automation You approve each SmartPulse schedule; optional performance ceiling; pause, revert or edit a schedule[S1] Automated Savings can be switched on or off per warehouse, and the actions it takes are visible[X6]
Integrations dbt, Airflow, Tableau, Looker and Power BI, among others[S7] dbt, Fivetran, Looker, Tableau (beta), Mode, Hex, Periscope and Opsgenie on its integrations page; Sigma per its changelog[X8][X11]
SOC 2 Type II Yes[S9] Yes[X10]

Where the two overlap

Both can attribute cost to dbt workloads, and both now change warehouse settings automatically.[X2][S8] If you have read older comparisons that call SELECT “visibility only”, they are out of date: SELECT’s Automated Savings feature makes changes to your warehouses.[X3][X6]

So the useful question is not “observability or automation”. It is what each tool is willing to change, and how much context it uses to decide.

The main difference: who sizes the warehouse

On Snowflake, SELECT keeps sizing as a recommendation. SELECT describes Automated Savings as monitoring warehouses in real time and making short-lived configuration changes. Its documentation says the feature does not take actions that can affect query performance, such as resizing the warehouse or reducing the maximum cluster count. Warehouse sizing appears in SELECT as a recommendation for someone to act on.[X2][X3] On Databricks, by contrast, SELECT adjusts worker counts and auto-scaling.[X4]

Seemore automates sizing, with you approving the plan. That is the core of what it does:

  • SmartPulse learns each warehouse’s usage pattern and turns it into an hourly schedule for size and, in some cases, generation (Gen1 or Gen2). You approve every schedule before it goes live, choose Cost or Performance mode per warehouse, and can set a performance ceiling it will never recommend crossing. It is built for warehouses with a predictable usage pattern.[S1]
  • Burst Protector runs on every SmartPulse-managed warehouse by default. When queries keep queueing beyond what the schedule can absorb, it scales the warehouse up, then back down once the spike passes. In Cost mode it never goes beyond the warehouse’s original configuration.[S2]
  • Auto-Shutdown suspends a warehouse the moment it stops working, instead of waiting out a fixed timeout, and never suspends one with a running or queued query.[S3]
  • Auto-Scaler scales multi-cluster warehouses back in once the extra clusters stop contributing, within the minimum and maximum you configured.[S4]
  • Live Tuning lets you make a temporary manual change (size, type or cluster count) that reverts on its own at the time you set.[S5]

Neither approach is wrong. On Snowflake, SELECT’s is the more conservative: automation that does not touch warehouse size. Seemore’s goes after oversizing directly, with approval and performance limits around it.

Lineage and the stack around Snowflake

  • Seemore’s lineage goes down to column level, from ingestion through orchestration and dbt to the dashboard, with cost, frequency and duration attached to each node.[S6][S8] SELECT’s lineage view maps table-to-table relationships, with workloads shown on the connections between tables.[X7]
  • The integration lists differ. Seemore’s integrations page lists dbt, Airflow, Tableau, Looker and Power BI, among others.[S7] SELECT’s integrations page lists dbt, Fivetran, Looker, Tableau (in beta), Mode, Hex, Periscope and Opsgenie, and its changelog also announces a Sigma integration.[X8][X11] Check each vendor’s current list for the tools you use.

Platforms

SELECT covers more platforms. Databricks has been generally available since June 2026, and BigQuery has been in private preview since 20 July 2026.[X4] SELECT is available as a standalone product or as part of DoiT Cloud Intelligence, DoiT’s FinOps and CloudOps platform.[X5] If your data spend is split across Snowflake and other platforms, SELECT covers them in one place. Seemore is built for Snowflake, and its Databricks support is in Early Access.[S10]

When SELECT is the better fit

  • You run Snowflake alongside Databricks or BigQuery and want one cost tool for all of them.
  • Your data engineers want to dig into cost themselves, with dashboards they build.
  • On Snowflake, you want automated savings that do not resize warehouses, switched on warehouse by warehouse.
  • You already work with DoiT for cloud cost management.

When Seemore is the better fit

  • You own a large Snowflake bill and want oversizing handled automatically, not left as a recommendation for your engineers to work through.
  • You want automation you can see and govern: hourly plans you approve, performance limits and a record of every change.
  • You need cost traced through column-level lineage to the job, dbt model or dashboard that caused it.
  • Snowflake is your main data platform, and depth there matters more than breadth across platforms.

Is a Snowflake renewal coming up, or did the last bill surprise you? Book a demo to see how Seemore sizes, suspends and scales warehouses for you.

Comparing other options? Read Seemore vs Keebo.

Frequently asked questions

Is SELECT still independent?

No. DoiT acquired SELECT, as SELECT announced on 7 January 2026. SELECT said it would continue to be offered as an independent product, and in June 2026 DoiT described it as available as a standalone product or as part of Cloud Intelligence.[X1][X5]

Does SELECT automate Snowflake optimization?

Yes, within limits. On Snowflake, its Automated Savings feature makes short-lived configuration changes and can be switched on or off per warehouse. SELECT’s documentation says it does not take actions that can affect query performance, such as resizing the warehouse or reducing the maximum cluster count, and warehouse sizing is offered as a recommendation. On Databricks, it adjusts worker counts and auto-scaling. Checked 14 October 2026.[X2][X3][X4][X6]

Does SELECT have column-level lineage?

SELECT’s lineage view maps table-to-table relationships, with workloads shown on the connections between tables, and Tableau assets can appear in lineage during its beta. We found no statement on SELECT’s lineage pages that it offers column-level lineage for Snowflake. Seemore’s lineage goes down to column level. Checked 14 October 2026.[X7][X8][S6][S8]

Does Seemore support Databricks?

Seemore is built for Snowflake, and Databricks support is in Early Access. See Seemore for Databricks.[S10]

Sources and dates

Every statement about SELECT comes from SELECT’s own pages or from DoiT’s press release, checked on 14 October 2026 unless a publication date is shown. Every statement about Seemore comes from seemoredata.io as published on the same day.