FAQ
This FAQ covers the most common questions about Seemore Data—what it does, how it saves money and improves performance, and how it fits into a modern data stack. It also explains automation, governanc
1. The Essentials: What We Do & Why It Matters
Q: What exactly is Seemore Data? A: Seemore Data is turning fragmented data infrastructure into a single, self-optimizing fabric through its Autonomous Context-Aware Data Engineer Agent. The platform continuously analyzes and optimizes cost, performance, and usage across the modern data cloud, giving data teams an “always-on” virtual engineer that makes smart decisions at machine speed.
Q: How is this different from Snowflake’s native cost dashboards? A: Snowflake’s native cost dashboards tell you what you spent inside Snowflake. Seemore tells you why—and what to do about it. Snowflake shows credits by warehouse and query; Seemore connects that spend end-to-end to pipelines, dbt models, dashboards, teams, and actual business usage. More importantly, Snowflake dashboards are passive and reactive. Seemore is active: it detects waste and anomalies in real time, explains the root cause with lineage and usage context, and recommends—or executes—optimizations safely. Think of Snowflake’s dashboards as a receipt; Seemore is the autonomous engineer preventing the waste in the first place.
Q: What's in it for me? A: You get full control of your data costs, performance, and usage—without babysitting the stack.
End-to-End Context: We connect Snowflake costs to upstream tools (Fivetran, dbt) and downstream BI (Tableau, Looker). We don’t just show an expensive query—we show the dashboard triggering it, the pipeline feeding it, and the team owning it.
True Observability: Costs, usage, and performance are correlated across the entire data stack, so you can understand impact, ownership, and waste—not just raw credits.
Actionable Automation:
Smart Pulse: Dynamically resizes warehouses hourly and transitions workloads to Gen2 based on real usage patterns.
Auto-Shutdown: Actively suspends idle compute beyond Snowflake’s native auto-suspend to eliminate wasted minutes.
AI-Powered Auto Clustering: Recommends optimal clustering keys based on real workload and query patterns.
Auto Scaler: Adjusts warehouse size and concurrency to balance cost and performance in real time.
Query Optimization: Delivers context-aware recommendations grounded in full lineage and real workload impact.
Anomaly Detection + AI RCA: When a spike happens, our AI agent immediately explains why (e.g., a dbt model frequency change), saving hours of investigation and freeing engineers to focus on innovation instead of firefighting.
Q: What's your biggest differentiator? A: Seemore’s biggest differentiator is its ability to correlate cost, usage, performance, and lineage end to end, then act on that context automatically. Most tools either show metrics or tune one layer in isolation. Seemore understands why something is expensive, who is responsible, and what will break if you change it—before making or recommending changes. That’s what turns optimization from one-off cleanups into a safe, continuous, system-level process.
Q: Who inside the organization typically uses Seemore? A: Seemore is built for cross-functional data teams, each getting value from the same source of truth.
CDOs & Heads of Data use it to tie spend to business value and defend budgets with confidence.
Data & Analytics Engineers use it to understand lineage, eliminate waste, and optimize safely without firefighting.
FinOps & Finance teams use it to gain real-time cost attribution, anomaly prevention, and predictable spend.
BI & Analytics leaders use it to align dashboard usage, refresh behavior, and licensing with actual demand. Everyone sees the same reality—just through the lens that matters to them.
2. Capabilities: Savings & Optimization
Q: How do you actually save us money? A: Seemore saves money through a tight ASK → TASK loop that runs continuously.
ASK (Observability): We give you full, end-to-end visibility across Snowflake, pipelines, and BI—down to the exact query, table, dashboard, and owner. That lets us surface real waste (unused pipelines, over-refreshing dashboards, idle compute) and catch anomalies early, with clear root cause instead of noise.
TASK (Automation): Once waste is identified, Seemore doesn’t stop at insight. We actively optimize warehouse configuration in real time—right-sizing, auto-suspending, and adapting to workload patterns without breaking performance. Query optimization is on the roadmap, following the same principle: only automate once we have full context and safety.
Observability tells you where money is leaking. Automation makes sure it actually stops leaking, continuously—not just during cleanup sprints.
Q: Where do the savings actually come from? A: Seemore drives savings by attacking inefficiency across three concrete areas of your Snowflake environment.
Waste Reduction (Unused Assets): We identify zombie assets—unused or orphan tables, bloated storage, inactive users, unused dashboards, and pipelines that keep ingesting or refreshing data no one consumes. Each asset is tied to an exact cost, so you know precisely what you save by slowing it down, resizing it, or shutting it off.
Compute Optimization: We continuously analyze warehouse behavior and adapt compute to real demand—not peak assumptions.
Smart Pulse: Automatically adjusts vertical size (e.g., XL → L) and warehouse generation (Gen1 ↔ Gen2) based on real workload patterns, re-evaluated hourly.
Auto Shutdown: Actively suspends idle warehouses beyond native auto-suspend settings to eliminate wasted minutes.
Auto Scaler: Tunes horizontal scaling and concurrency to avoid over-provisioning while protecting performance.
Service Optimization (Cost vs. Value): We monitor expensive Snowflake services like Auto-Clustering and Search Optimization and score them based on whether they actually improve performance, using health scores like pruning efficiency and cluster effectiveness.
Q: Can you optimize for performance instead of just cost? A: Seemore can optimize for performance first, cost second—or balance both—based on your goals. We analyze real workload behavior across queries, pipelines, and warehouses, then tune compute, scaling, and services to remove bottlenecks without brute-force oversizing.
Q: How does Seemore work with Snowflake Cortex and AI workloads? A: Seemore brings visibility and control to Cortex and AI-driven workloads the same way it does to the rest of your data stack. We track Cortex queries, services, and token-driven workloads, attribute their cost to the owning team, query, or downstream asset, and surface anomalies when AI usage spikes unexpectedly.
3. Automation & Intelligence
Q: What about Gen 1 vs. Gen 2 Warehouses? A: We studied Gen1 vs. Gen2 in depth and built a dedicated algorithmic solution called Smart Pulse. We identified exactly when Gen2 improves performance and can even lower cost—and when it simply adds overhead. Seemore analyzes your query patterns—from heavy scans and merges to simple selects—and determines which workloads truly benefit from Gen2 and which should stay on Gen1. Based on that insight, Seemore automatically selects and combines warehouse size + generation, re-evaluated hourly, using both historical and live usage patterns.
We provide specific insights on when to migrate. We analyze your query patterns (e.g., heavy scanning/merging vs. simple selects) and tell you exactly which warehouses will benefit from Gen 2 performance improvements and where it might just increase costs without benefit.
>> To learn more about Gen 2 and the algorithmic way to decide if Gen 2 actually saves you money.
Q: What does Smart Pulse actually do? A: Smart Pulse continuously analyzes how each warehouse is actually used and reconfigures it hourly to match real demand, not peak guesses. It automatically adjusts vertical size, warehouse generation (Gen1 vs. Gen2), and scaling behavior, and knows how to combine them for maximum efficiency. The result is sustained performance with no wasted compute and no manual tuning.
Q: What kind of insights do you surface? A: SSeemore surface insights across cost, usage, and performance—always with context. That includes waste detection (unused tables, idle warehouses, over-refreshing dashboards, inactive BI users), anomaly explanations (what spiked, who triggered it, and why), and optimization opportunities tied to real savings or performance impact.
Q: How does anomaly detection work? A: Seemore continuously learns baseline patterns across cost, usage, and performance for your warehouses, pipelines, queries, and dashboards. When something deviates—like a sudden cost spike or runtime jump—we don’t just flag it; we trace it end to end to the exact query, job, dashboard, or configuration change that caused it.
Each anomaly comes with context, ownership, and impact, so teams know whether it’s real risk or expected behavior. The goal isn’t alerts—it’s fast understanding and prevention of repeat incidents.
We also highlight service efficiency (e.g., whether Auto-Clustering or Search Optimization is actually helping), pipeline ROI end to end, and risk-aware impact analysis so you know what breaks if you change something.
Every insight is prioritized, quantified, and connected to a clear next action—not just another alert.
Q: Who controls what gets automated vs. manual? A: You do. Seemore is designed with explicit governance controls so teams decide where automation is allowed and where human approval is required. You can run Seemore in insight-only mode, recommendation mode, or fully automated mode—globally or per capability (e.g., warehouse resizing on, service changes off).
This lets data, platform, and FinOps teams move fast where it’s safe, and stay cautious where governance or org policy demands it.
Q: How do you make sure optimizations don’t break production? A: Seemore never optimizes blindly. Every action is evaluated with full lineage and impact context, so we understand which pipelines, dashboards, and teams would be affected before any change happens.
Optimizations are constrained by guardrails - including workload criticality, historical performance patterns, and safe operating ranges—and are continuously re-evaluated hourly. The result is controlled, reversible optimization that protects SLOs and production stability, not risky trial-and-error.
4. Integrations & Technical Stuff
Q: Do you support tools other than Snowflake? A: Yes. While our core optimization engine focuses on Snowflake compute and storage, we provide end-to-end visibility across your stack. We have native integrations with:
Ingestion: Fivetran, Rivery
Orchestration/Transformation: dbt, Airflow
Warehouse: Snowflake (Databricks on roadmap)
BI: Tableau, PowerBI, Looker
Q: How do you build lineage across all these tools? A: Seemore builds lineage by reading Snowflake query history and connecting to orchestration tools to extract the actual SQL being executed and map real dependencies. We then layer on metadata from transformation tools, ingestion systems, and BI to connect pipelines, models, tables, and dashboards into a single end-to-end graph.
Because this is based on what actually runs, not static configs or manual tagging, lineage stays accurate as your stack evolves—down to table and column level.
Q: Can I see the cost of an entire pipeline end-to-end? A: Yes. Seemore shows the full cost of a pipeline from ingestion to dashboard—including ETL, transformations, Snowflake compute, services, and BI refreshes. We don’t just total credits; we attribute cost across every step, so you see what runs, how often, and who or what consumes it.
That means you can finally answer: what does this pipeline cost us, who owns it, and what breaks or saves money if we change it.
Q: How granular can I get? A: As deep as you want. Start with high-level KPIs, drill into specifics, and go all the way down query-, table-, column-, user-, team-, dashboard-, and pipeline-level granularity. Seemore doesn’t just show where spend happens—it shows who triggered it, why it ran, and what business asset consumed it (e.g., the exact Looker dashboard behind a Snowflake spike). That granularity spans end to end: ingestion (Fivetran), transformations (dbt), orchestration (Airflow), warehouse (Snowflake), and BI. The edge: every data point is tied to lineage + cost + usage, so you can confidently decide what to optimize, slow down, resize, or shut off—without guessing or breaking prod.
Q: Can you help me cut BI licensing costs? A: YYes. Seemore connects BI tools to actual user activity and downstream data usage, so you can see who’s actively using dashboards—and who isn’t. We identify inactive users, rarely viewed dashboards, and over-refreshing reports that quietly justify unused licenses.
That gives you the confidence to reclaim licenses, consolidate access, or adjust refresh behavior—cutting BI costs without disrupting teams that actually rely on the data.
5. Security & Architecture
Q: Do you access our actual data (PII)? A: Absolutely not. We never touch your table data. We only fetch metadata and query history. We analyze how your data is processed, not what the data is. We are SOC 2 Type II compliant and work with large financial and healthcare enterprises that require rigorous security standards.
Q: Does Seemore Data impact our Snowflake performance? A: No. We fetch the metadata and query history to our environment and do all the heavy lifting (algorithms and analysis) on our side. The compute footprint on your environment is negligible—typically costing around $1 to $2 per day.
Q: Where is the data hosted? A: We are hosted on AWS (typically US or Frankfurt regions), but we support connecting to your Snowflake account regardless of which cloud provider you use (AWS, Azure, GCP).
6. Onboarding & ROI
Q: Can I try it first? A: Absolutely. You can start with a risk-free trial using read-only access, so nothing changes in your environment. Within days, Seemore analyzes your real workloads and shows where money is being wasted and what can be optimized, with clear, quantified impact. You’ll see value before you decide—no blind trust, no long setup, no pressure.
Q: How long does onboarding take? A: 15 to 20 minutes. You simply create a user for us in Snowflake with specific metadata permissions and connect your other tools (dbt, Tableau, etc.). We handle the rest. Within 48–72 hours, we populate the dashboard with historical data analysis and actionable insights — waste, anomalies, and optimization opportunities—without requiring changes to your pipelines. Automation can be enabled gradually, so teams see value fast without risking stability or slowing delivery.
Q: What is the typical ROI? A: Most customers see 20–50% savings, with some reaching 70%. Savings compound as Seemore continuously re-optimizes.
7. Comparison to Other Tools
Q: How are you different from Select.dev? A: Select.dev focuses primarily on Snowflake-level cost observability —warehouses, queries, and usage inside the warehouse. Seemore goes broader and deeper by correlating cost, usage, and lineage end to end: ingestion, dbt models, orchestration, Snowflake, and BI dashboards.
That context lets Seemore answer not just what is expensive, but why it exists, who triggered it, and what breaks if you change it. On top of that, Seemore doesn’t stop at visibility—we actively optimize compute (Smart Pulse, Gen1/Gen2, scaling, shutdowns) and prevent repeat waste through continuous automation.
Bottom line: Select helps you observe Snowflake spend. Seemore helps you run your data stack efficiently, safely, and continuously.
Q: How are you different from Keebo.ai? A: Keebo focuses primarily on autonomous warehouse and query-level optimization inside Snowflake. Seemore goes broader by correlating cost, usage, performance, and lineage end to end—from ingestion and dbt models to BI dashboards.
That context lets Seemore answer not just how to optimize, but whether something should run at all, who owns the cost, and what breaks if you change it. On top of that, Seemore combines observability + automation: we explain the “why,” prevent repeat waste, and apply changes safely with full impact analysis.
Bottom line: Keebo makes Snowflake faster. Seemore makes your entire data stack efficient, accountable, and sustainable.
Q: How are you different from espresso.ai? A: Espresso.ai focuses on warehouse-level cost reduction, often optimizing Snowflake in isolation. Seemore takes a different approach: we correlate cost, usage, performance, and lineage across the entire data stack - from ingestion and dbt models to BI dashboards.
That context lets us decide what should run, how often, and at what size, not just how small a warehouse can get. We combine observability with automation, apply changes hourly and safely, and explain why something is expensive before acting.
Bottom line: Espresso cuts fast. Seemore cuts smart—and keeps performance and teams safe.
Q: How are you different from Monte Carlo? A: Monte Carlo is a data reliability platform focused on freshness, volume, and schema issues—great for catching broken data.
Seemore solves a different problem: runaway cost, inefficiency, and performance waste across the data stack. Instead of asking “is the data correct?”, Seemore asks “is this worth running at all, how much does it cost, and who is using it?” We correlate lineage, usage, and cost end to end, then take action by optimizing compute and preventing waste—not just alerting on incidents. They ensure data trust. We ensure data efficiency and ROI.
Q: How are you different from Datadog? A: DataDog monitors Snowflake. Seemore Data optimizes it. Datadog is a broad infrastructure monitoring platform that can observe Snowflake metrics. Seemore Data is a specialized Snowflake optimization platform that actively reduces data warehouse costs and improves performance through autonomous AI-driven actions.
DataDog shows you what happened. Seemore shows you why, how much it costs, and fixes it automatically - cutting Snowflake spend 30–70% within weeks.
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