Does your BI tool read every row,
or just a sample?
Klar reads every row. Its dashboards, AI insights and data-quality checks are computed over 100% of the rows in a dataset — not a 50-row preview, not a proprietary attribution model, not a transformed copy. Where the data sits in a warehouse or database, the aggregation is pushed down and runs where the rows already live, so the numbers on your dashboard reconcile with what your finance team sees in Stripe.
Where your data actually lives
We'd rather tell you the truth up front than have you discover it later. Here's every path data takes in Klar — what we copy, what we don't, and why.
We push aggregations down via SQL. Your raw rows never leave your VPC. Only the chart's aggregated result travels — sums, counts, top-N selections, computed at the source.
SaaS APIs don't support real-time push-down — so we sync raw rows on the schedule you set (15 min to daily), store them encrypted in Klar's S3, and query them locally for chart performance. You can disconnect any source to permanently delete the sync.
There's no upstream source to query back to, so uploads live in Klar's encrypted storage until you delete the dataset. We don't enrich, transform, or model the rows — what you uploaded is what we query.
How Klar compares
Publicly verifiable claims only — every row below cites tier names + price pages as of June 2026.
Updated June 2026 · Spotted something out-of-date? support@aeye-on.com
| Klar | Triple Whale | Polar | Northbeam | Coupler + Power BI | Tableau | |
|---|---|---|---|---|---|---|
| Reads raw rows (not samples) | Yes — every row | Sampled / modeled | Warehouse copy | MTA model | Copy → BI tool | Warehouse copy |
| Warehouse data leaves your VPC | No — query in place | Yes | No (you BYO) | Yes | Yes | Yes |
| Proprietary attribution model | No — shows source ground truth | Yes (Triple Pixel) | No | Yes (MMM/MTA) | No | No |
| AI-generated dashboards bundled | Yes — from $29 | AI Assistant add-on | Limited | Enterprise only | No | Pulse add-on |
| Entry price (monthly) | $29 | $129 | ~$400 | $1,500 | $60 + BI cost | $70 + warehouse |
| INR billing + India region | Yes | No | No | No | No | No |
Honest tradeoffs
Full-fidelity isn't free. Here's what you give up.
When you swipe between dashboards we don't have a pre-built cube to read from — we hit your warehouse. We mitigate with aggressive query push-down and 5-minute result caching. For most DTC dashboards (sub-100M rows) the difference is imperceptible.
Shopify's REST API gives us 40 requests/sec; Stripe gives us 100. If your first historical sync is several years of orders, it can take 10-30 minutes. We checkpoint resume so a crash never restarts from zero, and incremental refreshes after that are 30s or less.
If you want a black-box MMM that tells you Meta drove $80k of last week's revenue with no math you can follow, that's Triple Whale or Northbeam. We show Stripe ground truth, Meta-reported, and GA4 last-click side by side, with the formulas exposed. Honest, but more work for you.
Frequently asked
Yes — and we say so above and in our Privacy Policy. SaaS APIs aren't built for push-down query, so we sync. The honest claim is full-fidelity (we read every row, not a sample), not zero-copy. If zero-copy is a hard requirement, use a Klar warehouse connection (Postgres/Snowflake/BigQuery/MySQL) instead.
Yes. Disconnect any SaaS source from /connections and we drop the underlying Parquet within minutes. There's no warehouse-tier extract we can't reach.
Triple Whale builds Triple Pixel — a proprietary tracking layer + MTA model on top of your data. Polar builds aggregates in Snowflake before charting. Both are useful if you want their opinion. Klar shows you what the source already says, without re-modeling.
Charts query 100% of the available rows (with push-down where possible). The 50-row preview banner you may see on the dataset detail page is for human-readable scrolling only; aggregates run against the full set.
It depends on which part of the tool you mean, and the distinction is worth being precise about. Enterprise platforms like Power BI and Tableau compute aggregates over your whole model — what they sample is the RENDERING of very dense visuals, which is a reasonable thing to do and not a fidelity problem. The sampling that actually misleads happens in automated profiling and AI features, where a tool inspects the first N rows (commonly 50 to 100) and describes your dataset from them. Klar profiles by pushing an aggregate query down to the source, so its column statistics, data-quality findings and AI insights are computed from every row rather than from a preview.
Included. Every plan carries a monthly allowance of AI credits covering dashboard generation, insights and profiling, starting at 100 credits on the $29 Starter plan. There is no separate AI SKU and no per-seat AI upcharge.
For estimating an average it often does not. It matters for the things a sample is structurally bad at finding: a null that appears in 0.5% of rows, a currency code that only shows up in one market, a duplicate key in a single batch, an outlier that is the whole story. Those are exactly the findings a data-quality check exists to surface, and a 50-row preview cannot see them. A representative sample answers 'roughly what does this look like'; it does not answer 'what is wrong with this'.
See your real numbers, not a model.
14-day trial. AI Credits included. No credit card.
Start free trial →