Monte Carlo vs Bigeye vs Soda: Real Data Observability Cost per 1,000 Tables in 2026
The short answer: the vendor invoice is only part of the bill, and sometimes the smaller part. Soda is the only one of the three with a public per-asset price: $8 per dataset per month, or $96,000 a year per 1,000 monitored tables on the Team plan. Monte Carlo and Bigeye are quote-only, with a Monte Carlo procurement median of $53,375 a year and a Bigeye marketplace reference of $50,000 for 200 tables ($250 per table). Separately, checks that run queries consume warehouse compute: under the ILLUSTRATIVE assumptions below, three daily query checks per table add about $18,000 a year per 1,000 tables, and the same checks run hourly add about $432,000. Cost per 1,000 tables is therefore two numbers, the platform fee and the compute the monitoring triggers.
What each vendor bills
Soda (OFFICIAL, Soda pricing page). Free for up to 3 production datasets; Team at $8 per dataset per month (billed annually), pay as you go for the datasets you test and monitor, with unlimited users and all integrations; Enterprise at custom pricing with annual billing and volume discounts. A fair-use policy applies to the definition of a dataset. Other listings show an older Team structure of $750 a month including 20 datasets and $8 for each additional dataset (REPORTED); the two structures disagree, and the current official rate is used here.
Monte Carlo (REPORTED). No public price list; plans are Start (one team), Scale (all teams and domains) and Enterprise, with metering described as per monitor or per table on different versions of its pricing pages. Vendr reported a median of $53,375 a year across 51 purchases (range $15,000–$115,000, average savings 18%); an earlier Vendr snapshot showed an average of $48,056 across 26 deals with a range of $7,500–$120,000. Other commentary put enterprise pricing at $50,000–$300,000 or more. A buyer reported being pushed to move to a new pricing model at renewal and negotiating the discount from 9% to 22%. Pricing for users, lineage and AI root-cause analysis add-ons was not publicly listed (QUOTE-ONLY / UNKNOWN).
Bigeye (REPORTED). No pricing page. A marketplace listing was reported at $50,000 for 200 tables (a ratio of $250 per table), and commentary places deployments in five-figure to low six-figure annual ranges, with drivers of monitored tables or data volume, connector count, seats, modules and term. Module pricing (lineage, sensitivity scanning, governance and AI) was not publicly listed (QUOTE-ONLY / UNKNOWN).
Platform fee by asset count
Formula: assets × per-asset rate × 12. Soda's Team plan is applied to all assets, which is conservative because the first three datasets are free on the Free plan.
| Monitored tables | Soda Team ($8) | Soda older structure ($750 incl. 20 + $8) | Monte Carlo | Bigeye |
|---|---|---|---|---|
| 100 | $9,600 | $16,680 | QUOTE-ONLY / UNKNOWN | QUOTE-ONLY / UNKNOWN |
| 1,000 | $96,000 | $103,080 | QUOTE-ONLY / UNKNOWN | QUOTE-ONLY / UNKNOWN |
| 10,000 | $960,000 (volume discounts available on Enterprise) | $967,080 | QUOTE-ONLY / UNKNOWN | QUOTE-ONLY / UNKNOWN |
For the quote-only vendors, an implied per-table cost is a reference calculation, not a price. If the Monte Carlo median of $53,375 covered 100, 1,000 or 10,000 tables, it would be $533.75, $53.38 or $5.34 per table per year. If Bigeye's $250 per table ratio held at 1,000 tables, the result would be $250,000, but volume discounts would apply, so treat it as a ceiling reference. Per 1,000 monitored assets, only Soda's $96,000 is a listed rate.
Warehouse compute triggered by monitoring
This is not on the vendor's invoice. It appears on your warehouse bill. The assumptions are ILLUSTRATIVE and should be replaced with your own numbers: three query-based monitors per table, each running 30 seconds on an extra-small warehouse (one credit per hour) at $2 per credit. Metadata-only checks are assumed to cost nothing.
Formula: tables × monitors × runs per month × seconds ÷ 3,600 × $2 × 12.
| Tables | Daily checks (30 runs a month) | Hourly checks (720 runs a month) |
|---|---|---|
| 100 | $1,800 a year | $43,200 a year |
| 1,000 | $18,000 a year | $432,000 a year |
| 10,000 | $180,000 a year | $4.32 million a year |
Per table, that is $18 a year daily or $432 hourly, against Soda's $96 a year platform fee. At hourly frequency the compute exceeds the vendor fee by a factor of 4.5. Scan size matters as much as frequency: a check that scans a full table costs far more than one that reads a partition, and a metadata-driven freshness or volume check may cost almost nothing.
Total cost per 1,000 tables
| Case | Platform fee | Daily-check compute | Total (daily) | Hourly-check compute | Total (hourly) |
|---|---|---|---|---|---|
| Soda Team | $96,000 | $18,000 | $114,000 | $432,000 | $528,000 |
| Monte Carlo at the reported median | $53,375 | $18,000 | $71,375 | $432,000 | $485,375 |
| Bigeye at the marketplace ratio | $250,000 | $18,000 | $268,000 | $432,000 | $682,000 |
Break-even: data-engineer hours saved per incident
Formula: hours saved per incident needed = annual cost ÷ (incidents per year × hourly cost). With 240 incidents a year per 1,000 tables (20 a month) and a loaded $90 an hour (both ILLUSTRATIVE):
| Case | Cost per incident | Hours saved needed |
|---|---|---|
| Soda, platform fee only | $400 | 4.4 |
| Soda, with daily-check compute | $475 | 5.3 |
| Monte Carlo median, with daily-check compute | $297 | 3.3 |
| Bigeye ratio, with daily-check compute | $1,117 | 12.4 |
| Soda, with hourly-check compute | $2,200 | 24.4 |
An observability tool pays for itself if each incident it catches or explains saves the engineering team several hours of detection and root-cause work. At hourly query checks, the required saving becomes implausible unless the checks are made cheaper.
Sensitivity
- Check frequency. Moving from daily to hourly multiplies warehouse compute by 24.
- Query weight. Doubling seconds per check doubles compute; partition-pruned checks can reduce it sharply.
- Incident volume. Halving incidents from 240 to 120 a year doubles hours needed per incident.
- Pricing structure. The Soda Team plan at $96,000 versus the older $103,080 structure differs by 7% at 1,000 tables, but the free-dataset allowance matters more at 100.
Budgeting traps
- Vendor fee versus warehouse fee. They are billed by different companies, so neither shows the other.
- Per-asset definitions. A dataset, table, monitor and asset are different units on different quotes.
- Enterprise minimums. Not publicly listed for Monte Carlo or Bigeye.
- Renewal repricing. A reported buyer was moved to a new pricing model at renewal.
- AI root-cause analysis. It may be bundled or metered; ask.
What to ask before you buy
Ask each vendor what counts as one asset, how many monitors are included per asset, whether monitors query the warehouse or read metadata, and what the renewal pricing basis is. Then estimate warehouse compute for your own frequency and add it to the platform fee.