BUYER GUIDE
Data Observability Pricing: What Every Tool Costs in 2026
Almost nobody in this category publishes a price. Here is what each data observability tool actually costs, what is verifiable, and what you should budget beyond the license.
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How much does data observability cost?
Data observability pricing splits into three bands. Self-serve tools publish a price and start between 0 and 750 dollars a month (Dataobservability from 99 dollars a month, Soda Team at 750, Metaplane with a free tier). Enterprise platforms are quote-only: the public AWS Marketplace listings for Bigeye start at 45,000 dollars a year and Monte Carlo at 50,000 dollars a year. Open-source frameworks have no license fee and cost engineering time instead. Verified July 2026.
Last updated July 2026
Side by side
Data observability pricing compared
| Tool | Publishes a price? | What is actually published (July 2026) | Free tier or trial |
|---|---|---|---|
| Dataobservability | Yes | Starter 99 dollars, Team 299, Scale 799 per month | 14-day trial, no card |
| Monte Carlo | No | Credit-based tiers, quote only. Its AWS Marketplace listing shows 50,000 dollars per 12-month contract | No free tier |
| Bigeye | No | No pricing page. Its AWS Marketplace listing shows Starter at 45,000 dollars a year (100 tables) and Enterprise Starter at 75,000 (300 tables) | Narrow trial only |
| Anomalo | No | No pricing page. Demo request only | No |
| Sifflet | No | Tiers scoped by monitored assets (Entry up to 500, Growth up to 1,000, Enterprise above), no figures | No public price |
| Acceldata | No | Pro and Enterprise tiers, both Contact Sales | 30-day trial |
| Soda | Yes | Free at 0 dollars, Team at 750 dollars a month, Enterprise custom | Free tier |
| Metaplane (Datadog) | Yes | Free forever tier (10 tables, 4 users), Pro usage-based, Enterprise custom | Free tier |
| Elementary | Partly | Open-source package is free (Apache 2.0). Cloud tiers are named (Scale, Enterprise, Unlimited) with no figures, seat and table capped | 30-day trial |
| Great Expectations | Partly | GX Core is free open source. GX Cloud has a free Developer tier, paid tiers not published | Free tier |
| Datafold | No | Removed its public pricing in 2026. The pricing page now redirects to contact sales | Not published |
| IBM Databand | No | Folded into IBM watsonx.data integration, priced in Resource Units, quote only | 30-day trial |
Positioning and pricing models are summarized in good faith from each vendor's public pages, July 2026. Verify current terms with the vendor.
What you get
What you are actually paying for
A price you can put in a budget
Starter is 99 dollars a month, Team is 299, Scale is 799. No credit model to decode, no quote to chase, no annual commitment to sign before you know whether the product works on your warehouse.
No sales cycle tax
Enterprise platforms bury a 6-week procurement process in the price. You can sign up, connect a read-only role, and be monitoring production tables in about 15 minutes.
Metadata-first, so the compute bill stays small
Monitors read warehouse metadata (information schema, query history, dbt artifacts) before they touch rows. Tools that run full-table scans on every check can cost more in warehouse credits than they cost in license.
You are not priced per table you forgot about
Table-count pricing punishes wide warehouses: one vendor caps its 45,000 dollar tier at 100 monitored tables. Coverage should not be the thing you ration.
How it works
From connected to caught
Add up all three costs, not one
Total cost is license plus warehouse compute burned by checks plus the engineering hours to build and maintain rules. Teams compare line one and get surprised by lines two and three.
Price your real table count
Ask every vendor what happens at 300, 1,000, and 3,000 tables. Table-capped tiers and per-asset pricing get expensive quickly, and the caps are where the quote actually comes from.
Run a real trial and count true positives
Connect your warehouse for a week and count how many alerts were real. A tool that costs half as much and fires twice as much noise is not cheaper.
Compare against the cost of one incident
A single day of a wrong revenue dashboard, a broken customer sync, or a model trained on stale data usually costs more than a year of monitoring. That is the honest ROI math, not a made-up multiplier.
The three price bands in this market
Band one is self-serve and published: Dataobservability from 99 dollars a month, Soda Team at 750 dollars a month, Metaplane with a free tier for 10 tables. You can buy without talking to anyone. Band two is enterprise and quote-only: Monte Carlo, Bigeye, Anomalo, Sifflet, Acceldata and IBM all require a demo before a number. Where those numbers surface publicly, they are large: the AWS Marketplace listings show Bigeye Starter at 45,000 dollars a year for 100 monitored tables and Monte Carlo at 50,000 dollars for a 12-month contract. Band three is open source (Great Expectations, Soda Core, Elementary): zero license fee, and the whole cost lands on your engineers.
Why almost nobody publishes a price
Value-based pricing is the polite explanation, and vendors say so openly: Elementary answers the question "can I get pricing without scheduling a call" with a paragraph about aligning price with value. The practical explanation is that quote-only pricing lets a vendor charge each buyer what that buyer looks able to pay, and it puts a salesperson between you and the number. It also means the cheapest way to find out what a tool costs is often to read its AWS Marketplace listing rather than its website. We publish our price because the buyer we want is a data engineer with a budget and no appetite for a six-week procurement dance.
The hidden line item: warehouse compute
Every data quality check is a query, and every query costs credits. A tool that runs SELECT COUNT(*) and full-column profiling across every table on every schedule can quietly add a meaningful percentage to a Snowflake or BigQuery bill. Metadata-first monitoring avoids most of that: freshness, volume, and schema can be derived from the information schema, query history, and dbt artifacts without scanning rows, and only distribution checks need to sample data. When you trial a tool, tag its warehouse role and look at the credit consumption it generates over that week. That number belongs in the comparison spreadsheet next to the license fee.
How the pricing models differ, and which one bites you
Four models dominate. Per-seat pricing (Elementary) is predictable until analysts want access. Per-table or per-asset pricing (Bigeye, Sifflet, Elementary) is the one that surprises people: a warehouse with 4,000 tables blows through a 500-asset tier, and the fix is either a bigger tier or monitoring less of your warehouse, which defeats the point. Credit or consumption pricing (Monte Carlo, IBM Resource Units) is flexible and genuinely hard to forecast before you run it. Flat subscription pricing (ours, Soda Team) is the easiest to budget and the least clever. Ask which model a vendor uses before you ask for a number, because the model tells you where the bill grows.
Is open source actually cheaper?
For a small number of critical tables, yes, and honestly so. Great Expectations, Soda Core, and the Elementary dbt package cost nothing in license fees and give you exact control. The bill arrives as engineering time: someone writes the checks, someone updates them when the schema changes, someone runs the scheduler, and someone fields the false positives. Coverage tends to stall at the tables one engineer worried about. If you price a mid-level data engineer at even a few hours a week of maintenance, an open-source stack passes the cost of a self-serve subscription within the first year, and it still only catches the failures somebody predicted in advance.
Questions buyers ask
Data observability pricing FAQ
How much does data observability cost?
It depends on the band. Self-serve tools that publish pricing run from 0 to 750 dollars a month: Dataobservability starts at 99 dollars a month, Soda Team is 750, Metaplane has a free tier. Enterprise platforms are quote-only and land far higher: public AWS Marketplace listings show Bigeye from 45,000 dollars a year and Monte Carlo at 50,000 dollars per 12-month contract.
How much does Monte Carlo data observability cost?
Monte Carlo does not publish a price on its website. It sells four tiers (Start, Scale, Enterprise, Business Critical) on a credit and consumption model, and you request pricing. The one public figure is its AWS Marketplace listing, which shows 50,000 dollars per 12-month contract with metered overage. Third-party contract data suggests real deals often land well above that, but only the listing is verifiable.
How much does Bigeye cost?
Bigeye has no pricing page (bigeye.com/pricing returns a 404) and sells through a demo. Its public AWS Marketplace listing shows Starter at 45,000 dollars for 12 months covering 100 actively monitored tables, and Enterprise Starter at 75,000 dollars for 300 tables. The table caps matter more than the headline number if your warehouse is wide.
Is there a free data observability tool?
Yes, with real limits. Metaplane by Datadog has a free forever tier (10 tables, 4 users). Soda has a free plan. Great Expectations Core and the Elementary open-source package are free to self-host under open-source licenses. All of them trade money for either a small table budget or your engineering time. Paid self-serve monitoring starts around 99 dollars a month.
Why do data observability vendors hide their pricing?
Because quote-only pricing lets a vendor size the price to the buyer and puts a sales conversation before the number. Most of the category does it: Monte Carlo, Bigeye, Anomalo, Sifflet, Acceldata, Datafold (which removed its public prices in 2026) and IBM all require contact with sales. Soda, Metaplane and Dataobservability are the exceptions that publish figures.
What should I budget for data observability beyond the license?
Budget three things. The license fee. The warehouse compute the checks burn, which is significant for tools that scan rows rather than read metadata. And engineering time, which is the dominant cost for open-source frameworks because every table needs its own hand-written, hand-maintained rules.
Catch broken data before your stakeholders do
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