MARKET LANDSCAPE
Data Observability Market 2026: The Data Quality Tool Market, Vendors, and Companies
A straight map of the data observability and data quality tool market as it stands in July 2026: who owns which vendor after two years of consolidation, which companies publish a price, and which analyst coverage actually exists.
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What is the data observability market?
The data observability market is the category of software that continuously monitors production data for freshness, volume, schema changes, distribution anomalies, and lineage, as distinct from the much larger APM and infrastructure observability market that watches applications and servers. As of July 2026 it holds roughly a dozen serious commercial vendors plus a long tail of adjacent catalog, testing, and quality tools. The defining trend of the past eighteen months is consolidation: Datadog acquired Metaplane, Coalesce acquired SYNQ, Fivetran merged with dbt Labs and took stewardship of Great Expectations, and IBM folded Databand into watsonx.data.
Last updated July 2026
Side by side
Data observability market compared
| Vendor | Status in July 2026 | Publishes a price? | Best known for |
|---|---|---|---|
| Monte Carlo | Independent, category leader by mindshare | No. AWS Marketplace lists a $50,000 twelve month contract | Defining the category and broad enterprise coverage |
| Bigeye | Independent | No. Pricing page is gone. AWS Marketplace lists $45,000 a year for 100 tables | Lineage across legacy and on premise systems |
| Anomalo | Independent | No. Demo only, no free trial | Machine learning first monitoring on wide tables |
| Sifflet | Independent, sales led | No. Tiers scale by monitored assets | European roots, broad integration coverage |
| Acceldata | Independent | No. Pro and Enterprise both contact sales | Hybrid and on premise estates, plus data FinOps |
| Soda | Independent | Yes. Free $0, Team $750 a month, Enterprise custom | SodaCL checks as code and data contracts |
| Metaplane | Acquired by Datadog in April 2025, still sold standalone | Yes. Free forever for 10 tables and 4 users, then usage based | Fast setup and a genuine free tier |
| Datafold | Independent | No. Removed public pricing in 2026, /pricing now redirects to contact | Value level data diff for migrations and CI |
| Elementary | Independent | No. Scale, Enterprise, and Unlimited tiers all say talk to us | Open source dbt package, Apache 2.0, 30 day cloud trial |
| Great Expectations | GX Core stewarded by Fivetran since May 2026 | Core is free and open source. GX Cloud was acquired by FICO and withdrawn from public availability on June 1, 2026 | The original open source expectations framework |
| Coalesce Quality (formerly SYNQ) | Acquired by Coalesce, announced March 10, 2026 | No. Sold as part of the Coalesce platform | Quality wired into a transformation and catalog layer |
| IBM Databand | Wound down as a standalone product, sold inside watsonx.data | No | Pipeline observability, now an IBM platform feature |
| Dataobservability | Independent | Yes. $99, $299, and $799 a month, 14 day trial, no card | Self serve setup and published pricing |
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
Four things that are actually true about this market in 2026
Three of thirteen vendors will tell you the price
Soda, Metaplane, and Dataobservability publish real numbers. Everyone else routes you to a sales call. The two public reference points that exist come from AWS Marketplace listings, where Monte Carlo appears at $50,000 for a twelve month contract and Bigeye at $45,000 a year for 100 tables. If you are budgeting, assume enterprise data observability starts in the mid five figures unless a vendor says otherwise in writing.
Consolidation took four vendors off the board in eighteen months
Datadog acquired Metaplane in April 2025. IBM absorbed Databand into watsonx.data. Coalesce acquired SYNQ in March 2026 and relaunched it as Coalesce Quality. Fivetran completed its merger with dbt Labs on June 1, 2026, having already become steward of Great Expectations in May. The independent pure plays left are a shorter list than most 2024 era roundups suggest.
There is no Gartner Magic Quadrant for data observability
Gartner published a Market Guide for Data Observability Tools on February 23, 2026. A Market Guide names Representative Vendors and explicitly does not rank them, so nobody is a Leader or a Challenger in this category. The Magic Quadrant people half remember is for Augmented Data Quality Solutions, published February 11, 2026, which is a related but different market. Any vendor claiming Magic Quadrant Leader status in data observability is describing a document that does not exist.
Most published market size figures are measuring something else
The widely quoted numbers of roughly $3.4 to $4.4 billion for 2026 come from observability tools reports that count APM, infrastructure, and log platforms such as Datadog, Dynatrace, and New Relic. Data observability is a much smaller subset of that spend. Treat any single dollar figure for the data observability market with suspicion unless the report states its inclusion criteria, because the analyst houses do not agree on where the boundary sits.
How it works
From connected to caught
Decide which of the four vendor types you actually need
The market splits into metadata first platforms that monitor the warehouse automatically, rules and testing frameworks where you write the checks, machine learning first products that profile wide tables, and suite features bundled into a transformation, catalog, or APM platform. These are not competing versions of the same thing. Pick the type before you compare names, or you will run demos against products solving different problems.
Filter by whether you can actually buy it
A self serve team with a $30,000 annual tooling budget cannot buy Monte Carlo, Anomalo, or Acceldata, regardless of how good they are. If nobody on your side owns a six month procurement cycle, restrict the shortlist to vendors with published pricing, a free tier, or a genuine self serve trial, and save yourself four discovery calls that end in a number you were never going to approve.
Check who owns the vendor and what that implies
Ownership changed for a third of this market recently and it affects roadmap and independence. Metaplane inside Datadog will pull toward unified application and data observability. Coalesce Quality is being built into the Coalesce platform. Fivetran now sits across ingestion, transformation, and an open source quality framework, which raises a fair question about having your pipelines graded by the company that supplies them.
Test coverage on the tables you forgot about
Every vendor demos well on the ten tables you nominate. The difference shows on table 400, the one a new pipeline created last month that nobody wrote a rule for. Ask each vendor how a brand new table gets monitored, whether that happens automatically or requires someone to configure it, and how long before a broken table with no configured checks produces an alert. That single question separates the market more cleanly than any feature grid.
How big is the data observability market, really
This question has a frustrating answer: nobody credible knows, and most of the numbers in circulation are measuring a different category. Search for observability market size and you will find figures around $3.35 billion for 2026 growing to $6.93 billion by 2031, or $3.5 billion in 2026 reaching $5.4 billion by 2030, or $4.35 billion in 2026. Those reports are counting observability tools and platforms, which means Datadog, Dynatrace, New Relic, Splunk, Grafana, and the rest of the application performance and infrastructure monitoring world. That market is mature, large, and only tangentially related to whether your revenue table loaded this morning. Data observability, meaning software that monitors the data itself, is a subset of that spend and a much younger one. The reports that do try to size it specifically disagree with each other, largely because they disagree about what to include: some count data quality and master data management tools that predate the category by a decade, some count catalog platforms that added a quality module, some count only the pure plays. The honest framing is directional rather than numeric. Roughly a dozen serious commercial vendors compete for the core buyer, several were acquired in the last eighteen months, the buyer is usually a data platform or analytics engineering leader rather than a CIO, and typical enterprise contracts land in the mid five to low six figures a year. If you need a number for a board slide, cite the specific report and its inclusion criteria rather than repeating a figure with no boundary attached, because the person who checks it will find three others that disagree.
Who owns whom after the 2025 and 2026 consolidation wave
The vendor list you would have written in early 2024 is meaningfully out of date, and most roundups still circulating have not caught up. Datadog acquired Metaplane in April 2025, which was the first signal that application observability vendors intended to own the data layer too rather than partner into it. Metaplane still sells standalone and still has its free forever tier for 10 tables and 4 users, so nothing broke for existing users, but the roadmap now has a very large parent with its own view of where data monitoring belongs. Coalesce announced its acquisition of SYNQ on March 10, 2026 and relaunched the product as Coalesce Quality, folding observability into a platform that already does transformation and cataloging. The pitch there is that quality signals arrive connected to lineage, ownership, and downstream impact because they share the same metadata layer, which is a real architectural advantage if you are already a Coalesce customer and largely irrelevant if you are not. The biggest structural move was Fivetran and dbt Labs, announced October 13, 2025 and completed June 1, 2026, with George Fraser as chief executive and Tristan Handy as president. Fivetran had already taken stewardship of the Great Expectations open source project in May 2026, and the hosted GX Cloud went a different direction entirely: acquired by FICO and withdrawn from public availability on June 1, 2026, which means there is no longer a self serve hosted Great Expectations to buy. IBM finished absorbing Databand, which no longer exists as a standalone purchase and lives inside watsonx.data. Net effect: Monte Carlo, Bigeye, Anomalo, Sifflet, Acceldata, Soda, Datafold, and Elementary are the independents left, and buyers evaluating this market should confirm current ownership rather than trusting a comparison article written last year.
The four kinds of company in this market
Vendor names are less useful than architecture, because products built on different foundations fail in different ways. Metadata first platforms connect to the warehouse read only, read system tables and information schema to derive freshness, volume, and schema for every table automatically, and sample data only for distribution checks. Coverage is broad by default and compute cost stays low. The tradeoff is that a metadata first tool knows a table is late or short before it knows the values are subtly wrong. Rules and testing frameworks, meaning Great Expectations, dbt tests, Soda checks, and AWS Glue Data Quality, invert that: they check exactly what you assert, with total precision on the rules you wrote and total blindness everywhere else. They are cheap, they live in version control, and their coverage is a direct function of team discipline, which decays. Machine learning first products profile table contents deeply and learn what normal looks like column by column, which catches value level drift the other two approaches miss, at the cost of scanning a lot of data and needing tuning time before the alerts are trustworthy. Suite features are quality modules inside a bigger platform: Databricks Lakehouse Monitoring, Snowflake Data Metric Functions, Dataplex on BigQuery, Coalesce Quality, Metaplane within Datadog. They integrate perfectly with their host and stop at its boundary, which is fine for a single platform shop and a problem the moment data crosses systems. Most teams end up with two of the four: something automatic for coverage, and something rule based for the business logic only they can express.
What Gartner and the analyst houses actually publish
This one causes real confusion in procurement, so it is worth stating precisely. Gartner does not publish a Magic Quadrant for data observability. What exists is a Market Guide for Data Observability Tools, published February 23, 2026. Market Guides serve a different purpose from Magic Quadrants: they describe an emerging or fragmented market, define the capability set, and list Representative Vendors, and they explicitly do not rank or position those vendors against each other. Nobody is in the upper right of a data observability quadrant, because there is no quadrant. Separately, Gartner published a Magic Quadrant for Augmented Data Quality Solutions on February 11, 2026, which is a related and older market covering profiling, cleansing, matching, and enrichment, with a vendor list that skews toward Informatica, IBM, SAP, Precisely, and similar. Those two documents get conflated constantly, including by vendors who should know better. When a data observability vendor tells you they are a Gartner Leader, ask which report and which year, because the plausible answers are that they appear as a Representative Vendor in a Market Guide, which carries no ranking, or that they are being positioned in a different market. Both documents are paywalled, so verify a claim by asking for the reprint rather than accepting a quoted line. Forrester has covered adjacent categories rather than data observability specifically, and G2 grids reflect review volume, which correlates more with marketing budget and customer count than with fit for your stack.
How pricing works in this market, and why most vendors hide it
Almost every vendor in this category prices on the number of tables, datasets, or monitored assets, sometimes with a seat component layered on top. That is why the price is hidden: table count varies enormously between customers, so a published number would either scare off the small buyer or leave money on the table with the large one. The practical consequence for a buyer is that you cannot compare vendors without knowing your own asset count, and you should get that number before the first call rather than during it. Count the tables you would actually want monitored, not every object in the warehouse, and know how fast that count is growing, because year two renewals are where table based pricing surprises people. The public reference points are worth memorizing because they are the only anchors most buyers get. AWS Marketplace has listed Monte Carlo at $50,000 for a twelve month contract and Bigeye at $45,000 a year for 100 tables with an enterprise tier at $75,000 a year for 300 tables. Soda publishes a Team tier at $750 a month. Metaplane has a genuine free forever tier at 10 tables and 4 users, then usage based pricing. Dataobservability publishes $99, $299, and $799 a month with a 14 day trial and no card required. Everyone else, including Anomalo, Acceldata, Sifflet, Datafold, and Elementary, requires a conversation. None of that makes the quote only vendors bad, and for a large regulated estate the sales led ones often genuinely fit better. It does mean that a lean team should start its evaluation at the transparent end of the market, because the four weeks spent discovering that a platform costs eight times the budget is four weeks of broken dashboards nobody was watching.
Questions buyers ask
Data observability market FAQ
How big is the data observability market?
There is no reliable single figure. The commonly cited numbers of roughly $3.4 to $4.4 billion for 2026 come from observability tools reports that count APM and infrastructure platforms such as Datadog and Dynatrace, not data observability specifically. Reports that try to size data observability alone disagree because they disagree on whether to include legacy data quality and catalog tools. Roughly a dozen serious commercial vendors compete for the core buyer.
Who are the main data observability companies in 2026?
The independent commercial vendors are Monte Carlo, Bigeye, Anomalo, Sifflet, Acceldata, Soda, Datafold, Elementary, and Dataobservability. Metaplane is owned by Datadog, Coalesce Quality is the former SYNQ acquired by Coalesce in March 2026, Databand sits inside IBM watsonx.data, and Great Expectations Core is now stewarded by Fivetran. Warehouse vendors also ship native features, including Snowflake Data Metric Functions and Databricks Lakehouse Monitoring.
Is there a Gartner Magic Quadrant for data observability?
No. Gartner published a Market Guide for Data Observability Tools on February 23, 2026, which names Representative Vendors and explicitly does not rank them. The Magic Quadrant frequently mistaken for it covers Augmented Data Quality Solutions and was published February 11, 2026, a related but distinct market. No vendor can accurately claim to be a Leader in a data observability Magic Quadrant.
Which data observability vendors publish their pricing?
Three of the thirteen main vendors publish real numbers: Soda at $0 free, $750 a month for Team, and custom Enterprise; Metaplane with a free forever tier for 10 tables and 4 users then usage based pricing; and Dataobservability at $99, $299, and $799 a month. Monte Carlo, Bigeye, Anomalo, Sifflet, Acceldata, Datafold, and Elementary all route to sales.
How much does data observability software cost?
For self serve tools, $0 to roughly $800 a month. For enterprise platforms, the public reference points come from AWS Marketplace, where Monte Carlo has been listed at $50,000 for a twelve month contract and Bigeye at $45,000 a year for 100 tables, rising to $75,000 a year for 300 tables. Almost all vendors price by monitored tables or assets, so your table count drives the quote more than anything else.
What happened to Metaplane and SYNQ?
Datadog acquired Metaplane in April 2025. It still sells standalone and still offers its free forever tier for 10 tables and 4 users. Coalesce announced the acquisition of SYNQ on March 10, 2026 and relaunched it as Coalesce Quality, integrating observability into the Coalesce transformation and catalog platform rather than continuing it as a standalone product.
Did Fivetran acquire dbt Labs?
They merged rather than one acquiring the other. The all stock merger was announced October 13, 2025 and completed June 1, 2026, with Fivetran chief executive George Fraser leading the combined company and dbt Labs co-founder Tristan Handy as president. Fivetran also became the steward of the Great Expectations open source project in May 2026, placing ingestion, transformation, and an open source data quality framework under one roof.
Can I still use Great Expectations Cloud?
Not as a public product. GX Core remains free, open source, and Apache 2.0 licensed, and Fivetran took over stewardship in May 2026. The hosted GX Cloud offering was acquired by FICO and withdrawn from public availability on June 1, 2026, so there is no self serve hosted Great Expectations to sign up for. Teams wanting a managed experience now have to self host GX Core or choose a different vendor.
What is the difference between the data observability market and the data quality tool market?
They overlap and are converging, but the buyers differ. The data quality tool market is older and centers on profiling, cleansing, matching, standardization, and master data, sold to governance and data management functions, with vendors like Informatica, IBM, and Precisely. Data observability is newer, monitors production pipelines for freshness, volume, schema, and anomalies, and is bought by data engineering and analytics leaders. Gartner still covers them as separate markets.
How do I choose between data observability vendors?
Start by picking an architecture rather than a brand: metadata first for automatic coverage, rules based for precise business logic, machine learning first for value level drift, or a suite feature if you are single platform. Then filter by what you can actually procure, since roughly three quarters of the market is quote only. Finally, test each finalist on the tables nobody nominated, because automatic coverage of new and forgotten tables is where products genuinely differ.
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