OMMA Data

Name: OMMA Data
Brand: OMMA Data

AI Native

Quality Data starts with OMMA

Est 2021|Raised $150M

LinkedIn 2K|Website|FTEs 13|HQ Barcelona, Catalonia, ES

MarketsData Quality

01Competitive Landscape

Narrative analysis

Where the vendor appears differentiated: - ingestion-time emphasis: vendor materials describe the product as focused on intake-time data quality - a no-code interface is presented as a way to reduce engineering involvement Source: - Regional supplier directory listing Where competitors often excel: - market presence and peer validation: competitors such as Soda and Monte Carlo have larger public footprints, more third-party references, and broader community engagement - open-source foundations: tools with open-source components tend to generate more community examples and integrations Sources: - Official vendor pricing and product pages for Soda Pricing comparison and value modeling: - OMMA’s published monthly range suggests potential accessibility for mid-market buyers, but the absence of a public calculator makes TCO modeling difficult. - Monte Carlo and similar enterprise observability vendors typically use sales-led pricing and contract negotiation. Source (cost sentiment, general): - Industry Reddit and community threads Adoption trends / market share: No reliable public market share metrics for OMMA Data were identified. Company directory listings are available but do not provide auditable adoption statistics. Sources: - Company directory and market-data listings (e.g., Crunchbase, CB Insights)

	OMMA Data	Soda (Soda Data Quality)	Monte Carlo (Data + AI Observability)
Starting Price	Published variable monthly pricing (vendor-provided range; volume-based; details unclear)	Public pricing information and a free option for limited scope; enterprise pricing may still be custom	Pricing is typically sales-led/custom; marketplace listings indicate contract-based pricing
Typical Fit	Teams seeking ingestion-time data quality controls with a no-code interface and Spark/Big Data orientation (vendor positioning)	Teams that want a combination of open-source tooling and commercial support for data quality	Larger organizations seeking broad data observability across many assets and tools
Key Strength	Emphasis on ingestion-time checks and a no-code rule UX as described by the vendor	Ecosystem and community footprint with open-source components	Broad market visibility and coverage across the data stack
Key Weakness	Limited publicly documented security attestations; sparse third-party reviews; limited publicly visible connector catalog	Requires engineering discipline; pricing may scale with assets	Often perceived as higher cost and may require significant setup/configuration

01Competitive Landscape

Narrative analysis

Where the vendor appears differentiated:

ingestion-time emphasis: vendor materials describe the product as focused on intake-time data quality
a no-code interface is presented as a way to reduce engineering involvement

Source:

Regional supplier directory listing

Where competitors often excel:

market presence and peer validation: competitors such as Soda and Monte Carlo have larger public footprints, more third-party references, and broader community engagement
open-source foundations: tools with open-source components tend to generate more community examples and integrations

Sources:

Official vendor pricing and product pages for Soda

Pricing comparison and value modeling:

OMMA’s published monthly range suggests potential accessibility for mid-market buyers, but the absence of a public calculator makes TCO modeling difficult.
Monte Carlo and similar enterprise observability vendors typically use sales-led pricing and contract negotiation.

Source (cost sentiment, general):

Industry Reddit and community threads

Adoption trends / market share: No reliable public market share metrics for OMMA Data were identified. Company directory listings are available but do not provide auditable adoption statistics.

Sources:

Company directory and market-data listings (e.g., Crunchbase, CB Insights)

	OMMA Data	Soda (Soda Data Quality)	Monte Carlo (Data + AI Observability)
Starting Price	Published variable monthly pricing (vendor-provided range; volume-based; details unclear)	Public pricing information and a free option for limited scope; enterprise pricing may still be custom	Pricing is typically sales-led/custom; marketplace listings indicate contract-based pricing
Typical Fit	Teams seeking ingestion-time data quality controls with a no-code interface and Spark/Big Data orientation (vendor positioning)	Teams that want a combination of open-source tooling and commercial support for data quality	Larger organizations seeking broad data observability across many assets and tools
Key Strength	Emphasis on ingestion-time checks and a no-code rule UX as described by the vendor	Ecosystem and community footprint with open-source components	Broad market visibility and coverage across the data stack
Key Weakness	Limited publicly documented security attestations; sparse third-party reviews; limited publicly visible connector catalog	Requires engineering discipline; pricing may scale with assets	Often perceived as higher cost and may require significant setup/configuration