definity

Name: definity
Brand: definity

AI Native

The Agentic Data Engineering Platform for the Lakehouse & Spark Ecosystem.

Est 2022|Valuation $52.5M|Raised $16.5M

LinkedIn 756|Web traffic 3K|FTEs 15|HQ Chicago, IL

MarketsData Observability

Published Sep 26, 2026

01Common complaints

1) If the primary need is dataset-level observability across many systems (warehouse + BI + many ETL tools), a data-observability vendor focused on table-level checks may provide broader out-of-the-box coverage. 2) If procurement requires publicly posted SOC 2 or ISO documentation prior to vendor engagement, this could introduce additional friction unless the vendor can supply documentation privately. 3) If the organization is highly risk-averse to automated intervention (stopping jobs or diverting outputs), a passive monitoring tool may be preferable. 4) If the organization has an existing strategy to consolidate observability onto a single platform and wants to avoid additional silos, adding a specialized agent may be undesirable. 5) If the procurement team requires abundant peer reviews on public platforms for de-risking, limited visible third-party reviews may be a blocker.

Deal disqualifiers (poor fit)

Minimal Spark usage (for example, warehouse-centric ELT implementations).
Organizational policies that prohibit runtime agents or additional job instrumentation.
A requirement for broad multi-engine observability as a first priority (Snowflake-only or BigQuery-only organizations without Spark workloads).

Limitations evidenced in sources

Public third-party review scarcity: limited public reviews make it harder to validate support responsiveness and product stability externally.
Security certifications not publicly documented: absence of SOC 2/ISO statements can slow enterprise procurement.
Operational complexity: features such as automated tuning and intervention require careful testing, rollback plans, and governance.

Roadmap / recent product updates

Vendor communications indicate ongoing expansion of capabilities (for example, support for additional Spark patterns). This suggests active product iteration and evolving maturity.

Scale considerations

The performance impact and overhead of agents under peak load are not explicitly documented; buyers should validate agent overhead during pilot testing.

02Why they buy

Likely differentiators

In-pipeline, runtime instrumentation (agents running inside Spark execution) that provide execution-level signals and enable detection during job runs.
A combined focus on cost optimization, observability, and upgrade validation rather than solely post-hoc monitoring.

Claims that require validation

Reported incident prevention rates and cost-reduction percentages are vendor-published; organizations should validate these claims via a proof-of-value with defined measurement criteria.

Competitive advantages of alternatives

Monte Carlo: broader data ecosystem coverage and structured enterprise packaging for dataset-level observability.
Acceldata: an enterprise-oriented observability cloud with a wide set of modules.
Unravel: mature alignment with compute billing metrics and optimization heritage.

03Field reports

Review-platform coverage reality check (G2 / TrustRadius / Gartner Peer Insights)

A search of major review platforms did not return a comprehensive, public presence for definity at the time of research. This may indicate limited third-party review coverage or that listings are not widely indexed.

Implication: Public third-party review coverage for this vendor appears limited in the sources accessed for this report.

Available testimonial evidence (vendor site and independent press quoting customers)

Because major review platforms did not surface vendor-specific reviews, the available customer-perspective content comes from: 1) Vendor-curated customer quotes on the company website. 2) Independent press (a trade publication) that included a named customer. Positive quotes (vendor-curated; marketing content): - "definity lets our engineers spend time where it matters the most" — Eric, Head of Data Science & Engineering. - "definity’s provided a turnkey solution to optimize Spark cost BIG TIME" — Dennis, Director of Data Engineering. - "definity’s technology is clearly missing in the AWS stack" — Glenn, Sr Director of Data Platform. Independent press (named customer example): - A trade article quoted a customer at Nexxen describing the need for full-stack visibility beyond existing monitoring tools and reported vendor-provided outcomes such as identification of optimization opportunities in an initial deployment period (various quantitative outcomes were reported in the coverage).

Negative reviews / criticisms

No definity-specific negative reviews on major platforms were located in the accessible research results.
Public community discussions specific to definity were not found in high-signal sources; searches for "definity" frequently surface unrelated entities.

What customers appear to choose the product for (based on available sources)

Spark cost optimization and efficiency, particularly for on-prem environments where inefficient runs have direct cost implications.
Runtime/in-motion incident detection and prevention rather than exclusively post-hoc alerting.
Upgrade validation and reduction of risk during platform changes (described by the vendor as CI-style validation for data applications).

Potential churn drivers (inferred from category dynamics)

Consolidation onto a broader observability vendor favored by the organization (for unified toolchains and billing).
If the primary problem is dataset-level data quality across many platforms, a data-observability vendor focused on tables and metadata may be preferred.
If an organization relies on managed services whose built-in telemetry is sufficient for operational needs, the incremental value of a specialized runtime agent may be reduced.

Case study evidence available

Vendor customer stories on the company website include claimed outcomes such as reductions in Spark platform cost and ROI statements.
A trade publication included a named customer (Nexxen) and reported customer statements about initial optimization opportunities and reductions in troubleshooting effort.

Critical evaluation

- Vendor-published metrics are not accompanied by independently verified methodologies in the materials accessed for this report. - Press coverage that includes named customers provides contextual information, but reported metrics are still customer or vendor statements rather than results from third-party audits. - Buyers should request the following details from the vendor when evaluating case studies: baseline compute spend, workload composition, what specific interventions were applied (configuration vs code changes), and how "prevented incidents" and ROI were defined and measured.

04Best fit

Company size

Most likely fit: mid-market to enterprise data platforms where Spark costs and reliability issues are material.
Practical proxy: mid-sized to large organizations with many employees and numerous Spark jobs.

Industries / verticals

Financial services, adtech/media/ecommerce, and other industries that run large-scale Spark workloads and have material compute costs.

Roles and personas

Decision makers: Head of Data Platform, VP Data Engineering, Director of Data Engineering, Platform Engineering leaders.
End users: data engineers, platform/SRE engineers supporting Spark, and data reliability engineers.

Pain points addressed

High or uncertain Spark compute spend and difficulty attributing waste.
Slow incident RCA across distributed Spark jobs.
Data issues that propagate downstream and require rapid isolation.
Risk during upgrades and migrations where pre- and post-validation of runs is necessary.

Qualification criteria (strong fit)

Spark is central to the environment and compute spend is a primary cost driver.
Many pipelines have SLA constraints where earlier runtime detection has clear operational benefit.
The platform team prefers centralized instrumentation rather than per-pipeline code changes.

05The pitch

What the product does (core value proposition)

definity describes itself as an "agentic data engineering" platform focused on Spark-first / lakehouse environments. In objective terms, it provides: - Runtime instrumentation (agents inside Spark jobs / pipelines) to capture execution and data signals while jobs run. - Operational analytics and recommendations intended to reduce compute waste, improve performance, and help prevent pipeline incidents. - In-motion observability and intervention (e.g., detect issues before or while pipelines run, with potential to preempt runs). - Troubleshooting and root-cause workflows that tie together job execution, lineage, code/environment changes, and data behavior. - CI-style validation for data applications (referred to by the vendor as "code changes validation" and automated testing for upgrades and migrations). This differs from data-quality observability tools that primarily analyze datasets after they land; definity emphasizes instrumentation at execution time.

Target market and market positioning

Target users: data platform teams and data engineering teams operating Spark-based production pipelines (Databricks, EMR, Dataproc, and on-prem Spark are explicitly referenced).
Positioning: the vendor presents the product as a combination of observability, optimization, incident prevention, and change validation for lakehouse environments, with claims of minimizing the need for code changes and facilitating fast deployment.

Get a comprehensive analysis of definity including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →

Published Sep 26, 2026

01Common complaints

If the primary need is dataset-level observability across many systems (warehouse + BI + many ETL tools), a data-observability vendor focused on table-level checks may provide broader out-of-the-box coverage.
If procurement requires publicly posted SOC 2 or ISO documentation prior to vendor engagement, this could introduce additional friction unless the vendor can supply documentation privately.
If the organization is highly risk-averse to automated intervention (stopping jobs or diverting outputs), a passive monitoring tool may be preferable.
If the organization has an existing strategy to consolidate observability onto a single platform and wants to avoid additional silos, adding a specialized agent may be undesirable.
If the procurement team requires abundant peer reviews on public platforms for de-risking, limited visible third-party reviews may be a blocker.

Deal disqualifiers (poor fit)

Minimal Spark usage (for example, warehouse-centric ELT implementations).
Organizational policies that prohibit runtime agents or additional job instrumentation.
A requirement for broad multi-engine observability as a first priority (Snowflake-only or BigQuery-only organizations without Spark workloads).

Limitations evidenced in sources

Public third-party review scarcity: limited public reviews make it harder to validate support responsiveness and product stability externally.
Security certifications not publicly documented: absence of SOC 2/ISO statements can slow enterprise procurement.
Operational complexity: features such as automated tuning and intervention require careful testing, rollback plans, and governance.

Roadmap / recent product updates

Vendor communications indicate ongoing expansion of capabilities (for example, support for additional Spark patterns). This suggests active product iteration and evolving maturity.

Scale considerations

The performance impact and overhead of agents under peak load are not explicitly documented; buyers should validate agent overhead during pilot testing.

02Why they buy

Likely differentiators

In-pipeline, runtime instrumentation (agents running inside Spark execution) that provide execution-level signals and enable detection during job runs.
A combined focus on cost optimization, observability, and upgrade validation rather than solely post-hoc monitoring.

Claims that require validation

Reported incident prevention rates and cost-reduction percentages are vendor-published; organizations should validate these claims via a proof-of-value with defined measurement criteria.

Competitive advantages of alternatives

Monte Carlo: broader data ecosystem coverage and structured enterprise packaging for dataset-level observability.
Acceldata: an enterprise-oriented observability cloud with a wide set of modules.
Unravel: mature alignment with compute billing metrics and optimization heritage.

03Field reports

Review-platform coverage reality check (G2 / TrustRadius / Gartner Peer Insights)

A search of major review platforms did not return a comprehensive, public presence for definity at the time of research. This may indicate limited third-party review coverage or that listings are not widely indexed.

Implication: Public third-party review coverage for this vendor appears limited in the sources accessed for this report.

Available testimonial evidence (vendor site and independent press quoting customers)

Because major review platforms did not surface vendor-specific reviews, the available customer-perspective content comes from:

Vendor-curated customer quotes on the company website.
Independent press (a trade publication) that included a named customer.

Positive quotes (vendor-curated; marketing content):

"definity lets our engineers spend time where it matters the most" — Eric, Head of Data Science & Engineering.
"definity’s provided a turnkey solution to optimize Spark cost BIG TIME" — Dennis, Director of Data Engineering.
"definity’s technology is clearly missing in the AWS stack" — Glenn, Sr Director of Data Platform.

Independent press (named customer example):

A trade article quoted a customer at Nexxen describing the need for full-stack visibility beyond existing monitoring tools and reported vendor-provided outcomes such as identification of optimization opportunities in an initial deployment period (various quantitative outcomes were reported in the coverage).

Negative reviews / criticisms

No definity-specific negative reviews on major platforms were located in the accessible research results.
Public community discussions specific to definity were not found in high-signal sources; searches for "definity" frequently surface unrelated entities.

What customers appear to choose the product for (based on available sources)

Spark cost optimization and efficiency, particularly for on-prem environments where inefficient runs have direct cost implications.
Runtime/in-motion incident detection and prevention rather than exclusively post-hoc alerting.
Upgrade validation and reduction of risk during platform changes (described by the vendor as CI-style validation for data applications).

Potential churn drivers (inferred from category dynamics)

Consolidation onto a broader observability vendor favored by the organization (for unified toolchains and billing).
If the primary problem is dataset-level data quality across many platforms, a data-observability vendor focused on tables and metadata may be preferred.
If an organization relies on managed services whose built-in telemetry is sufficient for operational needs, the incremental value of a specialized runtime agent may be reduced.

Case study evidence available

Vendor customer stories on the company website include claimed outcomes such as reductions in Spark platform cost and ROI statements.
A trade publication included a named customer (Nexxen) and reported customer statements about initial optimization opportunities and reductions in troubleshooting effort.

Critical evaluation

Vendor-published metrics are not accompanied by independently verified methodologies in the materials accessed for this report.
Press coverage that includes named customers provides contextual information, but reported metrics are still customer or vendor statements rather than results from third-party audits.
Buyers should request the following details from the vendor when evaluating case studies: baseline compute spend, workload composition, what specific interventions were applied (configuration vs code changes), and how "prevented incidents" and ROI were defined and measured.

04Best fit

Company size

Most likely fit: mid-market to enterprise data platforms where Spark costs and reliability issues are material.
Practical proxy: mid-sized to large organizations with many employees and numerous Spark jobs.

Industries / verticals

Financial services, adtech/media/ecommerce, and other industries that run large-scale Spark workloads and have material compute costs.

Roles and personas

Decision makers: Head of Data Platform, VP Data Engineering, Director of Data Engineering, Platform Engineering leaders.
End users: data engineers, platform/SRE engineers supporting Spark, and data reliability engineers.

Pain points addressed

High or uncertain Spark compute spend and difficulty attributing waste.
Slow incident RCA across distributed Spark jobs.
Data issues that propagate downstream and require rapid isolation.
Risk during upgrades and migrations where pre- and post-validation of runs is necessary.

Qualification criteria (strong fit)

Spark is central to the environment and compute spend is a primary cost driver.
Many pipelines have SLA constraints where earlier runtime detection has clear operational benefit.
The platform team prefers centralized instrumentation rather than per-pipeline code changes.

05The pitch

What the product does (core value proposition)

definity describes itself as an "agentic data engineering" platform focused on Spark-first / lakehouse environments. In objective terms, it provides:

Runtime instrumentation (agents inside Spark jobs / pipelines) to capture execution and data signals while jobs run.
Operational analytics and recommendations intended to reduce compute waste, improve performance, and help prevent pipeline incidents.
In-motion observability and intervention (e.g., detect issues before or while pipelines run, with potential to preempt runs).
Troubleshooting and root-cause workflows that tie together job execution, lineage, code/environment changes, and data behavior.
CI-style validation for data applications (referred to by the vendor as "code changes validation" and automated testing for upgrades and migrations).

This differs from data-quality observability tools that primarily analyze datasets after they land; definity emphasizes instrumentation at execution time.

Target market and market positioning

Target users: data platform teams and data engineering teams operating Spark-based production pipelines (Databricks, EMR, Dataproc, and on-prem Spark are explicitly referenced).
Positioning: the vendor presents the product as a combination of observability, optimization, incident prevention, and change validation for lakehouse environments, with claims of minimizing the need for code changes and facilitating fast deployment.

Get a comprehensive analysis of definity including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →