DataRoot Labs vs EPAM Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
EPAM Systems (3.9/5) edges ahead of DataRoot Labs (3.8/5) overall. EPAM Systems is the better choice for large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration. DataRoot Labs is the stronger option for startups and scale-ups needing AI strategy alongside execution, with accessible starting budgets and a boutique consultancy approach. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs EPAM Systems: head-to-head summary
| Criterion | DataRoot Labs | EPAM Systems |
|---|---|---|
| Founded | 2016 | 1993 |
| HQ | Kyiv, Ukraine | Newtown, PA, USA |
| Team size | 50–100 | 62,000+ |
| Rating | 3.8 / 5 | 3.9 / 5 |
| Best for | Startups and scale-ups needing AI strategy alongside execution, with accessible starting budgets and a boutique consultancy approach | Large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration |
| Pricing model | Fixed project, T&M, Retainer | Dedicated team, T&M, Fixed project, Staff augmentation |
| Min. engagement | $15K | $50K |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, TensorFlow, PyTorch |
| Industries served | SaaS, fintech, media, healthcare, logistics | financial services, healthcare, retail, media, government |
DataRoot Labs vs EPAM Systems: overview
DataRoot Labs
DataRoot Labs is a machine learning and AI consulting company headquartered in Kyiv, Ukraine. The company employs 50–100 professionals and is recognized as one of Ukraine's most trusted ML consultancies, combining strategic AI advisory with hands-on engineering execution. DataRoot Labs works with startups, scale-ups, and mid-market organizations needing to build or accelerate their ML capabilities, particularly in the Ukrainian and European tech ecosystems.
EPAM Systems
EPAM Systems is a global technology engineering company founded in 1993 and headquartered in Newtown, Pennsylvania. The company employs 62,000+ engineers across 50+ countries and is publicly traded on the NYSE. EPAM provides end-to-end AI development services from strategy and consulting to implementation and support, working with Fortune 500 clients across financial services, healthcare, retail, media, and government. EPAM is the largest firm in this review, with AI/ML capabilities delivered within a full-service technology engineering operation.
Services and capabilities: DataRoot Labs vs EPAM Systems
| Capability | DataRoot Labs | EPAM Systems |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| ML consulting | ✓ | ✓ |
| Deep learning | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Computer vision | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Predictive analytics | ✓ | ✗ |
| Generative AI | ✓ | ✓ |
| Data engineering | ✓ | ✓ |
| Staff augmentation | ✗ | ✓ |
Tech stack comparison: DataRoot Labs vs EPAM Systems
| Framework / platform | DataRoot Labs | EPAM Systems |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| Scikit-Learn | ✓ | N/A |
| LangChain | N/A | N/A |
| AWS SageMaker | N/A | ✓ |
| Azure ML | N/A | ✓ |
| GCP Vertex AI | N/A | N/A |
| Kubernetes | N/A | ✓ |
| Apache Spark | N/A | ✓ |
| MLflow | N/A | ✓ |
Pricing comparison: DataRoot Labs vs EPAM Systems
| Criterion | DataRoot Labs | EPAM Systems |
|---|---|---|
| Minimum engagement | $15K | $50K |
| Engagement models | Fixed project, T&M, Retainer | Dedicated team, T&M, Fixed project, Staff augmentation |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: DataRoot Labs vs EPAM Systems
| Dimension | DataRoot Labs | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, fintech, media | financial services, healthcare, retail |
| Best use cases | ML strategy and AI roadmap development for startups entering their first ML programme, Custom ML model development and integration for SaaS product differentiation | Global enterprise AI transformation programme requiring multi-country deployment and governance, Complex Fortune 500 ML programme integrating across dozens of legacy systems |
| Typical project type | Fixed project | Dedicated team |
DataRoot Labs vs EPAM Systems: pros and cons
| DataRoot Labs | |
|---|---|
| + | Strategy plus engineering in one team — avoids handoff friction between advisory and implementation |
| + | Low minimum engagement ($15K) makes sophisticated ML advisory accessible to seed-stage companies |
| + | Recognized as one of Ukraine's top ML firms with strong ecosystem reputation |
| + | Retainer model for ongoing AI advisory — suited to organizations building long-term ML capability |
| + | Generative AI integration capability alongside classical ML for modern startup architectures |
| - | Smaller team of 50–100 limits concurrent capacity — not suited to large-scale parallel programmes |
| - | Ukraine-based delivery introduces operational risk considerations for long-term programme dependencies |
| - | Less Western market brand visibility than US or Western European competitors |
| EPAM Systems | |
|---|---|
| + | 62,000+ engineers provides unmatched scale for simultaneous large-scale enterprise ML programmes |
| + | Publicly traded NYSE company with audited financials — maximum organizational stability and governance |
| + | Global delivery across 50+ countries enables ML delivery under local data sovereignty requirements |
| + | Full AI lifecycle from strategy through production MLOps within one organizational relationship |
| + | Fortune 500 client base validates enterprise-grade ML delivery at the highest complexity level |
| - | Enterprise scale means ML projects go through larger organizational process — slower initiation than boutiques |
| - | High minimum engagement ($50K) limits accessibility for SMBs or early-stage organizations |
| - | Generalist technology engineering scope means ML specialist depth may be lower per individual than pure-play ML boutiques |
Who should choose DataRoot Labs?
DataRoot Labs is the right choice for startups and scale-ups needing AI strategy alongside execution, with accessible starting budgets and a boutique consultancy approach.
One of Ukraine's most recognized ML consultancies — combining strategy-level AI advisory with hands-on engineering, a combination rare at this team size and price point. Minimum engagement starts at $15K. Works best with clients in SaaS, fintech, media, healthcare, logistics.
Who should choose EPAM Systems?
EPAM Systems is the right choice for large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration.
62,000+ engineers across 50+ countries delivering ML inside a full-service technology engineering operation — unmatched scale and compliance depth for global enterprise AI programmes. Minimum engagement starts at $50K. Works best with clients in financial services, healthcare, retail, media, government.
Decision matrix: DataRoot Labs vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DataRoot Labs |
| You need a large dedicated team for an ongoing programme | EPAM Systems |
| Your budget is at the lower end | DataRoot Labs |
| You need specialist depth in a specific vertical | DataRoot Labs |
| You need staff augmentation or team extension | EPAM Systems |
| You need consulting before committing to a build | DataRoot Labs |
Use case fit: DataRoot Labs vs EPAM Systems
| Use case | DataRoot Labs fit | EPAM Systems fit | Winner |
|---|---|---|---|
| ML strategy and AI roadmap development for startups entering their first ML programme | Strong | Strong | Both equally |
| Custom ML model development and integration for SaaS product differentiation | Strong | Limited | DataRoot Labs |
| Global enterprise AI transformation programme requiring multi-country deployment and governance | Limited | Strong | EPAM Systems |
| Complex Fortune 500 ML programme integrating across dozens of legacy systems | Limited | Strong | EPAM Systems |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs EPAM Systems
EPAM Systems (3.9/5) is the stronger overall choice for most Machine Learning Development projects. 62,000+ engineers across 50+ countries delivering ML inside a full-service technology engineering operation — unmatched scale and compliance depth for global enterprise AI programmes. It is best for large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration.
DataRoot Labs (3.8/5) is the better choice when startups and scale-ups needing AI strategy alongside execution, with accessible starting budgets and a boutique consultancy approach. If your situation matches those criteria, DataRoot Labs is a competitive option.
Related comparisons
DataRoot Labs vs EPAM Systems FAQ
Is DataRoot Labs better than EPAM Systems?
EPAM Systems (3.9/5) scores higher overall, but "better" depends on your use case. DataRoot Labs is better for startups and scale-ups needing AI strategy alongside execution, with accessible starting budgets and a boutique consultancy approach. EPAM Systems is better for large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration.
How do DataRoot Labs and EPAM Systems differ in pricing?
DataRoot Labs uses fixed project, t&m, retainer pricing with a minimum engagement of $15K. EPAM Systems uses dedicated team, t&m, fixed project, staff augmentation pricing with a minimum engagement of $50K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: DataRoot Labs or EPAM Systems?
EPAM Systems is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between DataRoot Labs and EPAM Systems?
DataRoot Labs's primary differentiator is: one of ukraine's most recognized ml consultancies — combining strategy-level ai advisory with hands-on engineering, a combination rare at this team size and price point. EPAM Systems's primary differentiator is: 62,000+ engineers across 50+ countries delivering ml inside a full-service technology engineering operation — unmatched scale and compliance depth for global enterprise ai programmes. They also differ in team size (50–100 vs 62,000+), minimum engagement ($15K vs $50K), and primary industries served (SaaS, fintech vs financial services, healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.