DataArt vs EPAM Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
DataArt (3.9/5) edges ahead of EPAM Systems (3.9/5) overall. DataArt is the better choice for financial services, media, and healthcare enterprises needing ML embedded in complex software systems with long-term maintainability as a priority. EPAM Systems is the stronger option for large enterprises needing scale, global delivery coverage, and programme management infrastructure alongside ML engineering. The right choice depends on your project size, budget, and required tech stack.
DataArt vs EPAM Systems: head-to-head summary
| Criterion | DataArt | EPAM Systems |
|---|---|---|
| Founded | 1997 | 1993 |
| HQ | New York, NY, USA | Newtown, PA, USA |
| Team size | 5,000+ | 58,000+ |
| Rating | 3.9 / 5 | 3.9 / 5 |
| Best for | Financial services, media, and healthcare enterprises needing ML embedded in complex software systems with long-term maintainability as a priority | Large enterprises needing scale, global delivery coverage, and programme management infrastructure alongside ML engineering |
| Pricing model | T&M, Dedicated team | T&M, Dedicated team |
| Min. engagement | $50K | $100K |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, TensorFlow, PyTorch |
| Industries served | Financial Services, Media / Entertainment, Healthcare, Hospitality / Travel, Technology / SaaS | Financial Services, Healthcare, Technology / SaaS, Media / Entertainment, Logistics, Retail / E-commerce |
DataArt vs EPAM Systems: overview
DataArt
DataArt is a global technology consultancy founded in 1997, headquartered in New York, with over 5,000 engineers across 30+ offices worldwide. Its ML practice specialises in building custom machine learning systems that integrate into broader software platforms, with particular strength in capital markets (time series forecasting, trading analytics), media (content recommendation, NLP), healthcare (clinical analytics, EHR integration), and travel and hospitality. DataArt emphasises system stability, long-term maintainability, and performance — qualities that reflect its origins as a software engineering firm rather than a data science startup, producing ML systems designed to remain operational and auditable over multi-year production lifespans.
EPAM Systems
EPAM Systems is a global digital transformation services company founded in 1993 and headquartered in Newtown, Pennsylvania, with over 58,000 professionals worldwide. It was ranked among the top three tech and AI companies on Glassdoor's Best Places to Work 2026. EPAM's AI and ML practice covers custom ML development, data engineering, generative AI, MLOps, and staff augmentation, delivered across financial services, healthcare, media, SaaS, and logistics. EPAM is best suited to enterprises needing a large-scale delivery partner with the governance, compliance, and programme management infrastructure of a major consultancy at software engineering rates.
Services and capabilities: DataArt vs EPAM Systems
| Capability | DataArt | EPAM Systems |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Deep learning | ✗ | ✗ |
| NLP / Text analytics | ✓ | ✗ |
| Computer vision | ✓ | ✗ |
| MLOps & deployment | ✗ | ✓ |
| Generative AI | ✗ | ✓ |
| AI strategy | ✓ | ✓ |
| Staff augmentation | ✗ | ✓ |
| Fixed-price projects | ✗ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: DataArt vs EPAM Systems
| Framework / platform | DataArt | EPAM Systems |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Kubernetes | ✓ | ✓ |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
Pricing comparison: DataArt vs EPAM Systems
| Criterion | DataArt | EPAM Systems |
|---|---|---|
| Minimum engagement | $50K | $100K |
| Engagement models | Time & materials, Dedicated team | Time & materials, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: DataArt vs EPAM Systems
| Dimension | DataArt | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial Services, Media / Entertainment, Healthcare | Financial Services, Healthcare, Technology / SaaS |
| Best use cases | Time series forecasting and trading analytics ML for capital markets and asset management firms, Content recommendation systems embedded in media and streaming platforms | Enterprise-scale ML platform build requiring 50+ engineers across data engineering, ML, and MLOps tracks simultaneously, Global digital transformation programmes embedding ML into enterprise software at multiple business units |
| Typical project type | Time & materials | Time & materials |
DataArt vs EPAM Systems: pros and cons
| DataArt | |
|---|---|
| + | 25+ years of operation and 5,000+ engineers provide exceptional vendor stability for long-duration enterprise programmes |
| + | Software engineering DNA produces ML systems built for long-term production operation rather than quick demos |
| + | Capital markets ML depth (time series, trading analytics, risk modelling) is among the strongest in this review |
| + | Media and healthcare ML secondary strengths add versatility for conglomerates spanning multiple verticals |
| + | Well-established offshore-onshore delivery model provides competitive blended rates with senior onshore oversight |
| - | ML is one practice within a very broad 5,000-person portfolio — specialist AI research depth is thinner than dedicated ML firms |
| - | Engineering-first approach can feel slower than ML-native boutiques for clients needing rapid iteration or experimentation |
| - | Less prominent in marketing or commercial AI use cases compared to analytics-native competitors |
| EPAM Systems | |
|---|---|
| + | 58,000+ engineers provide unmatched concurrent delivery capacity for large-scale enterprise ML programmes |
| + | Glassdoor top-3 Best Tech & AI Company 2026 reflects high engineering talent quality and retention |
| + | Full global delivery footprint enables follow-the-sun support and multi-geography data processing compliance |
| + | Strong programme management and governance infrastructure reduces enterprise delivery risk on complex projects |
| + | ML capability combined with broader digital transformation services reduces vendor proliferation for enterprise buyers |
| - | $100K minimum and large-firm overhead pricing makes EPAM less suitable for mid-market or startup buyers |
| - | ML specialisation depth is diluted by the breadth of a 58,000-person general technology firm |
| - | Large firm bureaucracy and account management layers can slow decision-making on agile ML projects |
Who should choose DataArt?
DataArt is the right choice for financial services, media, and healthcare enterprises needing ML embedded in complex software systems with long-term maintainability as a priority.
Software-engineering-first culture produces ML systems designed for 5-10 year production lifespans — maintainability and stability over speed-to-market. Minimum engagement starts at $50K. Works best with clients in Financial Services, Media / Entertainment, Healthcare, Hospitality / Travel, Technology / SaaS.
Who should choose EPAM Systems?
EPAM Systems is the right choice for large enterprises needing scale, global delivery coverage, and programme management infrastructure alongside ML engineering.
Global scale with 58,000+ engineers and top-3 Glassdoor AI company ranking — rare ML delivery capacity for simultaneous large enterprise programmes. Minimum engagement starts at $100K. Works best with clients in Financial Services, Healthcare, Technology / SaaS, Media / Entertainment, Logistics, Retail / E-commerce.
Decision matrix: DataArt vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | DataArt |
| Your budget is at the lower end | DataArt |
| You need specialist depth in a specific vertical | EPAM Systems |
| You need staff augmentation or team extension | EPAM Systems |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: DataArt vs EPAM Systems
| Use case | DataArt fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Time series forecasting and trading analytics ML for capital markets and asset management firms | Strong | Limited | DataArt |
| Content recommendation systems embedded in media and streaming platforms | Strong | Limited | DataArt |
| Enterprise-scale ML platform build requiring 50+ engineers across data engineering, ML, and MLOps tracks simultaneously | Limited | Strong | EPAM Systems |
| Global digital transformation programmes embedding ML into enterprise software at multiple business units | Limited | Strong | EPAM Systems |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Strong | EPAM Systems |
Verdict: DataArt vs EPAM Systems
DataArt (3.9/5) is the stronger overall choice for most Machine Learning projects. Software-engineering-first culture produces ML systems designed for 5-10 year production lifespans — maintainability and stability over speed-to-market. It is best for financial services, media, and healthcare enterprises needing ML embedded in complex software systems with long-term maintainability as a priority.
EPAM Systems (3.9/5) is the better choice when large enterprises needing scale, global delivery coverage, and programme management infrastructure alongside ML engineering. If your situation matches those criteria, EPAM Systems is a competitive option.
Related comparisons
DataArt vs EPAM Systems FAQ
Is DataArt better than EPAM Systems?
DataArt (3.9/5) scores higher overall, but "better" depends on your use case. DataArt is better for financial services, media, and healthcare enterprises needing ML embedded in complex software systems with long-term maintainability as a priority. EPAM Systems is better for large enterprises needing scale, global delivery coverage, and programme management infrastructure alongside ML engineering.
How do DataArt and EPAM Systems differ in pricing?
DataArt uses t&m, dedicated team pricing with a minimum engagement of $50K. EPAM Systems uses t&m, dedicated team pricing with a minimum engagement of $100K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: DataArt 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 agency before shortlisting.
What are the main differences between DataArt and EPAM Systems?
DataArt's primary differentiator is: software-engineering-first culture produces ml systems designed for 5-10 year production lifespans — maintainability and stability over speed-to-market. EPAM Systems's primary differentiator is: global scale with 58,000+ engineers and top-3 glassdoor ai company ranking — rare ml delivery capacity for simultaneous large enterprise programmes. They also differ in team size (5,000+ vs 58,000+), minimum engagement ($50K vs $100K), and primary industries served (Financial Services, Media / Entertainment vs Financial Services, Healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.