DataArt vs EPAM Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
DataArt (3.6/5) edges ahead of EPAM Systems (3.5/5) overall. DataArt is the better choice for mid-market and enterprise companies in finance, healthcare, or media needing a large engineering firm with ML integrated into product delivery. EPAM Systems is the stronger option for large enterprises needing ML within large-scale platform engineering programmes and access to a proprietary AI orchestration platform. 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 | 6,000+ | 60,000+ |
| Rating | 3.6 / 5 | 3.5 / 5 |
| Best for | Mid-market and enterprise companies in finance, healthcare, or media needing a large engineering firm with ML integrated into product delivery | Large enterprises needing ML within large-scale platform engineering programmes and access to a proprietary AI orchestration platform |
| Pricing model | T&M, dedicated team | Dedicated team, T&M |
| Min. engagement | $50K | $200K+ |
| Primary tech stack | Python, Scikit-learn, TensorFlow | Python, EPAM DIAL, AWS |
| Industries served | Fintech, Healthcare, SaaS, Logistics, E-commerce | Fintech, Healthcare, Manufacturing, SaaS, Logistics |
DataArt vs EPAM Systems: overview
DataArt
DataArt is a global engineering firm founded in 1997 and headquartered in New York, New York, with 6,000+ specialists across 20+ countries. The firm's AI and ML services deliver solutions in predictive analytics, natural language processing, data mining, and computer vision, integrated within product engineering and digital transformation delivery. DataArt serves financial services, healthcare, media, travel, and technology clients. The firm operates with a flat structure emphasising direct engineer-to-client interaction over multi-layer account management.
EPAM Systems
EPAM Systems is a global software engineering and digital services company founded in 1993 and headquartered in Newtown, Pennsylvania, publicly listed on the NYSE with 62,000+ professionals across 55+ countries. The company's AI and ML services encompass data engineering, platform modernisation, advanced analytics, and AI/ML model development, alongside its proprietary EPAM DIAL enterprise AI orchestration platform. EPAM has positioned itself as a leader in AI transformation engineering, integrating ML capability within large digital product and platform engineering programmes.
Services and capabilities: DataArt vs EPAM Systems
| Capability | DataArt | EPAM Systems |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP & text analytics | ✓ | ✗ |
| MLOps & deployment | ✗ | ✓ |
| Generative AI | ✗ | ✓ |
| ML consulting & strategy | ✗ | ✓ |
| Staff augmentation | ✗ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: DataArt vs EPAM Systems
| Framework / platform | DataArt | EPAM Systems |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | ✓ |
| Scikit-learn | ✓ | N/A |
| AWS SageMaker | N/A | N/A |
| MLflow | N/A | ✓ |
| Hugging Face | N/A | N/A |
| LangChain | N/A | N/A |
| Docker/Kubernetes | N/A | N/A |
| Databricks | N/A | ✓ |
Pricing comparison: DataArt vs EPAM Systems
| Criterion | DataArt | EPAM Systems |
|---|---|---|
| Minimum engagement | $50K | $200K+ |
| Engagement models | Time & materials, Dedicated team | Dedicated team, Time & materials |
| 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 | Fintech, Healthcare, SaaS | Fintech, Healthcare, Manufacturing |
| Best use cases | NLP-powered document analysis for financial services compliance and reporting, Predictive analytics for healthcare patient risk stratification and monitoring | Enterprise AI transformation programmes for Fortune 500 organisations, EPAM DIAL deployment for enterprise LLM governance and AI orchestration |
| Typical project type | Time & materials | Dedicated team |
DataArt vs EPAM Systems: pros and cons
| DataArt | |
|---|---|
| + | 29-year engineering track record across financial services, healthcare, and media |
| + | 6,000+ specialists provide large programme delivery capacity across 20+ countries |
| + | Flat organisational structure provides direct senior ML engineer access on projects |
| + | Multi-country delivery network for global client timezone and language coverage |
| + | Strong NLP and predictive analytics capability within product engineering context |
| - | ML sits within a broad engineering firm — not a specialist ML company |
| - | T&M and dedicated team models less suited to clients seeking fixed-price delivery |
| - | Less emphasis on cutting-edge generative AI research than newer AI-first firms |
| EPAM Systems | |
|---|---|
| + | Publicly listed company provides financial transparency and governance confidence |
| + | 62,000+ engineers deliver at a scale few ML development competitors can match |
| + | Proprietary EPAM DIAL AI orchestration platform for enterprise LLM management |
| + | AI transformation engineering positioning beyond standard ML delivery |
| + | 55+ country footprint supports global enterprise programme delivery and compliance |
| - | Very high minimum engagement ($200K+) limits access to large enterprise budgets |
| - | ML is one capability within a massive engineering conglomerate — specialist depth varies by practice and team |
| - | Eastern European primary delivery requires business continuity planning for regulated clients |
Who should choose DataArt?
DataArt is the right choice for mid-market and enterprise companies in finance, healthcare, or media needing a large engineering firm with ML integrated into product delivery.
29-year global engineering firm with 6,000+ specialists and a flat structure providing direct access to senior ML engineers on client projects. Minimum engagement starts at $50K. Works best with clients in Fintech, Healthcare, SaaS, Logistics, E-commerce.
Who should choose EPAM Systems?
EPAM Systems is the right choice for large enterprises needing ML within large-scale platform engineering programmes and access to a proprietary AI orchestration platform.
Publicly traded 62,000-person firm with proprietary EPAM DIAL AI orchestration platform and AI transformation engineering positioning for global enterprises. Minimum engagement starts at $200K+. Works best with clients in Fintech, Healthcare, Manufacturing, SaaS, Logistics.
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 | DataArt |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | EPAM Systems |
Use case fit: DataArt vs EPAM Systems
| Use case | DataArt fit | EPAM Systems fit | Winner |
|---|---|---|---|
| NLP-powered document analysis for financial services compliance and reporting | Strong | Limited | DataArt |
| Predictive analytics for healthcare patient risk stratification and monitoring | Strong | Limited | DataArt |
| Enterprise AI transformation programmes for Fortune 500 organisations | Limited | Strong | EPAM Systems |
| EPAM DIAL deployment for enterprise LLM governance and AI orchestration | Limited | Strong | EPAM Systems |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: DataArt vs EPAM Systems
DataArt (3.6/5) is the stronger overall choice for most Machine Learning Development projects. 29-year global engineering firm with 6,000+ specialists and a flat structure providing direct access to senior ML engineers on client projects. It is best for mid-market and enterprise companies in finance, healthcare, or media needing a large engineering firm with ML integrated into product delivery.
EPAM Systems (3.5/5) is the better choice when large enterprises needing ML within large-scale platform engineering programmes and access to a proprietary AI orchestration platform. 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 (3.6/5) scores higher overall, but "better" depends on your use case. DataArt is better for mid-market and enterprise companies in finance, healthcare, or media needing a large engineering firm with ML integrated into product delivery. EPAM Systems is better for large enterprises needing ML within large-scale platform engineering programmes and access to a proprietary AI orchestration platform.
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 dedicated team, t&m pricing with a minimum engagement of $200K+. 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 company before shortlisting.
What are the main differences between DataArt and EPAM Systems?
DataArt's primary differentiator is: 29-year global engineering firm with 6,000+ specialists and a flat structure providing direct access to senior ml engineers on client projects. EPAM Systems's primary differentiator is: publicly traded 62,000-person firm with proprietary epam dial ai orchestration platform and ai transformation engineering positioning for global enterprises. They also differ in team size (6,000+ vs 60,000+), minimum engagement ($50K vs $200K+), and primary industries served (Fintech, Healthcare vs Fintech, Healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.