EPAM Systems vs DataArt: full comparison for 2026
Quick verdict
EPAM Systems (4.1/5) edges ahead of DataArt (3.9/5) overall. EPAM Systems is the better choice for global enterprises running AI programs at massive scale. DataArt is the stronger option for enterprises in finance or healthcare needing AI at global scale. The right choice depends on your project size, budget, and required tech stack.
EPAM Systems vs DataArt: head-to-head summary
| Criterion | EPAM Systems | DataArt |
|---|---|---|
| Founded | 1993 | 1997 |
| HQ | Newtown, United States | New York, United States |
| Team size | 62,000+ | 5,700+ |
| Rating | 4.1 / 5 | 3.9 / 5 |
| Primary differentiator | Public-company scale (NYSE: EPAM) with financial transparency few competitors offer | Nearly 30 years of engineering history across 30-plus global delivery locations |
| Pricing model | Retainer or dedicated team, enterprise contracting | Dedicated team or retainer |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, AWS, Azure | Python, AWS, Azure |
| Industries served | Financial services, Healthcare, Retail & e-commerce, Media & entertainment | Financial services, Healthcare, Media & entertainment, Travel & hospitality |
EPAM Systems vs DataArt: overview
EPAM Systems
EPAM Systems traces back to 1993, founded jointly in New Jersey and Minsk by Arkadiy Dobkin and Leo Lozner, and has been an S&P 500 constituent trading on the NYSE since 2012. By the end of 2025 the company employed roughly 62,850 people across more than 55 countries, a scale that puts it in an entirely different category from any other firm on this list. AI transformation engineering is one of its marketed practice areas, but at this size it operates as part of a much larger digital engineering and cloud transformation business rather than a standalone specialty.
DataArt
DataArt goes back to 1997, founded by Eugene Goland, and is headquartered in New York City with roughly 5,700 employees spread across more than 30 locations in the US, Europe, the UK, Latin America, and the UAE. The firm delivers data, analytics, and AI platforms for finance, media and entertainment, healthcare and life sciences, retail, and travel and hospitality clients. Nearly three decades of history gives it a longer track record than almost every other firm here, though AI is delivered as part of a broader software engineering practice rather than a standalone specialty.
Services and capabilities: EPAM Systems vs DataArt
| Capability | EPAM Systems | DataArt |
|---|---|---|
| Generative AI | ✓ | ✗ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✓ | ✓ |
| AI consulting | ✓ | ✗ |
| Fixed-price projects | ✗ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: EPAM Systems vs DataArt
| Framework / platform | EPAM Systems | DataArt |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Kubernetes | ✓ | ✓ |
Pricing comparison: EPAM Systems vs DataArt
| Criterion | EPAM Systems | DataArt |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Retainer | Dedicated team, Retainer |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs DataArt
| Dimension | EPAM Systems | DataArt |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Healthcare, Retail & e-commerce | Financial services, Healthcare, Media & entertainment |
| Best use cases | Running an AI transformation program spanning multiple business units and regions at once., Needing a publicly-traded vendor for audit or procurement compliance reasons. | Building AI-driven analytics platforms for finance or healthcare clients with strict compliance needs., Running a long-term AI and data engineering program with a financially established vendor. |
| Typical project type | Dedicated team | Dedicated team |
EPAM Systems vs DataArt: pros and cons
| EPAM Systems | |
|---|---|
| + | Public-company financial disclosure that no private firm on this list can match. |
| + | Enough scale to staff several large AI programs across regions simultaneously. |
| + | S&P 500 membership means enterprise procurement teams can vet it through standard due diligence. |
| + | Partnerships across all three major cloud hyperscalers. |
| - | AI sits inside an enormous engineering business rather than functioning as a dedicated specialty |
| - | Scale generally means slower onboarding and higher minimum engagement than boutique firms |
| DataArt | |
|---|---|
| + | Nearly three decades of software engineering history, among the longest reviewed here. |
| + | 5,700-plus employees across 30-plus locations globally. |
| + | Named industry focus areas (finance, healthcare, travel) show real vertical depth. |
| + | Data and analytics platform experience supports AI work that needs solid data foundations. |
| - | AI sits inside a much broader software engineering practice rather than being the firm's core identity |
| - | Enterprise scale typically means slower onboarding than smaller, more agile AI boutiques |
Who should choose EPAM Systems?
A typical fit: running an AI transformation program spanning multiple business units and regions at once.
Public-company scale (NYSE: EPAM) with financial transparency few competitors offer. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Media & entertainment.
Who should choose DataArt?
A typical fit: building AI-driven analytics platforms for finance or healthcare clients with strict compliance needs.
Nearly 30 years of engineering history across 30-plus global delivery locations. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Media & entertainment, Travel & hospitality.
Decision matrix: EPAM Systems vs DataArt
| 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 | EPAM Systems |
| Your budget is at the lower end | Compare: EPAM Systems (Not disclosed) vs DataArt (Not disclosed) |
| You need specialist depth in a specific vertical | EPAM Systems |
| 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: EPAM Systems vs DataArt
| Use case | EPAM Systems fit | DataArt fit | Winner |
|---|---|---|---|
| Running an AI transformation program spanning multiple business units and regions at once. | Strong | Strong | Both equally |
| Needing a publicly-traded vendor for audit or procurement compliance reasons. | Strong | Strong | Both equally |
| Building AI-driven analytics platforms for finance or healthcare clients with strict compliance needs. | Limited | Strong | DataArt |
| Running a long-term AI and data engineering program with a financially established vendor. | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: EPAM Systems vs DataArt
EPAM Systems (4.1/5) is the stronger overall choice for most AI Development projects. Public-company scale (NYSE: EPAM) with financial transparency few competitors offer.
DataArt (3.9/5) is worth a look if you need running a long-term AI and data engineering program with a financially established vendor. If your situation matches that, DataArt is a competitive option.
Related comparisons
EPAM Systems vs DataArt FAQ
Is EPAM Systems better than DataArt?
EPAM Systems (4.1/5) scores higher overall, but "better" depends on your use case. EPAM Systems's strongest advantage: public-company financial disclosure that no private firm on this list can match. DataArt's strongest advantage: nearly three decades of software engineering history, among the longest reviewed here.
How do EPAM Systems and DataArt differ in pricing?
EPAM Systems uses retainer or dedicated team, enterprise contracting pricing. DataArt uses dedicated team or retainer pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: EPAM Systems or DataArt?
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 firm before shortlisting.
What are the main differences between EPAM Systems and DataArt?
EPAM Systems's primary differentiator is: public-company scale (NYSE: EPAM) with financial transparency few competitors offer. DataArt's primary differentiator is: nearly 30 years of engineering history across 30-plus global delivery locations. They also differ in team size (62,000+ vs 5,700+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Financial services, Healthcare vs Financial services, Healthcare).
Verify all details directly with each firm before making a decision.