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 enterprises wanting AI consulting paired directly with engineering delivery. DataArt is the stronger option for enterprises in finance or healthcare needing AI consulting 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 | Engineering-heavy consulting model, pairing strategy advisors with the technical build team | 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 dates to 1993, co-founded in New Jersey and Minsk by Arkadiy Dobkin and Leo Lozner, and has traded on the NYSE as an S&P 500 constituent since 2012. It employed roughly 62,850 people across more than 55 countries at the end of 2025. AI consulting and transformation engineering is a marketed practice area, distinguished from pure Big Four strategy shops by EPAM's engineering-heavy delivery model, pairing advisory work directly with the technical staff who build the resulting systems.
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. The firm delivers data, analytics, and AI consulting for finance, media and entertainment, healthcare, 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 consulting is delivered as part of a broader software engineering practice.
Services and capabilities: EPAM Systems vs DataArt
| Capability | EPAM Systems | DataArt |
|---|---|---|
| AI strategy consulting | ✓ | ✓ |
| Generative AI | ✓ | ✗ |
| Machine learning | ✓ | ✗ |
| Data engineering | ✗ | ✓ |
| MLOps | ✓ | ✓ |
| Fixed-price projects | ✗ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: EPAM Systems vs DataArt
| Framework / platform | EPAM Systems | DataArt |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | N/A |
| Kubernetes | ✓ | ✓ |
| LangChain | N/A | N/A |
| PyTorch | N/A | N/A |
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 strategy engagement that needs to transition directly into technical build with the same team., Needing a publicly-traded vendor for audit or procurement compliance reasons. | Getting an AI strategy assessment for finance or healthcare clients with strict compliance needs., Running a long-term AI consulting 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 consultancy on this list can match. |
| + | Engineering-heavy delivery model avoids the strategy-to-build handoff gap common at pure consultancies. |
| + | Scale to staff several large AI consulting and build programs across regions simultaneously. |
| + | S&P 500 membership lets enterprise procurement teams vet it through standard due diligence. |
| - | AI consulting 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 consulting grounded in solid data foundations. |
| - | AI consulting 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 strategy engagement that needs to transition directly into technical build with the same team.
Engineering-heavy consulting model, pairing strategy advisors with the technical build team. 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: getting an AI strategy assessment 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 strategy engagement that needs to transition directly into technical build with the same team. | Strong | Strong | Both equally |
| Needing a publicly-traded vendor for audit or procurement compliance reasons. | Strong | Strong | Both equally |
| Getting an AI strategy assessment for finance or healthcare clients with strict compliance needs. | Limited | Strong | DataArt |
| Running a long-term AI consulting and data engineering program with a financially established vendor. | Strong | Strong | Both equally |
| Fixed-price project | Limited | Limited | Both equally |
| Dedicated team model | Limited | Limited | Both equally |
Verdict: EPAM Systems vs DataArt
EPAM Systems (4.1/5) is the stronger overall choice for most AI Consulting projects. Engineering-heavy consulting model, pairing strategy advisors with the technical build team.
DataArt (3.9/5) is worth a look if you need running a long-term AI consulting 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 consultancy 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 company before shortlisting.
What are the main differences between EPAM Systems and DataArt?
EPAM Systems's primary differentiator is: engineering-heavy consulting model, pairing strategy advisors with the technical build team. 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 company before making a decision.