EPAM Systems vs DataArt: full comparison for 2026
Last updated: July 2026
Quick verdict
EPAM Systems (4.0/5) edges ahead of DataArt (3.9/5) overall. EPAM Systems is the better choice for large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner.. DataArt is the stronger option for regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks.. 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, Pennsylvania, United States | New York, New York, United States |
| Team size | 50,000+ | 6,000+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Best for | Large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner. | Regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks. |
| Pricing model | Time & materials, managed engagement | Time & materials, managed engagement |
| Min. engagement | $100,000+ | Not published |
| Primary tech stack | AWS SageMaker, Azure ML, Databricks | Python, AWS, Azure |
| Industries served | FinTech, Healthcare, Retail & E-commerce, Manufacturing, Telecom | FinTech, Media & Entertainment, Healthcare, Retail & E-commerce, Travel & Hospitality |
EPAM Systems vs DataArt: overview
EPAM Systems
EPAM Systems, Inc. (NYSE: EPAM) has operated since 1993 and has become one of the largest global digital transformation and engineering services providers, with a workforce in the tens of thousands. Its AI development services span generative AI, machine learning consulting, and intelligent automation, delivered by consultants, designers, and engineers who have worked with AI technologies for decades, and Clutch lists a minimum project size of $100,000+ with $150–$199/hr average rates. As a large publicly traded firm, EPAM offers the deepest compliance and financial transparency in this list, at a correspondingly higher entry price point.
DataArt
DataArt was founded in 1997 in New York City by Eugene Goland and has grown to more than 6,000 engineers across 40+ locations in the US, UK, Europe, Latin America, India, and the Middle East. The firm delivers data, analytics, and AI platforms for finance, media, healthcare, retail, and travel clients, built around Artisyn, its AI-enabled operating model that embeds AI agents and governance frameworks across the software development lifecycle, including regulated industries. Clients cited on its Clutch profile include Priceline, Ocado Technology, Legal & General, and Flutter Entertainment.
Services and capabilities: EPAM Systems vs DataArt
| Capability | EPAM Systems | DataArt |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✓ | ✗ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✓ | ✓ |
Tech stack comparison: EPAM Systems vs DataArt
| Framework / platform | EPAM Systems | DataArt |
|---|---|---|
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| Kubernetes | ✓ | ✓ |
Pricing comparison: EPAM Systems vs DataArt
| Criterion | EPAM Systems | DataArt |
|---|---|---|
| Minimum engagement | $100,000+ | Not published |
| Engagement models | Managed engagement, Time & materials, Staff augmentation | Managed engagement, Time & materials, Dedicated team |
| Rate transparency | Minimum disclosed | 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 | FinTech, Healthcare, Retail & E-commerce | FinTech, Media & Entertainment, Healthcare |
| Best use cases | Large enterprise with a $100K+ budget needs a publicly traded vendor for AI/ML procurement compliance requirements., Fortune 500 company needs generative AI deployed at global scale with responsible-AI governance built in. | Regulated financial services or healthcare company needs AI delivery with a built-in governance framework., Enterprise wants a vendor with named, publicly referenceable clients like Priceline and Legal & General. |
| Typical project type | Managed engagement | Managed engagement |
EPAM Systems vs DataArt: pros and cons
| EPAM Systems | |
|---|---|
| + | Publicly traded on the NYSE, giving clients access to audited financial disclosures unavailable from private competitors. |
| + | 50,000+ global workforce provides essentially unlimited delivery capacity for the largest enterprise AI programs. |
| + | 31+ years of engineering history (since 1993) predates the current AI hiring wave by decades. |
| + | AI/generative AI practice spans strategy through production deployment and responsible-AI compliance, covering the full enterprise lifecycle. |
| + | Scale/compliance standout among the researched companies — the clearest choice for regulated, large-budget enterprise programs. |
| - | $100,000+ minimum project size (per Clutch) puts EPAM out of reach for startups and mid-market budgets under six figures. |
| - | $150–$199/hr rate band is among the highest in this list, reflecting large-firm overhead. |
| - | At 50,000+ employees, AI/ML is one practice among dozens — clients should confirm they're getting a dedicated AI pod, not a generalist team. |
| DataArt | |
|---|---|
| + | Named enterprise clients (Priceline, Ocado Technology, Legal & General, Flutter Entertainment) are independently verifiable via public case studies. |
| + | 27+ years of operating history (since 1997) gives it one of the longer track records in this list. |
| + | Artisyn operating model specifically addresses AI governance for regulated industries like financial services and healthcare, a genuine differentiator. |
| + | 6,000+ engineers across 40+ global locations provide substantial delivery capacity and geographic flexibility. |
| - | At 6,000+ employees, engagements are structured around managed delivery rather than close founder-level involvement. |
| - | AI/ML is one of several core service lines (alongside broader data/analytics platform work), not the firm's exclusive focus. |
Who should choose EPAM Systems?
EPAM Systems is the right choice for large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner..
Public-company (NYSE: EPAM) scale and compliance rigor, with 30+ years of engineering history predating the AI wave.. Minimum engagement starts at $100,000+. Works best with clients in FinTech, Healthcare, Retail & E-commerce, Manufacturing, Telecom.
Who should choose DataArt?
DataArt is the right choice for regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks..
Artisyn, a proprietary AI-enabled operating model embedding governance and AI agents across the delivery lifecycle.. Minimum engagement starts at Not published. Works best with clients in FinTech, Media & Entertainment, Healthcare, Retail & E-commerce, 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 | DataArt |
| Your budget is at the lower end | Compare: EPAM Systems ($100,000+) vs DataArt (Not published) |
| You need specialist depth in a specific vertical | EPAM Systems |
| You need production MLOps support after model launch | EPAM Systems |
| 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 |
|---|---|---|---|
| Large enterprise with a $100K+ budget needs a publicly traded vendor for AI/ML procurement compliance requirements. | Strong | Strong | Both equally |
| Fortune 500 company needs generative AI deployed at global scale with responsible-AI governance built in. | Strong | Limited | EPAM Systems |
| Regulated financial services or healthcare company needs AI delivery with a built-in governance framework. | Strong | Strong | Both equally |
| Enterprise wants a vendor with named, publicly referenceable clients like Priceline and Legal & General. | Strong | Strong | Both equally |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: EPAM Systems vs DataArt
EPAM Systems (4.0/5) is the stronger overall choice for most Machine Learning Development projects. Public-company (NYSE: EPAM) scale and compliance rigor, with 30+ years of engineering history predating the AI wave.. It is best for large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner..
DataArt (3.9/5) is the better choice when regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks.. If your situation matches those criteria, DataArt is a competitive option.
Related comparisons
EPAM Systems vs DataArt FAQ
Is EPAM Systems better than DataArt?
EPAM Systems (4.0/5) scores higher overall, but "better" depends on your use case. EPAM Systems is better for large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner.. DataArt is better for regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks..
How do EPAM Systems and DataArt differ in pricing?
EPAM Systems uses time & materials, managed engagement pricing with a minimum engagement of $100,000+. DataArt uses time & materials, managed engagement pricing with a minimum engagement of Not published. 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: public-company (nyse: epam) scale and compliance rigor, with 30+ years of engineering history predating the ai wave.. DataArt's primary differentiator is: artisyn, a proprietary ai-enabled operating model embedding governance and ai agents across the delivery lifecycle.. They also differ in team size (50,000+ vs 6,000+), minimum engagement ($100,000+ vs Not published), and primary industries served (FinTech, Healthcare vs FinTech, Media & Entertainment).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.