EPAM Systems vs DataArt: full comparison for 2026
Quick verdict
EPAM Systems (4.6/5) edges ahead of DataArt (4.0/5) overall. EPAM Systems is the better choice for large enterprises, regulated industries, multi-team AI programs. DataArt is the stronger option for finance and healthcare firms extending data and AI teams. 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, USA | New York, New York, USA |
| Team size | 61,000+ | 5,000+ |
| Rating | 4.6 / 5 | 4.0 / 5 |
| Primary differentiator | Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes | Nearly three decades of domain work in finance, healthcare and travel |
| Pricing model | Time and materials for augmented engineers; dedicated team and managed program contracts; rates on request | Time and materials; dedicated teams; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Spark, Databricks |
| Industries served | Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing | Financial services, Healthcare & life sciences, Travel, Media |
EPAM Systems vs DataArt: overview
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with roughly 61,000 employees across delivery centers in Europe, the Americas and Asia. It is a public company listed on the New York Stock Exchange. On its Q2 2026 earnings call, management said EPAM had more than 5,700 Anthropic-certified engineers and was among the five largest certified partners worldwide, with AI-native work making up about 11% of revenue. EPAM employs its engineers directly and sells them as augmented capacity, dedicated teams or managed programs, though most large accounts end up in the managed model.
DataArt
DataArt was founded in New York in 1997 by Eugene Goland, who still leads it. Reported headcount ranges from about 4,000 to more than 6,000 across 30 to 40 locations. The firm builds data, analytics and AI platforms and works heavily in finance, healthcare and travel. Clients can bring in DataArt engineers as part of their own team or contract a full delivery team.
Services and capabilities: EPAM Systems vs DataArt
| Capability | EPAM Systems | DataArt |
|---|---|---|
| LLM / GenAI engineers | ✓ | ✗ |
| MLOps & deployment | ✓ | ✗ |
| Computer vision | ✗ | ✗ |
| Data engineering | ✓ | ✓ |
| AI agent development | ✓ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Risk-free trial period | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
Tech stack comparison: EPAM Systems vs DataArt
| Framework / platform | EPAM Systems | DataArt |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Databricks | ✓ | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: EPAM Systems vs DataArt
| Criterion | EPAM Systems | DataArt |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Full-time dedicated engineers, Dedicated team, Managed delivery |
| 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 | Mid-market to enterprise | Startup to mid-market |
| Best industries | Financial services, Healthcare & life sciences, Retail & e-commerce | Financial services, Healthcare & life sciences, Travel |
| Best use cases | Adding a 15-person GenAI squad to a bank's existing platform team, Rolling out agentic workflows across several business units under one master agreement | Extending a trading firm's data team with ML engineers, Building a clinical data platform before adding models |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
EPAM Systems vs DataArt: pros and cons
| EPAM Systems | |
|---|---|
| + | A Q2 2026 earnings call put its Anthropic-certified engineer count above 5,700, the largest verified GenAI bench on this list |
| + | Public-company reporting, audited financials and mature security reviews make vendor onboarding easier at banks and insurers |
| + | Can staff ten or more AI engineers in parallel across several time zones without running out of senior people |
| + | Deep data-platform practice means LLM work and the data engineering under it can come from one supplier |
| - | Rates are among the highest on this list and are only shared after scoping |
| - | Small requests for one or two engineers rarely get the same attention as large programs |
| - | Engagements tend to drift toward managed delivery, which moves decisions away from your own team |
| DataArt | |
|---|---|
| + | Deep domain knowledge in regulated sectors |
| + | Strong data-platform engineering supports AI work |
| + | Long client relationships suggest stable delivery |
| - | AI specialists are a small share of a broad workforce |
| - | Headcount figures vary considerably between sources |
| - | Engagements often lean toward managed delivery |
Who should choose EPAM Systems?
A typical fit: adding a 15-person GenAI squad to a bank's existing platform team.
Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing.
Who should choose DataArt?
A typical fit: extending a trading firm's data team with ML engineers.
Nearly three decades of domain work in finance, healthcare and travel. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare & life sciences, Travel, Media.
Decision matrix: EPAM Systems vs DataArt
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; EPAM Systems rates higher overall |
| You want the supplier to own delivery as well as staffing | Both; EPAM Systems rates higher overall |
| You need one expert part-time | Neither lists part-time experts; ask about reduced hours |
| You want to test an engineer before signing for months | Neither publishes a trial; ask for a short first term |
| Your budget is at the lower end | Compare: EPAM Systems (Not published) vs DataArt (Not published) |
| You need overlap with U.S. working hours | Neither is nearshore; agree overlap hours up front |
| You need specialist depth in a specific vertical | EPAM Systems |
Use case fit: EPAM Systems vs DataArt
| Use case | EPAM Systems fit | DataArt fit | Winner |
|---|---|---|---|
| Adding a 15-person GenAI squad to a bank's existing platform team | Strong | Strong | Both equally |
| Rolling out agentic workflows across several business units under one master agreement | Strong | Limited | EPAM Systems |
| Extending a trading firm's data team with ML engineers | Limited | Strong | DataArt |
| Building a clinical data platform before adding models | Limited | Strong | DataArt |
Verdict: EPAM Systems vs DataArt
EPAM Systems (4.6/5) is the stronger overall choice for most AI Staff Augmentation projects. Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes.
DataArt (4.0/5) is worth a look if you need building a clinical data platform before adding models. 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 (4.6/5) scores higher overall, but "better" depends on your use case. EPAM Systems's strongest advantage: a Q2 2026 earnings call put its Anthropic-certified engineer count above 5,700, the largest verified GenAI bench on this list. DataArt's strongest advantage: deep domain knowledge in regulated sectors.
How do EPAM Systems and DataArt differ in pricing?
EPAM Systems uses time and materials for augmented engineers; dedicated team and managed program contracts; rates on request pricing. DataArt uses time and materials; dedicated teams; rates on request 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: thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. DataArt's primary differentiator is: nearly three decades of domain work in finance, healthcare and travel. They also differ in team size (61,000+ vs 5,000+), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Healthcare & life sciences vs Financial services, Healthcare & life sciences).
Verify all details directly with each company before making a decision.