DataArt vs EPAM Systems: full comparison for 2026
Quick verdict
DataArt (4.0/5) edges ahead of EPAM Systems (3.9/5) overall. DataArt is the better choice for financial and travel firms needing long-lived dedicated teams. EPAM Systems is the stronger option for global enterprises with large, compliance-heavy AI programs. 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, USA | Newtown, Pennsylvania, USA |
| Team size | 5,000–6,000 | 62,850 |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Dedicated development centers with nearly 30 years of history | Scale, compliance maturity and vendor certifications |
| Pricing model | Dedicated development center; time and materials; rates on request | Enterprise time and materials; dedicated teams; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, Azure ML, AWS | Claude, OpenAI, Gemini |
| Industries served | Fintech, Travel, Healthcare, Media | Fintech, Healthcare, Retail, Manufacturing, Travel |
DataArt vs EPAM Systems: overview
DataArt
DataArt was founded in New York in 1997 by Eugene Goland and now employs between 5,000 and 6,000 people across more than 40 locations. Its Dedicated Development Center model staffs a team that works on one client's project only. An AI/ML group is actively hiring, with recent roles including a lead AI/ML engineer for an HR copilot built for a French SaaS client.
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with about 62,850 employees as of June 30, 2026, of whom roughly 56,650 work in delivery. It reports more than 5,700 Claude-certified engineers and set a target of 10,000, along with thousands of OpenAI- and Gemini-certified specialists. The company is targeting $600 million in AI-native services revenue for 2026. Its model is enterprise delivery, so individual staff augmentation usually sits inside a larger program.
Services and capabilities: DataArt vs EPAM Systems
| Capability | DataArt | EPAM Systems |
|---|---|---|
| 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: DataArt vs EPAM Systems
| Framework / platform | DataArt | EPAM Systems |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | ✓ |
| AWS SageMaker | N/A | ✓ |
| Azure ML | ✓ | ✓ |
| Databricks | ✓ | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: DataArt vs EPAM Systems
| Criterion | DataArt | EPAM Systems |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Managed delivery | Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
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, Travel, Healthcare | Fintech, Healthcare, Retail |
| Best use cases | Setting up a long-term dedicated team that includes ML engineers, Adding an LLM engineer to a SaaS copilot project | Staffing a multi-team GenAI program at a global bank, Adding certified Claude engineers to an enterprise AI platform |
| Typical project type | Dedicated team | Dedicated team |
DataArt vs EPAM Systems: pros and cons
| DataArt | |
|---|---|
| + | Long-running dedicated teams with low churn |
| + | Strong presence in finance and travel |
| + | Wide location choice |
| - | Built for multi-year centers more than quick single hires |
| - | AI/ML group is still growing |
| - | Enterprise pricing |
| EPAM Systems | |
|---|---|
| + | Largest bench on this list, with security and compliance processes to match |
| + | Thousands of engineers certified on major model platforms |
| + | Can staff any role an AI program needs |
| - | Built for enterprise programs; a single-engineer request is a poor fit |
| - | Highest overhead and slowest procurement on this list |
| - | Rates not published |
Who should choose DataArt?
A typical fit: setting up a long-term dedicated team that includes ML engineers.
Dedicated development centers with nearly 30 years of history. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Travel, Healthcare, Media.
Who should choose EPAM Systems?
A typical fit: staffing a multi-team GenAI program at a global bank.
Scale, compliance maturity and vendor certifications. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Healthcare, Retail, Manufacturing, Travel.
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 | Compare: DataArt (Not disclosed) vs EPAM Systems (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 | Both may offer discovery engagements |
Use case fit: DataArt vs EPAM Systems
| Use case | DataArt fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Setting up a long-term dedicated team that includes ML engineers | Strong | Limited | DataArt |
| Adding an LLM engineer to a SaaS copilot project | Strong | Strong | Both equally |
| Staffing a multi-team GenAI program at a global bank | Limited | Strong | EPAM Systems |
| Adding certified Claude engineers to an enterprise AI platform | Strong | Strong | Both equally |
Verdict: DataArt vs EPAM Systems
DataArt (4.0/5) is the stronger overall choice for most AI Staffing projects. Dedicated development centers with nearly 30 years of history.
EPAM Systems (3.9/5) is worth a look if you need adding certified Claude engineers to an enterprise AI platform. If your situation matches that, EPAM Systems is a competitive option.
Related comparisons
DataArt vs EPAM Systems FAQ
Is DataArt better than EPAM Systems?
DataArt (4.0/5) scores higher overall, but "better" depends on your use case. DataArt's strongest advantage: long-running dedicated teams with low churn. EPAM Systems's strongest advantage: largest bench on this list, with security and compliance processes to match.
How do DataArt and EPAM Systems differ in pricing?
DataArt uses dedicated development center; time and materials; rates on request pricing. EPAM Systems uses enterprise 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: DataArt or EPAM Systems?
DataArt is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.
What are the main differences between DataArt and EPAM Systems?
DataArt's primary differentiator is: dedicated development centers with nearly 30 years of history. EPAM Systems's primary differentiator is: Scale, compliance maturity and vendor certifications. They also differ in team size (5,000–6,000 vs 62,850), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Fintech, Travel vs Fintech, Healthcare).
Verify all details directly with each agency before making a decision.