DataRoot Labs vs EPAM Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
DataRoot Labs (4.5/5) edges ahead of EPAM Systems (4.0/5) overall. DataRoot Labs is the better choice for startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer.. EPAM Systems is the stronger option for large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner.. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs EPAM Systems: head-to-head summary
| Criterion | DataRoot Labs | EPAM Systems |
|---|---|---|
| Founded | 2016 | 1993 |
| HQ | Kyiv, Ukraine | Newtown, Pennsylvania, United States |
| Team size | 27–50 | 50,000+ |
| Rating | 4.5 / 5 | 4.0 / 5 |
| Best for | Startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer. | Large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner. |
| Pricing model | Project-based, dedicated team | Time & materials, managed engagement |
| Min. engagement | Not published | $100,000+ |
| Primary tech stack | Python, PyTorch, Hugging Face | AWS SageMaker, Azure ML, Databricks |
| Industries served | Startups (cross-industry), FinTech, Healthcare | FinTech, Healthcare, Retail & E-commerce, Manufacturing, Telecom |
DataRoot Labs vs EPAM Systems: overview
DataRoot Labs
DataRoot Labs was founded in 2016 in Kyiv, Ukraine and has worked exclusively in AI and R&D since inception, building generative AI, machine learning, and data engineering systems for startups and enterprises. The company is notably lean — roughly 27 employees across three continents as of late 2025 — and also runs DataRoot University, a free ML and data engineering school with more than 6,000 graduates, which doubles as its own technical talent pipeline. Its small size and academic ties make it a lower-cost, highly specialized option relative to larger regional peers.
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.
Services and capabilities: DataRoot Labs vs EPAM Systems
| Capability | DataRoot Labs | EPAM Systems |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✓ |
Tech stack comparison: DataRoot Labs vs EPAM Systems
| Framework / platform | DataRoot Labs | EPAM Systems |
|---|---|---|
| TensorFlow | N/A | N/A |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| Kubernetes | N/A | ✓ |
Pricing comparison: DataRoot Labs vs EPAM Systems
| Criterion | DataRoot Labs | EPAM Systems |
|---|---|---|
| Minimum engagement | Not published | $100,000+ |
| Engagement models | Project-based, Dedicated team | Managed engagement, Time & materials, Staff augmentation |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataRoot Labs vs EPAM Systems
| Dimension | DataRoot Labs | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Startups (cross-industry), FinTech, Healthcare | FinTech, Healthcare, Retail & E-commerce |
| Best use cases | Startup with a limited AI budget needs senior-level generative AI or ML engineering without enterprise agency overhead., Company wants a lean, R&D-focused partner for an experimental ML feature rather than a large staffing engagement. | 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. |
| Typical project type | Project-based | Managed engagement |
DataRoot Labs vs EPAM Systems: pros and cons
| DataRoot Labs | |
|---|---|
| + | Team of roughly 27 keeps overhead low, which typically translates into lower blended rates than 500+ person firms. |
| + | Exclusive AI/R&D focus since 2016 with no general software-development sideline diluting expertise. |
| + | DataRoot University (6,000+ graduates) gives the firm a homegrown, vetted junior-to-mid talent pipeline instead of relying purely on open-market hiring. |
| + | Cost/accessibility standout among the researched companies for startups with constrained AI budgets. |
| - | 27–50 person team size limits capacity for multiple large concurrent enterprise engagements. |
| - | Small headcount means less bench depth if a key engineer rotates off a project mid-engagement. |
| - | Thinner public enterprise case-study base than larger Ukraine-headquartered peers like N-iX or ELEKS. |
| 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. |
Who should choose DataRoot Labs?
DataRoot Labs is the right choice for startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer..
Runs its own free ML/data-engineering school (DataRoot University, 6,000+ graduates) as a self-built talent pipeline.. Minimum engagement starts at Not published. Works best with clients in Startups (cross-industry), FinTech, Healthcare.
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.
Decision matrix: DataRoot Labs 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 | DataRoot Labs |
| Your budget is at the lower end | Compare: DataRoot Labs (Not published) vs EPAM Systems ($100,000+) |
| 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: DataRoot Labs vs EPAM Systems
| Use case | DataRoot Labs fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Startup with a limited AI budget needs senior-level generative AI or ML engineering without enterprise agency overhead. | Strong | Limited | DataRoot Labs |
| Company wants a lean, R&D-focused partner for an experimental ML feature rather than a large staffing engagement. | Strong | Strong | Both equally |
| 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. | Limited | Strong | EPAM Systems |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs EPAM Systems
DataRoot Labs (4.5/5) is the stronger overall choice for most Machine Learning Development projects. Runs its own free ML/data-engineering school (DataRoot University, 6,000+ graduates) as a self-built talent pipeline.. It is best for startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer..
EPAM Systems (4.0/5) is the better choice when large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner.. If your situation matches those criteria, EPAM Systems is a competitive option.
Related comparisons
DataRoot Labs vs EPAM Systems FAQ
Is DataRoot Labs better than EPAM Systems?
DataRoot Labs (4.5/5) scores higher overall, but "better" depends on your use case. DataRoot Labs is better for startups and lean teams that want direct access to senior ML engineers at boutique pricing without a large account layer.. EPAM Systems is better for large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner..
How do DataRoot Labs and EPAM Systems differ in pricing?
DataRoot Labs uses project-based, dedicated team pricing with a minimum engagement of Not published. EPAM Systems uses time & materials, managed engagement pricing with a minimum engagement of $100,000+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: DataRoot Labs or EPAM Systems?
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 DataRoot Labs and EPAM Systems?
DataRoot Labs's primary differentiator is: runs its own free ml/data-engineering school (dataroot university, 6,000+ graduates) as a self-built talent pipeline.. EPAM Systems's primary differentiator is: public-company (nyse: epam) scale and compliance rigor, with 30+ years of engineering history predating the ai wave.. They also differ in team size (27–50 vs 50,000+), minimum engagement (Not published vs $100,000+), and primary industries served (Startups (cross-industry), FinTech vs FinTech, Healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.