DataRoot Labs vs EPAM Systems: full comparison for 2026
Quick verdict
DataRoot Labs (4.4/5) edges ahead of EPAM Systems (4.1/5) overall. DataRoot Labs is the better choice for startups needing applied ML research capacity. EPAM Systems is the stronger option for global enterprises running AI programs at massive scale. 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, United States |
| Team size | 11-50 | 62,000+ |
| Rating | 4.4 / 5 | 4.1 / 5 |
| Primary differentiator | Research-oriented engagement style built for startup speed, not enterprise procurement | Public-company scale (NYSE: EPAM) with financial transparency few competitors offer |
| Pricing model | Dedicated team or fixed project | Retainer or dedicated team, enterprise contracting |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, PyTorch, scikit-learn | Python, AWS, Azure |
| Industries served | Healthtech, Fintech, Retail & e-commerce | Financial services, Healthcare, Retail & e-commerce, Media & entertainment |
DataRoot Labs vs EPAM Systems: overview
DataRoot Labs
DataRoot Labs runs out of Kyiv and has focused on applied data science research since founding in 2016. Public staff counts vary widely, from about 11 to nearly 200 depending on the source, likely a function of how contractors get counted differently across trackers. What's consistent is the specialty: machine learning models, computer vision pipelines, and hands-on AI R&D for startups that need research capability without hiring a full internal team.
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, a scale category no other agency on this list approaches. AI transformation engineering is a marketed practice area, but at this size it functions as part of a much larger digital engineering and cloud transformation business rather than a standalone specialty.
Services and capabilities: DataRoot Labs vs EPAM Systems
| Capability | DataRoot Labs | EPAM Systems |
|---|---|---|
| Generative AI | ✗ | ✓ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| AI consulting | ✓ | ✓ |
| Fixed-price projects | ✓ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: DataRoot Labs vs EPAM Systems
| Framework / platform | DataRoot Labs | EPAM Systems |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Kubernetes | N/A | ✓ |
Pricing comparison: DataRoot Labs vs EPAM Systems
| Criterion | DataRoot Labs | EPAM Systems |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Fixed project | Dedicated team, Retainer |
| Rate transparency | Not public | Not public |
| 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 | Healthtech, Fintech, Retail & e-commerce | Financial services, Healthcare, Retail & e-commerce |
| Best use cases | Standing up an ML proof of concept ahead of a seed round., Getting a second, independent build on a computer vision pipeline. | Running an AI transformation program spanning multiple business units and regions at once., Needing a publicly-traded vendor for audit or procurement compliance reasons. |
| Typical project type | Dedicated team | Dedicated team |
DataRoot Labs vs EPAM Systems: pros and cons
| DataRoot Labs | |
|---|---|
| + | Research culture suits startups needing genuine experimentation over templated builds. |
| + | Small team keeps direct communication between founders and the engineers doing the work. |
| + | Kyiv talent pool offers strong ML fundamentals at lower cost than US or Western European teams. |
| + | Named computer vision projects back up the firm's stated specialty. |
| - | Employee counts differ substantially across public sources, making capacity hard to verify |
| - | Little public evidence of enterprise-scale delivery experience |
| EPAM Systems | |
|---|---|
| + | Public-company financial disclosure that no private agency on this list can match. |
| + | Scale to staff several large AI programs across regions simultaneously. |
| + | S&P 500 membership lets enterprise procurement teams vet it through standard due diligence. |
| + | Partnerships span all three major cloud hyperscalers. |
| - | AI 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 |
Who should choose DataRoot Labs?
A typical fit: standing up an ML proof of concept ahead of a seed round.
Research-oriented engagement style built for startup speed, not enterprise procurement. Minimum engagement is not publicly disclosed. Works best with clients in Healthtech, Fintech, Retail & e-commerce.
Who should choose EPAM Systems?
A typical fit: running an AI transformation program spanning multiple business units and regions at once.
Public-company scale (NYSE: EPAM) with financial transparency few competitors offer. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Media & entertainment.
Decision matrix: DataRoot Labs vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DataRoot Labs |
| You need a large dedicated team for an ongoing programme | DataRoot Labs |
| Your budget is at the lower end | Compare: DataRoot Labs (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 | DataRoot Labs |
Use case fit: DataRoot Labs vs EPAM Systems
| Use case | DataRoot Labs fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Standing up an ML proof of concept ahead of a seed round. | Strong | Limited | DataRoot Labs |
| Getting a second, independent build on a computer vision pipeline. | Strong | Limited | DataRoot Labs |
| Running an AI transformation program spanning multiple business units and regions at once. | Limited | Strong | EPAM Systems |
| Needing a publicly-traded vendor for audit or procurement compliance reasons. | Limited | Strong | EPAM Systems |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs EPAM Systems
DataRoot Labs (4.4/5) is the stronger overall choice for most AI Development projects. Research-oriented engagement style built for startup speed, not enterprise procurement.
EPAM Systems (4.1/5) is worth a look if you need needing a publicly-traded vendor for audit or procurement compliance reasons. If your situation matches that, EPAM Systems is a competitive option.
Related comparisons
DataRoot Labs vs EPAM Systems FAQ
Is DataRoot Labs better than EPAM Systems?
DataRoot Labs (4.4/5) scores higher overall, but "better" depends on your use case. DataRoot Labs's strongest advantage: research culture suits startups needing genuine experimentation over templated builds. EPAM Systems's strongest advantage: public-company financial disclosure that no private agency on this list can match.
How do DataRoot Labs and EPAM Systems differ in pricing?
DataRoot Labs uses dedicated team or fixed project pricing. EPAM Systems uses retainer or dedicated team, enterprise contracting pricing. 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 agency before shortlisting.
What are the main differences between DataRoot Labs and EPAM Systems?
DataRoot Labs's primary differentiator is: research-oriented engagement style built for startup speed, not enterprise procurement. EPAM Systems's primary differentiator is: public-company scale (NYSE: EPAM) with financial transparency few competitors offer. They also differ in team size (11-50 vs 62,000+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthtech, Fintech vs Financial services, Healthcare).
Verify all details directly with each agency before making a decision.