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 data-heavy startups needing applied ML research capacity. EPAM Systems is the stronger option for fortune 500 buyers needing AI at global engineering 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 | R&D-style engagement model built for startups, not enterprise procurement | Public company scale (NYSE: EPAM) with AI folded into a much larger engineering practice |
| 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 is a Kyiv-based data science and AI consulting company founded in 2016. Team size estimates vary by source, ranging from roughly 11 to 200 employees depending on whether contractors and R&D partners are counted, but the firm consistently positions itself around applied research and development for data science and AI-powered startups rather than broad enterprise IT outsourcing. Its focus stays narrow: machine learning models, computer vision pipelines, and AI R&D partnerships for companies that need a research-capable team without hiring one in-house.
EPAM Systems
EPAM Systems was founded in 1993 in New Jersey and Minsk by Arkadiy Dobkin and Leo Lozner, and has traded on the New York Stock Exchange since 2012 as a member of the S&P 500. The company employed roughly 62,850 people across more than 55 countries at the end of 2025, which puts it in a different capacity class from every other firm on this list. EPAM markets itself as a leader in AI transformation engineering, and its scale means AI work is one part of a much larger digital engineering and cloud transformation business rather than the whole of it.
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 a machine learning proof of concept before a startup's seed round closes., Getting a second opinion or independent build on a computer vision pipeline. | Running an AI transformation program that spans multiple business units at once., Needing a publicly-traded vendor for procurement or audit reasons. |
| Typical project type | Dedicated team | Dedicated team |
DataRoot Labs vs EPAM Systems: pros and cons
| DataRoot Labs | |
|---|---|
| + | Research-oriented culture suits startups that need genuine ML experimentation, not templated builds. |
| + | Small team keeps communication direct between founders and the engineers doing the work. |
| + | Kyiv talent pool gives strong ML fundamentals at lower rates than US or Western European firms. |
| + | Computer vision work is a genuine specialty backed by named client projects. |
| - | Reported employee counts vary widely by source, making true capacity hard to verify |
| - | Limited public information on enterprise-scale delivery experience |
| EPAM Systems | |
|---|---|
| + | Public-company financial transparency and stability that private firms on this list can't match. |
| + | Scale to staff multiple large AI programs across regions simultaneously. |
| + | S&P 500 membership signals enterprise procurement teams can vet it through standard due diligence. |
| + | Cloud partnerships span all three major hyperscalers. |
| - | AI is one line of business inside a much larger engineering firm, not a dedicated specialty |
| - | Enterprise scale typically means longer sales cycles and higher minimum engagement sizes than boutiques |
Who should choose DataRoot Labs?
A typical fit: standing up a machine learning proof of concept before a startup's seed round closes.
R&D-style engagement model built for startups, 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 that spans multiple business units at once.
Public company scale (NYSE: EPAM) with AI folded into a much larger engineering practice. 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 a machine learning proof of concept before a startup's seed round closes. | Strong | Limited | DataRoot Labs |
| Getting a second opinion or independent build on a computer vision pipeline. | Strong | Limited | DataRoot Labs |
| Running an AI transformation program that spans multiple business units at once. | Limited | Strong | EPAM Systems |
| Needing a publicly-traded vendor for procurement or audit 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. R&D-style engagement model built for startups, not enterprise procurement.
EPAM Systems (4.1/5) is worth a look if you need needing a publicly-traded vendor for procurement or audit 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-oriented culture suits startups that need genuine ML experimentation, not templated builds. EPAM Systems's strongest advantage: public-company financial transparency and stability that private firms on this list can't 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 company before shortlisting.
What are the main differences between DataRoot Labs and EPAM Systems?
DataRoot Labs's primary differentiator is: R&D-style engagement model built for startups, not enterprise procurement. EPAM Systems's primary differentiator is: public company scale (NYSE: EPAM) with AI folded into a much larger engineering practice. 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 company before making a decision.