EPAM Systems vs DataRoot Labs: full comparison for 2026
Quick verdict
EPAM Systems (4.1/5) edges ahead of DataRoot Labs (3.9/5) overall. EPAM Systems is the better choice for enterprises wanting AI advisory paired directly with engineering delivery. DataRoot Labs is the stronger option for startups needing applied AI research capacity. The right choice depends on your project size, budget, and required tech stack.
EPAM Systems vs DataRoot Labs: head-to-head summary
| Criterion | EPAM Systems | DataRoot Labs |
|---|---|---|
| Founded | 1993 | 2016 |
| HQ | Newtown, United States | Kyiv, Ukraine |
| Team size | 62,000+ | 11-50 |
| Rating | 4.1 / 5 | 3.9 / 5 |
| Primary differentiator | Engineering-heavy advisory where strategists and the build team sit together | A research-oriented engagement style built for startup speed, not enterprise procurement |
| Pricing model | Retainer or dedicated team, enterprise contracting | Dedicated team or fixed project |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, AWS, Azure | Python, PyTorch, scikit-learn |
| Industries served | Financial services, Healthcare, Retail & e-commerce, Media & entertainment | Healthtech, Fintech, Retail & e-commerce |
EPAM Systems vs DataRoot Labs: overview
EPAM Systems
EPAM Systems was co-founded in 1993 in New Jersey and Minsk by Arkadiy Dobkin and Leo Lozner, and it's been an S&P 500 constituent on the NYSE since 2012. By the end of 2025 it employed roughly 62,850 people across more than 55 countries. Its AI advisory and transformation engineering work runs as a company-wide practice, and what separates it from a typical Big Four strategy firm is that its advisors sit directly alongside the technical staff who build what gets recommended, rather than handing off to a separate delivery team.
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, likely a function of how contractors get counted differently across trackers. Its work centers on machine learning models, computer vision pipelines, and hands-on AI research and development for startups that need real research capability and technical AI advisory without hiring a full internal team.
Services and capabilities: EPAM Systems vs DataRoot Labs
| Capability | EPAM Systems | DataRoot Labs |
|---|---|---|
| AI strategy consulting | ✓ | ✓ |
| Generative AI | ✓ | ✗ |
| Machine learning | ✓ | ✓ |
| Data engineering | ✗ | ✓ |
| MLOps | ✓ | ✗ |
| Fixed-price projects | ✗ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: EPAM Systems vs DataRoot Labs
| Framework / platform | EPAM Systems | DataRoot Labs |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | ✓ | N/A |
| Kubernetes | ✓ | N/A |
| LangChain | N/A | N/A |
| PyTorch | N/A | ✓ |
Pricing comparison: EPAM Systems vs DataRoot Labs
| Criterion | EPAM Systems | DataRoot Labs |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Retainer | Dedicated team, Fixed project |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs DataRoot Labs
| Dimension | EPAM Systems | DataRoot Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Healthcare, Retail & e-commerce | Healthtech, Fintech, Retail & e-commerce |
| Best use cases | Running an AI strategy engagement that needs to move straight into technical build with the same team., Needing a publicly-traded vendor for audit or procurement compliance reasons. | Getting an independent AI strategy assessment ahead of a seed round., Bringing in dedicated research capacity for a specific AI question a small team can't resolve alone. |
| Typical project type | Dedicated team | Dedicated team |
EPAM Systems vs DataRoot Labs: pros and cons
| EPAM Systems | |
|---|---|
| + | Public-company financial disclosure that a privately held agency simply can't offer. |
| + | Advisors and builders sit together, avoiding the strategy-to-build handoff gap common at pure advisory firms. |
| + | Enough scale to run several large AI advisory and build programs across regions simultaneously. |
| + | S&P 500 membership lets enterprise procurement run standard financial due diligence. |
| - | AI advisory sits inside an enormous engineering business rather than as its own dedicated specialty |
| - | Enterprise scale generally means slower onboarding and a higher minimum than boutique agencies |
| DataRoot Labs | |
|---|---|
| + | A research culture suits startups needing genuine experimentation over templated builds. |
| + | A small team keeps direct communication between founders and the engineers doing the work. |
| + | Kyiv's talent pool offers strong ML fundamentals at lower cost than US or Western European teams. |
| + | Named computer vision projects back up the agency's stated specialty. |
| - | Employee counts differ substantially across public sources, making capacity hard to verify |
| - | Little public evidence of enterprise-scale delivery experience |
Who should choose EPAM Systems?
A typical fit: running an AI strategy engagement that needs to move straight into technical build with the same team.
Engineering-heavy advisory where strategists and the build team sit together. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Media & entertainment.
Who should choose DataRoot Labs?
A typical fit: getting an independent AI strategy assessment ahead of a seed round.
A 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.
Decision matrix: EPAM Systems vs DataRoot Labs
| 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 | EPAM Systems |
| Your budget is at the lower end | Compare: EPAM Systems (Not disclosed) vs DataRoot Labs (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 | EPAM Systems |
Use case fit: EPAM Systems vs DataRoot Labs
| Use case | EPAM Systems fit | DataRoot Labs fit | Winner |
|---|---|---|---|
| Running an AI strategy engagement that needs to move straight into technical build with the same team. | Strong | Limited | EPAM Systems |
| Needing a publicly-traded vendor for audit or procurement compliance reasons. | Strong | Limited | EPAM Systems |
| Getting an independent AI strategy assessment ahead of a seed round. | Limited | Strong | DataRoot Labs |
| Bringing in dedicated research capacity for a specific AI question a small team can't resolve alone. | Limited | Strong | DataRoot Labs |
| Fixed-price project | Limited | Limited | Both equally |
| Dedicated team model | Limited | Strong | DataRoot Labs |
Verdict: EPAM Systems vs DataRoot Labs
EPAM Systems (4.1/5) is the stronger overall choice for most AI Consulting projects. Engineering-heavy advisory where strategists and the build team sit together.
DataRoot Labs (3.9/5) is worth a look if you need bringing in dedicated research capacity for a specific AI question a small team can't resolve alone. If your situation matches that, DataRoot Labs is a competitive option.
Related comparisons
EPAM Systems vs DataRoot Labs FAQ
Is EPAM Systems better than DataRoot Labs?
EPAM Systems (4.1/5) scores higher overall, but "better" depends on your use case. EPAM Systems's strongest advantage: public-company financial disclosure that a privately held agency simply can't offer. DataRoot Labs's strongest advantage: a research culture suits startups needing genuine experimentation over templated builds.
How do EPAM Systems and DataRoot Labs differ in pricing?
EPAM Systems uses retainer or dedicated team, enterprise contracting pricing. DataRoot Labs uses dedicated team or fixed project 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 DataRoot Labs?
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 EPAM Systems and DataRoot Labs?
EPAM Systems's primary differentiator is: engineering-heavy advisory where strategists and the build team sit together. DataRoot Labs's primary differentiator is: a research-oriented engagement style built for startup speed, not enterprise procurement. They also differ in team size (62,000+ vs 11-50), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Financial services, Healthcare vs Healthtech, Fintech).
Verify all details directly with each agency before making a decision.