InData Labs vs EPAM Systems: full comparison for 2026
Quick verdict
InData Labs (4.1/5) edges ahead of EPAM Systems (4.1/5) overall. InData Labs is the better choice for teams needing data science depth behind a generative AI build. EPAM Systems is the stronger option for global enterprises running generative AI at massive scale. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs EPAM Systems: head-to-head summary
| Criterion | InData Labs | EPAM Systems |
|---|---|---|
| Founded | 2014 | 1993 |
| HQ | Limassol, Cyprus | Newtown, United States |
| Team size | 51-200 | 62,000+ |
| Rating | 4.1 / 5 | 4.1 / 5 |
| Primary differentiator | Data-science-first heritage predating the generative AI branding wave | Public-company scale (NYSE: EPAM) with financial transparency few competitors offer |
| Pricing model | Fixed project or dedicated team | Retainer or dedicated team, enterprise contracting |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, OpenAI API, TensorFlow | Python, OpenAI API, AWS |
| Industries served | Retail & e-commerce, Gaming, Fintech, Healthcare | Financial services, Healthcare, Retail & e-commerce, Media & entertainment |
InData Labs vs EPAM Systems: overview
InData Labs
InData Labs was founded in 2014 by gaming-industry veteran Marat Karpeko and is headquartered in Cyprus, with additional offices reported in Lithuania and the US. Staff estimates swing between roughly 65 and 200 across sources. Its practice centers on data science, predictive analytics, natural language processing, and computer vision, with generative AI layered onto that foundation rather than replacing it, positioning it closer to a data-first consultancy than a generative-AI-branded agency.
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. Generative AI transformation engineering is a marketed practice area, but at this size it functions as part of a much larger digital engineering business rather than a standalone specialty.
Services and capabilities: InData Labs vs EPAM Systems
| Capability | InData Labs | EPAM Systems |
|---|---|---|
| Generative AI | ✓ | ✓ |
| Machine learning | ✗ | ✗ |
| AI agents | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| AI consulting | ✗ | ✓ |
| Fixed-price projects | ✓ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: InData Labs vs EPAM Systems
| Framework / platform | InData Labs | EPAM Systems |
|---|---|---|
| Python | ✓ | ✓ |
| OpenAI API | ✓ | ✓ |
| PyTorch | N/A | N/A |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Kubernetes | N/A | N/A |
Pricing comparison: InData Labs vs EPAM Systems
| Criterion | InData Labs | EPAM Systems |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Fixed project, Dedicated team | Dedicated team, Retainer |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs EPAM Systems
| Dimension | InData Labs | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Retail & e-commerce, Gaming, Fintech | Financial services, Healthcare, Retail & e-commerce |
| Best use cases | Building a generative AI feature on top of an existing data warehouse., Adding computer vision alongside generative AI to a product with image or video data. | Running a generative AI transformation program spanning multiple business units and regions., Needing a publicly-traded vendor for audit or procurement compliance reasons. |
| Typical project type | Fixed project | Dedicated team |
InData Labs vs EPAM Systems: pros and cons
| InData Labs | |
|---|---|
| + | Founder's gaming background brings real-time data processing experience to computer vision work. |
| + | Cyprus headquarters (EU-based) can simplify GDPR-aligned handling for European clients. |
| + | Predictive analytics and NLP expertise predates the current generative AI wave. |
| + | More than a decade of track record in a narrower, more defensible specialty. |
| - | Reported team size varies close to 3x across public sources |
| - | Less generative AI-specific public case work than agencies built specifically around that |
| EPAM Systems | |
|---|---|
| + | Public-company financial disclosure that no private agency on this list can match. |
| + | Scale to staff several large generative 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. |
| - | Generative AI sits inside an enormous engineering business rather than as a dedicated specialty |
| - | Scale generally means slower onboarding and higher minimum engagement than boutique firms |
Who should choose InData Labs?
A typical fit: building a generative AI feature on top of an existing data warehouse.
Data-science-first heritage predating the generative AI branding wave. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Gaming, Fintech, Healthcare.
Who should choose EPAM Systems?
A typical fit: running a generative AI transformation program spanning multiple business units and regions.
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: InData Labs vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | InData Labs |
| You need a large dedicated team for an ongoing programme | InData Labs |
| Your budget is at the lower end | Compare: InData Labs (Not disclosed) vs EPAM Systems (Not disclosed) |
| You need specialist depth in a specific vertical | InData Labs |
| 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: InData Labs vs EPAM Systems
| Use case | InData Labs fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Building a generative AI feature on top of an existing data warehouse. | Strong | Limited | InData Labs |
| Adding computer vision alongside generative AI to a product with image or video data. | Strong | Limited | InData Labs |
| Running a generative AI transformation program spanning multiple business units and regions. | Strong | Strong | Both equally |
| 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: InData Labs vs EPAM Systems
InData Labs (4.1/5) is the stronger overall choice for most Generative AI Development projects. Data-science-first heritage predating the generative AI branding wave.
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
InData Labs vs EPAM Systems FAQ
Is InData Labs better than EPAM Systems?
InData Labs (4.1/5) scores higher overall, but "better" depends on your use case. InData Labs's strongest advantage: Founder's gaming background brings real-time data processing experience to computer vision work. EPAM Systems's strongest advantage: public-company financial disclosure that no private agency on this list can match.
How do InData Labs and EPAM Systems differ in pricing?
InData Labs uses fixed project or dedicated team 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: InData 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 InData Labs and EPAM Systems?
InData Labs's primary differentiator is: data-science-first heritage predating the generative AI branding wave. EPAM Systems's primary differentiator is: public-company scale (NYSE: EPAM) with financial transparency few competitors offer. They also differ in team size (51-200 vs 62,000+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Retail & e-commerce, Gaming vs Financial services, Healthcare).
Verify all details directly with each company before making a decision.