EPAM Systems vs InData Labs: full comparison for 2026
Quick verdict
EPAM Systems (4.1/5) edges ahead of InData Labs (4.1/5) overall. EPAM Systems is the better choice for global enterprises running AI programs at massive scale. InData Labs is the stronger option for teams needing data science depth before an AI build. The right choice depends on your project size, budget, and required tech stack.
EPAM Systems vs InData Labs: head-to-head summary
| Criterion | EPAM Systems | InData Labs |
|---|---|---|
| Founded | 1993 | 2014 |
| HQ | Newtown, United States | Limassol, Cyprus |
| Team size | 62,000+ | 51-200 |
| Rating | 4.1 / 5 | 4.1 / 5 |
| Primary differentiator | Public-company scale (NYSE: EPAM) with financial transparency few competitors offer | Data-science-first heritage that predates the generative AI branding wave |
| Pricing model | Retainer or dedicated team, enterprise contracting | Fixed project or dedicated team |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, AWS, Azure | Python, scikit-learn, TensorFlow |
| Industries served | Financial services, Healthcare, Retail & e-commerce, Media & entertainment | Retail & e-commerce, Gaming, Fintech, Healthcare |
EPAM Systems vs InData Labs: overview
EPAM Systems
EPAM Systems traces back to 1993, founded jointly in New Jersey and Minsk by Arkadiy Dobkin and Leo Lozner, and has been an S&P 500 constituent trading on the NYSE since 2012. By the end of 2025 the company employed roughly 62,850 people across more than 55 countries, a scale that puts it in an entirely different category from any other firm on this list. AI transformation engineering is one of its marketed practice areas, but at this size it operates as part of a much larger digital engineering and cloud transformation business rather than a standalone specialty.
InData Labs
InData Labs traces its founding to 2014 and gaming-industry veteran Marat Karpeko, with headquarters in Cyprus and additional offices reported in Lithuania and the US. Reported staff counts swing between roughly 65 and 200 across different trackers, common for firms mixing core employees with project-based contractors. The firm's practice centers on data science: predictive analytics, natural language processing, computer vision, and large-scale data analytics, positioning it closer to a data-first consultancy than a generative-AI-branded shop.
Services and capabilities: EPAM Systems vs InData Labs
| Capability | EPAM Systems | InData Labs |
|---|---|---|
| Generative AI | ✓ | ✗ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✓ | ✗ |
| AI consulting | ✓ | ✗ |
| Fixed-price projects | ✗ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: EPAM Systems vs InData Labs
| Framework / platform | EPAM Systems | InData Labs |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | N/A | N/A |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: EPAM Systems vs InData Labs
| Criterion | EPAM Systems | InData Labs |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Retainer | Fixed project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs InData Labs
| Dimension | EPAM Systems | InData Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Healthcare, Retail & e-commerce | Retail & e-commerce, Gaming, Fintech |
| Best use cases | Running an AI transformation program spanning multiple business units and regions at once., Needing a publicly-traded vendor for audit or procurement compliance reasons. | Building predictive models from an existing data warehouse or event stream., Adding computer vision to a product that already produces image or video data. |
| Typical project type | Dedicated team | Fixed project |
EPAM Systems vs InData Labs: pros and cons
| EPAM Systems | |
|---|---|
| + | Public-company financial disclosure that no private firm on this list can match. |
| + | Enough scale to staff several large AI programs across regions simultaneously. |
| + | S&P 500 membership means enterprise procurement teams can vet it through standard due diligence. |
| + | Partnerships across 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 |
| InData Labs | |
|---|---|
| + | Founder's gaming background brings real-time data processing experience to computer vision work. |
| + | Cyprus headquarters (EU-based) can simplify GDPR-aligned data 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 and LLM-specific public case work than firms built specifically around that |
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.
Who should choose InData Labs?
A typical fit: building predictive models from an existing data warehouse or event stream.
Data-science-first heritage that predates the generative AI branding wave. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Gaming, Fintech, Healthcare.
Decision matrix: EPAM Systems vs InData Labs
| 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 | EPAM Systems |
| Your budget is at the lower end | Compare: EPAM Systems (Not disclosed) vs InData 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 InData Labs
| Use case | EPAM Systems fit | InData Labs fit | Winner |
|---|---|---|---|
| Running an AI transformation program spanning multiple business units and regions at once. | Strong | Strong | Both equally |
| Needing a publicly-traded vendor for audit or procurement compliance reasons. | Strong | Limited | EPAM Systems |
| Building predictive models from an existing data warehouse or event stream. | Limited | Strong | InData Labs |
| Adding computer vision to a product that already produces image or video data. | Limited | Strong | InData Labs |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: EPAM Systems vs InData Labs
EPAM Systems (4.1/5) is the stronger overall choice for most AI Development projects. Public-company scale (NYSE: EPAM) with financial transparency few competitors offer.
InData Labs (4.1/5) is worth a look if you need adding computer vision to a product that already produces image or video data. If your situation matches that, InData Labs is a competitive option.
Related comparisons
EPAM Systems vs InData Labs FAQ
Is EPAM Systems better than InData 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 no private firm on this list can match. InData Labs's strongest advantage: Founder's gaming background brings real-time data processing experience to computer vision work.
How do EPAM Systems and InData Labs differ in pricing?
EPAM Systems uses retainer or dedicated team, enterprise contracting pricing. InData Labs uses fixed project or dedicated team 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 InData 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 firm before shortlisting.
What are the main differences between EPAM Systems and InData Labs?
EPAM Systems's primary differentiator is: public-company scale (NYSE: EPAM) with financial transparency few competitors offer. InData Labs's primary differentiator is: data-science-first heritage that predates the generative AI branding wave. They also differ in team size (62,000+ vs 51-200), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Financial services, Healthcare vs Retail & e-commerce, Gaming).
Verify all details directly with each firm before making a decision.