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 fortune 500 buyers needing AI at global engineering scale. InData Labs is the stronger option for teams needing data science depth before an AI product 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 AI folded into a much larger engineering practice | Data-science-first practice rather than a generative-AI-branded service line |
| 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 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.
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. Employee figures vary from roughly 65 to 200 across different trackers, which is common for firms that mix core staff with project-based contractors. The company's practice centers on data science consulting: predictive analytics, natural language processing, computer vision, and big data analytics, positioned as a data-first alternative to firms that lead with generative AI branding.
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 that spans multiple business units at once., Needing a publicly-traded vendor for procurement or audit reasons. | Building predictive models from an existing data warehouse or event stream., Adding computer vision to a product that already generates image or video data. |
| Typical project type | Dedicated team | Fixed project |
EPAM Systems vs InData Labs: pros and cons
| 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 |
| InData Labs | |
|---|---|
| + | Founder's gaming-industry background brings real-time data experience to computer vision work. |
| + | EU-based headquarters (Cyprus) can simplify GDPR-aligned data handling for European clients. |
| + | Predictive analytics and NLP depth predate the generative AI hype cycle. |
| + | Decade-plus track record in a narrower, more defensible specialty than broad AI consulting. |
| - | Reported team size varies close to 3x across public sources |
| - | Less public-facing generative AI and LLM case work than firms built around that specifically |
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.
Who should choose InData Labs?
A typical fit: building predictive models from an existing data warehouse or event stream.
Data-science-first practice rather than a generative-AI-branded service line. 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 that spans multiple business units at once. | Strong | Strong | Both equally |
| Needing a publicly-traded vendor for procurement or audit 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 generates 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 AI folded into a much larger engineering practice.
InData Labs (4.1/5) is worth a look if you need adding computer vision to a product that already generates 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 transparency and stability that private firms on this list can't match. InData Labs's strongest advantage: Founder's gaming-industry background brings real-time data 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 company 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 AI folded into a much larger engineering practice. InData Labs's primary differentiator is: data-science-first practice rather than a generative-AI-branded service line. 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 company before making a decision.