EPAM Systems vs InData Labs: full comparison for 2026
Quick verdict
EPAM Systems (4.1/5) edges ahead of InData Labs (3.9/5) overall. EPAM Systems is the better choice for enterprises wanting AI advisory paired directly with engineering delivery. InData Labs is the stronger option for teams needing data science advisory 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 | 3.9 / 5 |
| Primary differentiator | An engineering-heavy advisory model that pairs strategists with the actual build team | A data-science-first heritage predating 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 was co-founded in 1993 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. AI advisory and transformation engineering runs as a marketed practice across the firm, distinguished from pure Big Four strategy firms by EPAM's engineering-heavy delivery model: advisors sit alongside the technical staff who actually build what gets recommended.
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 advisory, predictive analytics, natural language processing, and computer vision, positioning it closer to a data-first agency than a generative-AI-branded competitor.
Services and capabilities: EPAM Systems vs InData Labs
| Capability | EPAM Systems | InData Labs |
|---|---|---|
| AI strategy consulting | ✓ | ✓ |
| Generative AI | ✓ | ✗ |
| Machine learning | ✓ | ✓ |
| Data engineering | ✗ | ✓ |
| MLOps | ✓ | ✗ |
| Fixed-price projects | ✗ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: EPAM Systems vs InData Labs
| Framework / platform | EPAM Systems | InData Labs |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | ✓ | N/A |
| Kubernetes | ✓ | N/A |
| LangChain | N/A | N/A |
| PyTorch | N/A | 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 strategy engagement that needs to move directly into technical build with the same team., Needing a publicly-traded agency for audit or procurement compliance reasons. | Getting a data science advisory assessment before committing to a full AI build., Adding computer vision strategy 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 privately held agency on this list can match. |
| + | The engineering-heavy delivery model avoids the strategy-to-build handoff gap common at pure advisory firms. |
| + | Enough scale to staff several large AI advisory and build programs across regions at once. |
| + | S&P 500 membership lets enterprise procurement teams vet the firm through standard due diligence. |
| - | AI advisory sits inside an enormous engineering business rather than functioning as a dedicated specialty |
| - | Enterprise scale generally means slower onboarding and a higher minimum engagement than boutique agencies |
| InData Labs | |
|---|---|
| + | The founder's gaming background brings real-time data processing experience to computer vision work. |
| + | A 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 agencies built specifically around that |
Who should choose EPAM Systems?
A typical fit: running an AI strategy engagement that needs to move directly into technical build with the same team.
An engineering-heavy advisory model that pairs strategists with the actual build team. 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: getting a data science advisory assessment before committing to a full AI build.
A 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.
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 strategy engagement that needs to move directly into technical build with the same team. | Strong | Strong | Both equally |
| Needing a publicly-traded agency for audit or procurement compliance reasons. | Strong | Limited | EPAM Systems |
| Getting a data science advisory assessment before committing to a full AI build. | Limited | Strong | InData Labs |
| Adding computer vision strategy to a product that already produces image or video data. | Limited | Strong | InData Labs |
| Fixed-price project | Limited | Limited | Both equally |
| Dedicated team model | Limited | Limited | Both equally |
Verdict: EPAM Systems vs InData Labs
EPAM Systems (4.1/5) is the stronger overall choice for most AI Consulting projects. An engineering-heavy advisory model that pairs strategists with the actual build team.
InData Labs (3.9/5) is worth a look if you need adding computer vision strategy 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 privately held agency on this list can match. InData Labs's strongest advantage: the 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 agency before shortlisting.
What are the main differences between EPAM Systems and InData Labs?
EPAM Systems's primary differentiator is: an engineering-heavy advisory model that pairs strategists with the actual build team. InData Labs's primary differentiator is: a data-science-first heritage predating 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 agency before making a decision.