InData Labs vs EPAM Systems: full comparison for 2026
Quick verdict
InData Labs (4.4/5) edges ahead of EPAM Systems (3.9/5) overall. InData Labs is the better choice for data-science-heavy teams, AWS-based ML work. EPAM Systems is the stronger option for global enterprises with large, compliance-heavy AI programs. 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 | Nicosia, Cyprus | Newtown, Pennsylvania, USA |
| Team size | 50–249 | 62,850 |
| Rating | 4.4 / 5 | 3.9 / 5 |
| Primary differentiator | Data scientists and data engineers from one AI-only company | Scale, compliance maturity and vendor certifications |
| Pricing model | Dedicated team; time and materials; project budgets from under $50K per Clutch | Enterprise time and materials; dedicated teams; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, PyTorch, TensorFlow | Claude, OpenAI, Gemini |
| Industries served | Healthcare, Fintech, Retail and e-commerce, Media | Fintech, Healthcare, Retail, Manufacturing, Travel |
InData Labs vs EPAM Systems: overview
InData Labs
InData Labs was founded in 2014 and is headquartered in Nicosia, Cyprus, with offices in Vilnius and Miami. Its services include AI research and development, generative AI, predictive analytics, computer vision, data engineering, and a dedicated-team or staff-augmentation option. Clutch lists it as a certified AWS partner with 50–249 employees. Clutch reviewers single out its data-science and ML engineering skills.
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with about 62,850 employees as of June 30, 2026, of whom roughly 56,650 work in delivery. It reports more than 5,700 Claude-certified engineers and set a target of 10,000, along with thousands of OpenAI- and Gemini-certified specialists. The company is targeting $600 million in AI-native services revenue for 2026. Its model is enterprise delivery, so individual staff augmentation usually sits inside a larger program.
Services and capabilities: InData Labs vs EPAM Systems
| Capability | InData Labs | EPAM Systems |
|---|---|---|
| LLM / GenAI engineers | ✗ | ✓ |
| MLOps & deployment | ✗ | ✓ |
| Computer vision | ✓ | ✗ |
| Data engineering | ✓ | ✓ |
| AI agent development | ✗ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Risk-free trial period | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
Tech stack comparison: InData Labs vs EPAM Systems
| Framework / platform | InData Labs | EPAM Systems |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS SageMaker | ✓ | ✓ |
| Azure ML | N/A | ✓ |
| Databricks | N/A | ✓ |
| MLflow | N/A | 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 | Dedicated team, Full-time dedicated engineers, Managed delivery | Dedicated team, Managed delivery |
| 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 | Healthcare, Fintech, Retail and e-commerce | Fintech, Healthcare, Retail |
| Best use cases | Adding an NLP engineer to a text-analytics product, Placing a computer-vision specialist for an image-recognition feature | Staffing a multi-team GenAI program at a global bank, Adding certified Claude engineers to an enterprise AI platform |
| Typical project type | Dedicated team | Dedicated team |
InData Labs vs EPAM Systems: pros and cons
| InData Labs | |
|---|---|
| + | AI and data are the whole business, so placed engineers come from a specialist bench |
| + | Combines NLP, computer vision and predictive analytics under one contract |
| + | AWS partnership is useful for SageMaker-based teams |
| + | EU-registered company, which simplifies contracting for European buyers |
| - | Smaller bench than nearshore generalists |
| - | Staff augmentation is a secondary offer next to project work |
| - | Limited time-zone overlap with the U.S. West Coast |
| EPAM Systems | |
|---|---|
| + | Largest bench on this list, with security and compliance processes to match |
| + | Thousands of engineers certified on major model platforms |
| + | Can staff any role an AI program needs |
| - | Built for enterprise programs; a single-engineer request is a poor fit |
| - | Highest overhead and slowest procurement on this list |
| - | Rates not published |
Who should choose InData Labs?
A typical fit: adding an NLP engineer to a text-analytics product.
Data scientists and data engineers from one AI-only company. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Fintech, Retail and e-commerce, Media.
Who should choose EPAM Systems?
A typical fit: staffing a multi-team GenAI program at a global bank.
Scale, compliance maturity and vendor certifications. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Healthcare, Retail, Manufacturing, Travel.
Decision matrix: InData Labs vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| 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 | EPAM Systems |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: InData Labs vs EPAM Systems
| Use case | InData Labs fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Adding an NLP engineer to a text-analytics product | Strong | Strong | Both equally |
| Placing a computer-vision specialist for an image-recognition feature | Strong | Limited | InData Labs |
| Staffing a multi-team GenAI program at a global bank | Limited | Strong | EPAM Systems |
| Adding certified Claude engineers to an enterprise AI platform | Strong | Strong | Both equally |
Verdict: InData Labs vs EPAM Systems
InData Labs (4.4/5) is the stronger overall choice for most AI Staffing projects. Data scientists and data engineers from one AI-only company.
EPAM Systems (3.9/5) is worth a look if you need adding certified Claude engineers to an enterprise AI platform. If your situation matches that, EPAM Systems is a competitive option.
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InData Labs vs EPAM Systems FAQ
Is InData Labs better than EPAM Systems?
InData Labs (4.4/5) scores higher overall, but "better" depends on your use case. InData Labs's strongest advantage: AI and data are the whole business, so placed engineers come from a specialist bench. EPAM Systems's strongest advantage: largest bench on this list, with security and compliance processes to match.
How do InData Labs and EPAM Systems differ in pricing?
InData Labs uses dedicated team; time and materials; project budgets from under $50k per clutch pricing. EPAM Systems uses enterprise time and materials; dedicated teams; rates on request 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 agency before shortlisting.
What are the main differences between InData Labs and EPAM Systems?
InData Labs's primary differentiator is: data scientists and data engineers from one AI-only company. EPAM Systems's primary differentiator is: Scale, compliance maturity and vendor certifications. They also differ in team size (50–249 vs 62,850), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthcare, Fintech vs Fintech, Healthcare).
Verify all details directly with each agency before making a decision.