InData Labs vs EPAM Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.6/5) edges ahead of EPAM Systems (3.8/5) overall. InData Labs is the better choice for businesses with complex, highly specific ML problems requiring deep data science expertise and custom model architecture. EPAM Systems is the stronger option for global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation. 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 | New York, NY | Newtown, PA |
| Team size | 100+ | 50,000+ |
| Rating | 4.6 / 5 | 3.8 / 5 |
| Best for | Businesses with complex, highly specific ML problems requiring deep data science expertise and custom model architecture | Global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation |
| Pricing model | Fixed project, T&M | Dedicated team, T&M |
| Min. engagement | $20K | ~$200K+ |
| Primary tech stack | TensorFlow, PyTorch, Scikit-learn | Python, TensorFlow, PyTorch |
| Industries served | Healthcare & Life Sciences, Financial Services, Retail & E-commerce, Manufacturing & Industrial, Media & Entertainment | Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce |
InData Labs vs EPAM Systems: overview
InData Labs
InData Labs is a specialist data science and AI company founded in 2014 with offices in New York and the EU. The firm focuses on complex, domain-specific ML problems — custom computer vision systems, unique NLP models, and advanced predictive analytics — that require deep data science expertise rather than off-the-shelf tooling. InData Labs has delivered production ML solutions for healthcare, fintech, retail, and manufacturing clients.
EPAM Systems
EPAM Systems is a global software engineering and IT services company founded in 1993 and headquartered in Newtown, PA, with 50,000+ professionals. The firm offers AI-native engineering services with a focus on scaling ML, generative AI, and agentic systems across large enterprises with strict governance requirements. EPAM is a powerhouse for building complex, software-heavy AI products from scratch, though it comes at a premium price point.
Services and capabilities: InData Labs vs EPAM Systems
| Capability | InData Labs | EPAM Systems |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP & LLMs | ✓ | ✗ |
| MLOps & deployment | ✗ | ✓ |
| Generative AI | ✗ | ✓ |
| Staff augmentation | ✗ | ✓ |
Tech stack comparison: InData Labs vs EPAM Systems
| Framework / platform | InData Labs | EPAM Systems |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
| Vertex AI | N/A | N/A |
| Scikit-learn | ✓ | N/A |
| Hugging Face | N/A | N/A |
| Apache Spark | ✓ | ✓ |
| Kubernetes | N/A | ✓ |
| MLflow | N/A | N/A |
Pricing comparison: InData Labs vs EPAM Systems
| Criterion | InData Labs | EPAM Systems |
|---|---|---|
| Minimum engagement | $20K | ~$200K+ |
| Engagement models | Fixed project, Time & materials, Retainer | Dedicated team, Time & materials |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Enterprise |
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 & Life Sciences, Financial Services, Retail & E-commerce | Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial |
| Best use cases | Custom NLP model for healthcare clinical documentation and medical coding, Computer vision quality control for high-precision manufacturing environments | Global AI transformation programme for Fortune 100 enterprise with multi-year delivery scope, Enterprise GenAI platform with strict governance and compliance for regulated financial institution |
| Typical project type | Fixed project | Dedicated team |
InData Labs vs EPAM Systems: pros and cons
| InData Labs | |
|---|---|
| + | Recognised for tackling high-complexity ML problems other firms deprioritise |
| + | Deep data science bench — not a repurposed software team with ML wrapping |
| + | Production track record across healthcare NLP, fintech predictive models, and retail computer vision |
| + | EU presence simplifies GDPR compliance scoping for European data workflows |
| + | Accessible $20K minimum for complex niche projects |
| - | Team size (100+) limits parallel project capacity for large enterprise programmes |
| - | Niche focus means less coverage for MLOps infrastructure build-out or large-scale data engineering |
| - | Less brand visibility than larger peers — harder to benchmark via public reviews |
| EPAM Systems | |
|---|---|
| + | 50,000+ professionals — unmatched delivery scale for global multi-stream AI programmes |
| + | AI-native engineering practice purpose-built for scaling ML, GenAI, and agentic systems |
| + | Strict governance and compliance frameworks for regulated enterprise AI delivery |
| + | Full-stack capability from hardware infrastructure through ML models to frontend AI products |
| + | Strong US and Eastern European delivery mix for cost-performance balance at enterprise scale |
| - | ~$200K+ minimum makes EPAM inaccessible for all but the largest enterprise budgets |
| - | Large-firm overhead — procurement, contracting, and ramp-up timelines are significantly longer than boutiques |
| - | Generalist breadth means less niche ML depth than boutiques in specific domains like healthcare imaging or time-series |
Who should choose InData Labs?
InData Labs is the right choice for businesses with complex, highly specific ML problems requiring deep data science expertise and custom model architecture.
Boutique firm with a track record of solving atypical, high-complexity ML problems that generalist shops decline or under-deliver on. Minimum engagement starts at $20K. Works best with clients in Healthcare & Life Sciences, Financial Services, Retail & E-commerce, Manufacturing & Industrial, Media & Entertainment.
Who should choose EPAM Systems?
EPAM Systems is the right choice for global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation.
AI-native engineering practice at 50,000-person scale — the broadest talent pool and delivery capacity of any firm on this list. Minimum engagement starts at ~$200K+. Works best with clients in Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce.
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 | EPAM Systems |
| Your budget is at the lower end | InData Labs |
| You need specialist depth in a specific vertical | InData Labs |
| You need staff augmentation or team extension | EPAM Systems |
| You need consulting before committing to a build | InData Labs |
Use case fit: InData Labs vs EPAM Systems
| Use case | InData Labs fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Custom NLP model for healthcare clinical documentation and medical coding | Strong | Limited | InData Labs |
| Computer vision quality control for high-precision manufacturing environments | Strong | Limited | InData Labs |
| Global AI transformation programme for Fortune 100 enterprise with multi-year delivery scope | Limited | Strong | EPAM Systems |
| Enterprise GenAI platform with strict governance and compliance for regulated financial institution | 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 (4.6/5) is the stronger overall choice for most Machine Learning Development projects. Boutique firm with a track record of solving atypical, high-complexity ML problems that generalist shops decline or under-deliver on. It is best for businesses with complex, highly specific ML problems requiring deep data science expertise and custom model architecture.
EPAM Systems (3.8/5) is the better choice when global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation. If your situation matches those criteria, EPAM Systems is a competitive option.
Related comparisons
InData Labs vs EPAM Systems FAQ
Is InData Labs better than EPAM Systems?
InData Labs (4.6/5) scores higher overall, but "better" depends on your use case. InData Labs is better for businesses with complex, highly specific ML problems requiring deep data science expertise and custom model architecture. EPAM Systems is better for global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation.
How do InData Labs and EPAM Systems differ in pricing?
InData Labs uses fixed project, t&m pricing with a minimum engagement of $20K. EPAM Systems uses dedicated team, t&m pricing with a minimum engagement of ~$200K+. 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: boutique firm with a track record of solving atypical, high-complexity ml problems that generalist shops decline or under-deliver on. EPAM Systems's primary differentiator is: ai-native engineering practice at 50,000-person scale — the broadest talent pool and delivery capacity of any firm on this list. They also differ in team size (100+ vs 50,000+), minimum engagement ($20K vs ~$200K+), and primary industries served (Healthcare & Life Sciences, Financial Services vs Financial Services, Healthcare & Life Sciences).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.