DataToBiz vs EPAM Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
DataToBiz (4.0/5) edges ahead of EPAM Systems (3.8/5) overall. DataToBiz is the better choice for startups and growth-stage companies that need to take an ML product idea from strategy through to market-ready delivery. 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.
DataToBiz vs EPAM Systems: head-to-head summary
| Criterion | DataToBiz | EPAM Systems |
|---|---|---|
| Founded | 2019 | 1993 |
| HQ | Chandigarh, India (US office) | Newtown, PA |
| Team size | 100–250 | 50,000+ |
| Rating | 4.0 / 5 | 3.8 / 5 |
| Best for | Startups and growth-stage companies that need to take an ML product idea from strategy through to market-ready delivery | 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 | $10K | ~$200K+ |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, TensorFlow, PyTorch |
| Industries served | Financial Services, Retail & E-commerce, Healthcare & Life Sciences, Manufacturing & Industrial | Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce |
DataToBiz vs EPAM Systems: overview
DataToBiz
DataToBiz is an AI product development company founded in 2019 and headquartered in Chandigarh, India, with US presence and 100–250 employees. The firm focuses on transforming ML ideas into market-ready AI products — covering AI product strategy, data engineering, model development, and product delivery in a single engagement model. DataToBiz serves clients in finance, retail, healthcare, and manufacturing.
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: DataToBiz vs EPAM Systems
| Capability | DataToBiz | EPAM Systems |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP & LLMs | ✗ | ✗ |
| MLOps & deployment | ✗ | ✓ |
| Generative AI | ✓ | ✓ |
| Staff augmentation | ✗ | ✓ |
Tech stack comparison: DataToBiz vs EPAM Systems
| Framework / platform | DataToBiz | 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 | N/A | ✓ |
| Kubernetes | N/A | ✓ |
| MLflow | N/A | N/A |
Pricing comparison: DataToBiz vs EPAM Systems
| Criterion | DataToBiz | EPAM Systems |
|---|---|---|
| Minimum engagement | $10K | ~$200K+ |
| Engagement models | Fixed project, Time & materials | Dedicated team, Time & materials |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Enterprise |
Target audience comparison: DataToBiz vs EPAM Systems
| Dimension | DataToBiz | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial Services, Retail & E-commerce, Healthcare & Life Sciences | Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial |
| Best use cases | AI product MVP for fintech startup — from ML idea through to investor-ready demo, E-commerce personalisation product built with ML recommendation engine | 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 |
DataToBiz vs EPAM Systems: pros and cons
| DataToBiz | |
|---|---|
| + | Lowest minimum engagement at $10K — accessible for pre-seed and seed-stage AI product development |
| + | Product-first delivery model — engineers launchable AI products, not isolated models |
| + | AI strategy and product roadmap capability alongside engineering reduces vendor count |
| + | Fast time-to-MVP orientation aligns with startup fundraising and growth timelines |
| + | Generative AI product capability alongside core ML model development |
| - | Younger firm (founded 2019) with shorter delivery track record than established peers |
| - | India-based offshore delivery requires active async communication management |
| - | Less depth in enterprise-grade MLOps, compliance, and large-scale data engineering |
| 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 DataToBiz?
DataToBiz is the right choice for startups and growth-stage companies that need to take an ML product idea from strategy through to market-ready delivery.
Product-oriented ML delivery — combines AI strategy with full-cycle engineering to produce launchable products, not just models. Minimum engagement starts at $10K. Works best with clients in Financial Services, Retail & E-commerce, Healthcare & Life Sciences, Manufacturing & Industrial.
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: DataToBiz vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DataToBiz |
| You need a large dedicated team for an ongoing programme | EPAM Systems |
| Your budget is at the lower end | DataToBiz |
| You need specialist depth in a specific vertical | EPAM Systems |
| You need staff augmentation or team extension | EPAM Systems |
| You need consulting before committing to a build | DataToBiz |
Use case fit: DataToBiz vs EPAM Systems
| Use case | DataToBiz fit | EPAM Systems fit | Winner |
|---|---|---|---|
| AI product MVP for fintech startup — from ML idea through to investor-ready demo | Strong | Strong | Both equally |
| E-commerce personalisation product built with ML recommendation engine | Strong | Limited | DataToBiz |
| 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: DataToBiz vs EPAM Systems
DataToBiz (4.0/5) is the stronger overall choice for most Machine Learning Development projects. Product-oriented ML delivery — combines AI strategy with full-cycle engineering to produce launchable products, not just models. It is best for startups and growth-stage companies that need to take an ML product idea from strategy through to market-ready delivery.
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
DataToBiz vs EPAM Systems FAQ
Is DataToBiz better than EPAM Systems?
DataToBiz (4.0/5) scores higher overall, but "better" depends on your use case. DataToBiz is better for startups and growth-stage companies that need to take an ML product idea from strategy through to market-ready delivery. 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 DataToBiz and EPAM Systems differ in pricing?
DataToBiz uses fixed project, t&m pricing with a minimum engagement of $10K. 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: DataToBiz or EPAM Systems?
DataToBiz 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 DataToBiz and EPAM Systems?
DataToBiz's primary differentiator is: product-oriented ml delivery — combines ai strategy with full-cycle engineering to produce launchable products, not just models. 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 (100–250 vs 50,000+), minimum engagement ($10K vs ~$200K+), and primary industries served (Financial Services, Retail & E-commerce vs Financial Services, Healthcare & Life Sciences).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.