ScienceSoft vs EPAM Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
ScienceSoft (4.2/5) edges ahead of EPAM Systems (3.8/5) overall. ScienceSoft is the better choice for healthcare and financial services organisations that need ML delivered within HIPAA, PCI-DSS, or SOC 2 compliance frameworks. 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.
ScienceSoft vs EPAM Systems: head-to-head summary
| Criterion | ScienceSoft | EPAM Systems |
|---|---|---|
| Founded | 1989 | 1993 |
| HQ | McKinney, TX | Newtown, PA |
| Team size | 750+ | 50,000+ |
| Rating | 4.2 / 5 | 3.8 / 5 |
| Best for | Healthcare and financial services organisations that need ML delivered within HIPAA, PCI-DSS, or SOC 2 compliance frameworks | 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 | $30K | ~$200K+ |
| Primary tech stack | Python, TensorFlow, Scikit-learn | Python, TensorFlow, PyTorch |
| Industries served | Healthcare & Life Sciences, Financial Services, Manufacturing & Industrial, Retail & E-commerce | Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce |
ScienceSoft vs EPAM Systems: overview
ScienceSoft
ScienceSoft is an IT services company founded in 1989 and headquartered in McKinney, TX, with 750+ employees. The firm's ML practice covers the full pipeline including data preprocessing, feature engineering, algorithm selection, and model training, with clear industry specialisations in healthcare and finance that include regulatory compliance expertise. ScienceSoft is noted for translating complex ML requirements into production systems that meet HIPAA, PCI-DSS, and SOC 2 standards.
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: ScienceSoft vs EPAM Systems
| Capability | ScienceSoft | EPAM Systems |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP & LLMs | ✗ | ✗ |
| MLOps & deployment | ✓ | ✓ |
| Generative AI | ✗ | ✓ |
| Staff augmentation | ✗ | ✓ |
Tech stack comparison: ScienceSoft vs EPAM Systems
| Framework / platform | ScienceSoft | EPAM Systems |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | N/A | ✓ |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | 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: ScienceSoft vs EPAM Systems
| Criterion | ScienceSoft | EPAM Systems |
|---|---|---|
| Minimum engagement | $30K | ~$200K+ |
| Engagement models | Fixed project, Time & materials | Dedicated team, Time & materials |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Enterprise |
Target audience comparison: ScienceSoft vs EPAM Systems
| Dimension | ScienceSoft | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare & Life Sciences, Financial Services, Manufacturing & Industrial | Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial |
| Best use cases | HIPAA-compliant predictive readmission model for healthcare system, PCI-DSS-aligned fraud detection ML pipeline for payment processor | 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 |
ScienceSoft vs EPAM Systems: pros and cons
| ScienceSoft | |
|---|---|
| + | 35+ years of regulated IT delivery — compliance frameworks like HIPAA and PCI-DSS are deeply embedded |
| + | Full ML pipeline coverage from data preprocessing through deployed model documentation |
| + | US HQ with McKinney TX base reduces offshore communication risk for North American clients |
| + | Industry specialisation in healthcare and finance provides vertical domain depth |
| + | Accessible $30K minimum for compliance-aware ML projects |
| - | Less generative AI and LLM depth than firms that built AI-native practices post-2020 |
| - | Conservative delivery approach prioritises compliance over speed — not ideal for fast-moving experimental ML |
| - | Large portfolio breadth (IT services beyond ML) means ML is one of many practices, not the core product |
| 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 ScienceSoft?
ScienceSoft is the right choice for healthcare and financial services organisations that need ML delivered within HIPAA, PCI-DSS, or SOC 2 compliance frameworks.
Over 35 years of regulated IT delivery — compliance-aligned ML architecture is a core competency, not an add-on. Minimum engagement starts at $30K. Works best with clients in Healthcare & Life Sciences, Financial Services, Manufacturing & Industrial, Retail & E-commerce.
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: ScienceSoft vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | ScienceSoft |
| You need a large dedicated team for an ongoing programme | EPAM Systems |
| Your budget is at the lower end | ScienceSoft |
| 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 | ScienceSoft |
Use case fit: ScienceSoft vs EPAM Systems
| Use case | ScienceSoft fit | EPAM Systems fit | Winner |
|---|---|---|---|
| HIPAA-compliant predictive readmission model for healthcare system | Strong | Limited | ScienceSoft |
| PCI-DSS-aligned fraud detection ML pipeline for payment processor | Strong | Limited | ScienceSoft |
| 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: ScienceSoft vs EPAM Systems
ScienceSoft (4.2/5) is the stronger overall choice for most Machine Learning Development projects. Over 35 years of regulated IT delivery — compliance-aligned ML architecture is a core competency, not an add-on. It is best for healthcare and financial services organisations that need ML delivered within HIPAA, PCI-DSS, or SOC 2 compliance frameworks.
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.
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ScienceSoft vs EPAM Systems FAQ
Is ScienceSoft better than EPAM Systems?
ScienceSoft (4.2/5) scores higher overall, but "better" depends on your use case. ScienceSoft is better for healthcare and financial services organisations that need ML delivered within HIPAA, PCI-DSS, or SOC 2 compliance frameworks. 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 ScienceSoft and EPAM Systems differ in pricing?
ScienceSoft uses fixed project, t&m pricing with a minimum engagement of $30K. 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: ScienceSoft 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 ScienceSoft and EPAM Systems?
ScienceSoft's primary differentiator is: over 35 years of regulated it delivery — compliance-aligned ml architecture is a core competency, not an add-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 (750+ vs 50,000+), minimum engagement ($30K 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.