ScienceSoft vs EPAM Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
ScienceSoft (4.0/5) edges ahead of EPAM Systems (3.9/5) overall. ScienceSoft is the better choice for manufacturing, healthcare, and oil & gas enterprises needing ISO-certified ML delivery from a stable 35-year-old vendor. EPAM Systems is the stronger option for large enterprises needing scale, global delivery coverage, and programme management infrastructure alongside ML engineering. 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, USA | Newtown, PA, USA |
| Team size | 500–1,000 | 58,000+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Best for | Manufacturing, healthcare, and oil & gas enterprises needing ISO-certified ML delivery from a stable 35-year-old vendor | Large enterprises needing scale, global delivery coverage, and programme management infrastructure alongside ML engineering |
| Pricing model | Fixed project, T&M, Dedicated team | T&M, Dedicated team |
| Min. engagement | $30K | $100K |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, TensorFlow, PyTorch |
| Industries served | Manufacturing, Healthcare, Financial Services, Logistics, Energy / Oil & Gas | Financial Services, Healthcare, Technology / SaaS, Media / Entertainment, Logistics, Retail / E-commerce |
ScienceSoft vs EPAM Systems: overview
ScienceSoft
ScienceSoft was founded in 1989 and is headquartered in McKinney, Texas, with a team of 500–1,000 professionals spanning software development, data science, cybersecurity, and IT consulting. Its machine learning practice focuses on manufacturing, healthcare, and oil and gas — regulated industries where domain expertise, compliance knowledge, and long-term support matter more than speed. ScienceSoft's longevity provides clients with an unusually stable vendor relationship: unlike startups or mid-sized boutiques, it has survived multiple technology cycles and carries ISO 9001 and ISO 27001 certifications that many manufacturing and healthcare clients require before signing.
EPAM Systems
EPAM Systems is a global digital transformation services company founded in 1993 and headquartered in Newtown, Pennsylvania, with over 58,000 professionals worldwide. It was ranked among the top three tech and AI companies on Glassdoor's Best Places to Work 2026. EPAM's AI and ML practice covers custom ML development, data engineering, generative AI, MLOps, and staff augmentation, delivered across financial services, healthcare, media, SaaS, and logistics. EPAM is best suited to enterprises needing a large-scale delivery partner with the governance, compliance, and programme management infrastructure of a major consultancy at software engineering rates.
Services and capabilities: ScienceSoft vs EPAM Systems
| Capability | ScienceSoft | EPAM Systems |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Deep learning | ✗ | ✗ |
| NLP / Text analytics | ✓ | ✗ |
| Computer vision | ✓ | ✗ |
| MLOps & deployment | ✗ | ✓ |
| Generative AI | ✗ | ✓ |
| AI strategy | ✓ | ✓ |
| Staff augmentation | ✗ | ✓ |
| Fixed-price projects | ✓ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: ScienceSoft vs EPAM Systems
| Framework / platform | ScienceSoft | EPAM Systems |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Kubernetes | ✓ | ✓ |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
Pricing comparison: ScienceSoft vs EPAM Systems
| Criterion | ScienceSoft | EPAM Systems |
|---|---|---|
| Minimum engagement | $30K | $100K |
| Engagement models | Fixed project, Time & materials, Dedicated team | Time & materials, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: ScienceSoft vs EPAM Systems
| Dimension | ScienceSoft | EPAM Systems |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Manufacturing, Healthcare, Financial Services | Financial Services, Healthcare, Technology / SaaS |
| Best use cases | Predictive maintenance ML for manufacturing and industrial equipment with compliance documentation, Medical image analysis and clinical decision support systems for regulated healthcare environments | Enterprise-scale ML platform build requiring 50+ engineers across data engineering, ML, and MLOps tracks simultaneously, Global digital transformation programmes embedding ML into enterprise software at multiple business units |
| Typical project type | Fixed project | Time & materials |
ScienceSoft vs EPAM Systems: pros and cons
| ScienceSoft | |
|---|---|
| + | 35+ years of operation provides rare vendor stability for enterprises requiring long-term maintenance commitments |
| + | ISO 9001 and ISO 27001 certifications satisfy compliance requirements in manufacturing, healthcare, and regulated industries |
| + | Broad technology stack spans ML, cybersecurity, and traditional software — reduces need for separate vendors on complex projects |
| + | McKinney, TX headquarters provides US-based relationship management for North American enterprise clients |
| + | Competitively priced relative to US-headquartered firms of comparable certification status |
| - | ML is one practice within a very broad portfolio — specialist depth in cutting-edge deep learning is thinner than ML-native boutiques |
| - | Conservative delivery style suits compliance-heavy industries but can slow projects where experimentation and iteration are prioritised |
| - | Less suitable for startups needing fast ML prototyping or cutting-edge generative AI capability |
| EPAM Systems | |
|---|---|
| + | 58,000+ engineers provide unmatched concurrent delivery capacity for large-scale enterprise ML programmes |
| + | Glassdoor top-3 Best Tech & AI Company 2026 reflects high engineering talent quality and retention |
| + | Full global delivery footprint enables follow-the-sun support and multi-geography data processing compliance |
| + | Strong programme management and governance infrastructure reduces enterprise delivery risk on complex projects |
| + | ML capability combined with broader digital transformation services reduces vendor proliferation for enterprise buyers |
| - | $100K minimum and large-firm overhead pricing makes EPAM less suitable for mid-market or startup buyers |
| - | ML specialisation depth is diluted by the breadth of a 58,000-person general technology firm |
| - | Large firm bureaucracy and account management layers can slow decision-making on agile ML projects |
Who should choose ScienceSoft?
ScienceSoft is the right choice for manufacturing, healthcare, and oil & gas enterprises needing ISO-certified ML delivery from a stable 35-year-old vendor.
35+ years of operation with ISO 9001 and ISO 27001 certifications — provides compliance-mandated vendor stability rare in the ML agency market. Minimum engagement starts at $30K. Works best with clients in Manufacturing, Healthcare, Financial Services, Logistics, Energy / Oil & Gas.
Who should choose EPAM Systems?
EPAM Systems is the right choice for large enterprises needing scale, global delivery coverage, and programme management infrastructure alongside ML engineering.
Global scale with 58,000+ engineers and top-3 Glassdoor AI company ranking — rare ML delivery capacity for simultaneous large enterprise programmes. Minimum engagement starts at $100K. Works best with clients in Financial Services, Healthcare, Technology / SaaS, Media / Entertainment, Logistics, 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 | ScienceSoft |
| 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 | Both may offer discovery engagements |
Use case fit: ScienceSoft vs EPAM Systems
| Use case | ScienceSoft fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Predictive maintenance ML for manufacturing and industrial equipment with compliance documentation | Strong | Limited | ScienceSoft |
| Medical image analysis and clinical decision support systems for regulated healthcare environments | Strong | Limited | ScienceSoft |
| Enterprise-scale ML platform build requiring 50+ engineers across data engineering, ML, and MLOps tracks simultaneously | Limited | Strong | EPAM Systems |
| Global digital transformation programmes embedding ML into enterprise software at multiple business units | Limited | Strong | EPAM Systems |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Strong | EPAM Systems |
Verdict: ScienceSoft vs EPAM Systems
ScienceSoft (4.0/5) is the stronger overall choice for most Machine Learning projects. 35+ years of operation with ISO 9001 and ISO 27001 certifications — provides compliance-mandated vendor stability rare in the ML agency market. It is best for manufacturing, healthcare, and oil & gas enterprises needing ISO-certified ML delivery from a stable 35-year-old vendor.
EPAM Systems (3.9/5) is the better choice when large enterprises needing scale, global delivery coverage, and programme management infrastructure alongside ML engineering. If your situation matches those criteria, EPAM Systems is a competitive option.
Related comparisons
ScienceSoft vs EPAM Systems FAQ
Is ScienceSoft better than EPAM Systems?
ScienceSoft (4.0/5) scores higher overall, but "better" depends on your use case. ScienceSoft is better for manufacturing, healthcare, and oil & gas enterprises needing ISO-certified ML delivery from a stable 35-year-old vendor. EPAM Systems is better for large enterprises needing scale, global delivery coverage, and programme management infrastructure alongside ML engineering.
How do ScienceSoft and EPAM Systems differ in pricing?
ScienceSoft uses fixed project, t&m, dedicated team pricing with a minimum engagement of $30K. EPAM Systems uses t&m, dedicated team pricing with a minimum engagement of $100K. 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?
ScienceSoft 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 ScienceSoft and EPAM Systems?
ScienceSoft's primary differentiator is: 35+ years of operation with iso 9001 and iso 27001 certifications — provides compliance-mandated vendor stability rare in the ml agency market. EPAM Systems's primary differentiator is: global scale with 58,000+ engineers and top-3 glassdoor ai company ranking — rare ml delivery capacity for simultaneous large enterprise programmes. They also differ in team size (500–1,000 vs 58,000+), minimum engagement ($30K vs $100K), and primary industries served (Manufacturing, Healthcare vs Financial Services, Healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.