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 established enterprises needing ML consulting from a vendor with 35+ years of enterprise software experience and US-based organizational stability. EPAM Systems is the stronger option for large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration. 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 | 700+ | 62,000+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Best for | Established enterprises needing ML consulting from a vendor with 35+ years of enterprise software experience and US-based organizational stability | Large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration |
| Pricing model | Fixed project, T&M, Dedicated team, Retainer | Dedicated team, T&M, Fixed project, Staff augmentation |
| Min. engagement | $30K | $50K |
| Primary tech stack | Python, R, TensorFlow | Python, TensorFlow, PyTorch |
| Industries served | healthcare, retail, financial services, manufacturing, government | financial services, healthcare, retail, media, government |
ScienceSoft vs EPAM Systems: overview
ScienceSoft
ScienceSoft is a US-based IT consulting and software development company founded in 1989 and headquartered in McKinney, Texas. The company employs 700+ professionals and has been delivering enterprise software for 35+ years, with an ML practice serving healthcare, retail, financial services, manufacturing, and government clients. ScienceSoft's unusual organizational longevity provides compliance readiness, institutional knowledge, and process maturity rare in younger ML-focused firms.
EPAM Systems
EPAM Systems is a global technology engineering company founded in 1993 and headquartered in Newtown, Pennsylvania. The company employs 62,000+ engineers across 50+ countries and is publicly traded on the NYSE. EPAM provides end-to-end AI development services from strategy and consulting to implementation and support, working with Fortune 500 clients across financial services, healthcare, retail, media, and government. EPAM is the largest firm in this review, with AI/ML capabilities delivered within a full-service technology engineering operation.
Services and capabilities: ScienceSoft vs EPAM Systems
| Capability | ScienceSoft | EPAM Systems |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| ML consulting | ✓ | ✓ |
| Deep learning | ✗ | ✗ |
| NLP | ✓ | ✗ |
| Computer vision | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Predictive analytics | ✓ | ✗ |
| Generative AI | ✗ | ✓ |
| Data engineering | ✓ | ✓ |
| Staff augmentation | ✗ | ✓ |
Tech stack comparison: ScienceSoft vs EPAM Systems
| Framework / platform | ScienceSoft | EPAM Systems |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | N/A | ✓ |
| Scikit-Learn | ✓ | N/A |
| LangChain | N/A | N/A |
| AWS SageMaker | ✓ | ✓ |
| Azure ML | ✓ | ✓ |
| GCP Vertex AI | N/A | N/A |
| Kubernetes | N/A | ✓ |
| Apache Spark | ✓ | ✓ |
| MLflow | ✓ | ✓ |
Pricing comparison: ScienceSoft vs EPAM Systems
| Criterion | ScienceSoft | EPAM Systems |
|---|---|---|
| Minimum engagement | $30K | $50K |
| Engagement models | Fixed project, T&M, Dedicated team, Retainer | Dedicated team, T&M, Fixed project, Staff augmentation |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
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, retail, financial services | financial services, healthcare, retail |
| Best use cases | ML consulting and roadmap development for enterprises beginning their AI programme, Predictive maintenance model development for manufacturing equipment | Global enterprise AI transformation programme requiring multi-country deployment and governance, Complex Fortune 500 ML programme integrating across dozens of legacy systems |
| Typical project type | Fixed project | Dedicated team |
ScienceSoft vs EPAM Systems: pros and cons
| ScienceSoft | |
|---|---|
| + | 35+ years of enterprise software delivery history gives clients a stable long-term partner |
| + | US-based HQ with government sector experience including compliance-aware ML delivery |
| + | Retainer model available for ongoing ML improvement and model maintenance programmes |
| + | Broad technology coverage across Python, R, Azure ML, and AWS SageMaker |
| + | Established reputation on Clutch and industry directories with long-standing client relationships |
| - | Generalist heritage means ML is one of many practice areas — less specialist depth than pure-play boutiques |
| - | Less exposure to cutting-edge LLM and generative AI tooling than newer AI-native firms |
| - | Larger organization may mean slower engagement initiation than boutiques |
| EPAM Systems | |
|---|---|
| + | 62,000+ engineers provides unmatched scale for simultaneous large-scale enterprise ML programmes |
| + | Publicly traded NYSE company with audited financials — maximum organizational stability and governance |
| + | Global delivery across 50+ countries enables ML delivery under local data sovereignty requirements |
| + | Full AI lifecycle from strategy through production MLOps within one organizational relationship |
| + | Fortune 500 client base validates enterprise-grade ML delivery at the highest complexity level |
| - | Enterprise scale means ML projects go through larger organizational process — slower initiation than boutiques |
| - | High minimum engagement ($50K) limits accessibility for SMBs or early-stage organizations |
| - | Generalist technology engineering scope means ML specialist depth may be lower per individual than pure-play ML boutiques |
Who should choose ScienceSoft?
ScienceSoft is the right choice for established enterprises needing ML consulting from a vendor with 35+ years of enterprise software experience and US-based organizational stability.
35+ years of enterprise delivery experience with a mature ML practice — providing compliance readiness, institutional knowledge, and process maturity rare in younger ML-focused competitors. Minimum engagement starts at $30K. Works best with clients in healthcare, retail, financial services, manufacturing, government.
Who should choose EPAM Systems?
EPAM Systems is the right choice for large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration.
62,000+ engineers across 50+ countries delivering ML inside a full-service technology engineering operation — unmatched scale and compliance depth for global enterprise AI programmes. Minimum engagement starts at $50K. Works best with clients in financial services, healthcare, retail, media, government.
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 | ScienceSoft |
| 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 |
|---|---|---|---|
| ML consulting and roadmap development for enterprises beginning their AI programme | Strong | Strong | Both equally |
| Predictive maintenance model development for manufacturing equipment | Strong | Limited | ScienceSoft |
| Global enterprise AI transformation programme requiring multi-country deployment and governance | Limited | Strong | EPAM Systems |
| Complex Fortune 500 ML programme integrating across dozens of legacy systems | Limited | Strong | EPAM Systems |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: ScienceSoft vs EPAM Systems
ScienceSoft (4.0/5) is the stronger overall choice for most Machine Learning Development projects. 35+ years of enterprise delivery experience with a mature ML practice — providing compliance readiness, institutional knowledge, and process maturity rare in younger ML-focused competitors. It is best for established enterprises needing ML consulting from a vendor with 35+ years of enterprise software experience and US-based organizational stability.
EPAM Systems (3.9/5) is the better choice when large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration. 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 established enterprises needing ML consulting from a vendor with 35+ years of enterprise software experience and US-based organizational stability. EPAM Systems is better for large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration.
How do ScienceSoft and EPAM Systems differ in pricing?
ScienceSoft uses fixed project, t&m, dedicated team, retainer pricing with a minimum engagement of $30K. EPAM Systems uses dedicated team, t&m, fixed project, staff augmentation pricing with a minimum engagement of $50K. 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: 35+ years of enterprise delivery experience with a mature ml practice — providing compliance readiness, institutional knowledge, and process maturity rare in younger ml-focused competitors. EPAM Systems's primary differentiator is: 62,000+ engineers across 50+ countries delivering ml inside a full-service technology engineering operation — unmatched scale and compliance depth for global enterprise ai programmes. They also differ in team size (700+ vs 62,000+), minimum engagement ($30K vs $50K), and primary industries served (healthcare, retail vs financial services, healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.