EPAM Systems vs ScienceSoft: full comparison for 2026
Last updated: July 2026
Quick verdict
EPAM Systems (4.0/5) edges ahead of ScienceSoft (3.8/5) overall. EPAM Systems is the better choice for large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner.. ScienceSoft is the stronger option for enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record.. The right choice depends on your project size, budget, and required tech stack.
EPAM Systems vs ScienceSoft: head-to-head summary
| Criterion | EPAM Systems | ScienceSoft |
|---|---|---|
| Founded | 1993 | 1989 |
| HQ | Newtown, Pennsylvania, United States | McKinney, Texas, United States |
| Team size | 50,000+ | 750+ |
| Rating | 4.0 / 5 | 3.8 / 5 |
| Best for | Large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner. | Enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record. |
| Pricing model | Time & materials, managed engagement | Time & materials, managed engagement |
| Min. engagement | $100,000+ | Not published |
| Primary tech stack | AWS SageMaker, Azure ML, Databricks | AWS, Azure ML, Google Cloud |
| Industries served | FinTech, Healthcare, Retail & E-commerce, Manufacturing, Telecom | Healthcare, Insurance, Manufacturing, Retail & E-commerce, Telecom |
EPAM Systems vs ScienceSoft: overview
EPAM Systems
EPAM Systems, Inc. (NYSE: EPAM) has operated since 1993 and has become one of the largest global digital transformation and engineering services providers, with a workforce in the tens of thousands. Its AI development services span generative AI, machine learning consulting, and intelligent automation, delivered by consultants, designers, and engineers who have worked with AI technologies for decades, and Clutch lists a minimum project size of $100,000+ with $150–$199/hr average rates. As a large publicly traded firm, EPAM offers the deepest compliance and financial transparency in this list, at a correspondingly higher entry price point.
ScienceSoft
ScienceSoft was founded in 1989 and is headquartered in McKinney, Texas, bringing together more than 750 engineers and consultants with a track record of over 4,200 successful projects for 1,400+ clients across healthcare, insurance, investment, manufacturing, retail, and telecom. Its AI practice includes AI engineers, generative AI consultants, and MLOps experts working with both open-source frameworks and cloud-native AI services, and Clutch has named ScienceSoft a 2018 Global IT Leader among its Clutch 1000 companies. At 35+ years old, it is one of the longest-established firms in this list, with AI as a newer addition to a much older core business.
Services and capabilities: EPAM Systems vs ScienceSoft
| Capability | EPAM Systems | ScienceSoft |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✓ | ✓ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✓ | ✓ |
Tech stack comparison: EPAM Systems vs ScienceSoft
| Framework / platform | EPAM Systems | ScienceSoft |
|---|---|---|
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: EPAM Systems vs ScienceSoft
| Criterion | EPAM Systems | ScienceSoft |
|---|---|---|
| Minimum engagement | $100,000+ | Not published |
| Engagement models | Managed engagement, Time & materials, Staff augmentation | Managed engagement, Time & materials, Staff augmentation |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs ScienceSoft
| Dimension | EPAM Systems | ScienceSoft |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | FinTech, Healthcare, Retail & E-commerce | Healthcare, Insurance, Manufacturing |
| Best use cases | Large enterprise with a $100K+ budget needs a publicly traded vendor for AI/ML procurement compliance requirements., Fortune 500 company needs generative AI deployed at global scale with responsible-AI governance built in. | Enterprise wants AI/MLOps delivery from a vendor with 35+ years of institutional stability., Insurance or healthcare company needs a vendor with a very large (4,200+) project delivery track record. |
| Typical project type | Managed engagement | Managed engagement |
EPAM Systems vs ScienceSoft: pros and cons
| EPAM Systems | |
|---|---|
| + | Publicly traded on the NYSE, giving clients access to audited financial disclosures unavailable from private competitors. |
| + | 50,000+ global workforce provides essentially unlimited delivery capacity for the largest enterprise AI programs. |
| + | 31+ years of engineering history (since 1993) predates the current AI hiring wave by decades. |
| + | AI/generative AI practice spans strategy through production deployment and responsible-AI compliance, covering the full enterprise lifecycle. |
| + | Scale/compliance standout among the researched companies — the clearest choice for regulated, large-budget enterprise programs. |
| - | $100,000+ minimum project size (per Clutch) puts EPAM out of reach for startups and mid-market budgets under six figures. |
| - | $150–$199/hr rate band is among the highest in this list, reflecting large-firm overhead. |
| - | At 50,000+ employees, AI/ML is one practice among dozens — clients should confirm they're getting a dedicated AI pod, not a generalist team. |
| ScienceSoft | |
|---|---|
| + | 35+ years of operating history (since 1989) is among the longest track records of any firm in this list. |
| + | 4,200+ successful projects for 1,400+ clients provides an extensive delivery pattern library across industries. |
| + | 2018 Global IT Leader recognition from Clutch, part of the Clutch 1000, is an independently sourced distinction. |
| + | 750+ engineers and consultants with dedicated MLOps and generative AI consulting roles, not just generalist developers relabeled. |
| - | AI is a comparatively newer addition to a company whose core 35-year identity is broader IT consulting. |
| - | 750-person total headcount spans many practice areas, so AI-specific bench depth is smaller than the total suggests. |
Who should choose EPAM Systems?
EPAM Systems is the right choice for large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner..
Public-company (NYSE: EPAM) scale and compliance rigor, with 30+ years of engineering history predating the AI wave.. Minimum engagement starts at $100,000+. Works best with clients in FinTech, Healthcare, Retail & E-commerce, Manufacturing, Telecom.
Who should choose ScienceSoft?
ScienceSoft is the right choice for enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record..
35+ years of continuous operating history (since 1989), among the longest-established vendors in this list.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Insurance, Manufacturing, Retail & E-commerce, Telecom.
Decision matrix: EPAM Systems vs ScienceSoft
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | Check each company's engagement model |
| Your budget is at the lower end | Compare: EPAM Systems ($100,000+) vs ScienceSoft (Not published) |
| You need specialist depth in a specific vertical | EPAM Systems |
| You need production MLOps support after model launch | Both offer MLOps support |
| You need consulting before committing to a build | EPAM Systems |
Use case fit: EPAM Systems vs ScienceSoft
| Use case | EPAM Systems fit | ScienceSoft fit | Winner |
|---|---|---|---|
| Large enterprise with a $100K+ budget needs a publicly traded vendor for AI/ML procurement compliance requirements. | Strong | Strong | Both equally |
| Fortune 500 company needs generative AI deployed at global scale with responsible-AI governance built in. | Strong | Limited | EPAM Systems |
| Enterprise wants AI/MLOps delivery from a vendor with 35+ years of institutional stability. | Strong | Strong | Both equally |
| Insurance or healthcare company needs a vendor with a very large (4,200+) project delivery track record. | Limited | Strong | ScienceSoft |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: EPAM Systems vs ScienceSoft
EPAM Systems (4.0/5) is the stronger overall choice for most Machine Learning Development projects. Public-company (NYSE: EPAM) scale and compliance rigor, with 30+ years of engineering history predating the AI wave.. It is best for large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner..
ScienceSoft (3.8/5) is the better choice when enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record.. If your situation matches those criteria, ScienceSoft is a competitive option.
Related comparisons
EPAM Systems vs ScienceSoft FAQ
Is EPAM Systems better than ScienceSoft?
EPAM Systems (4.0/5) scores higher overall, but "better" depends on your use case. EPAM Systems is better for large enterprises with $100K+ AI budgets that need a publicly traded, globally scaled engineering partner.. ScienceSoft is better for enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record..
How do EPAM Systems and ScienceSoft differ in pricing?
EPAM Systems uses time & materials, managed engagement pricing with a minimum engagement of $100,000+. ScienceSoft uses time & materials, managed engagement pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: EPAM Systems or ScienceSoft?
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 EPAM Systems and ScienceSoft?
EPAM Systems's primary differentiator is: public-company (nyse: epam) scale and compliance rigor, with 30+ years of engineering history predating the ai wave.. ScienceSoft's primary differentiator is: 35+ years of continuous operating history (since 1989), among the longest-established vendors in this list.. They also differ in team size (50,000+ vs 750+), minimum engagement ($100,000+ vs Not published), and primary industries served (FinTech, Healthcare vs Healthcare, Insurance).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.