EPAM Systems vs ScienceSoft: full comparison for 2026
Quick verdict
EPAM Systems (4.6/5) edges ahead of ScienceSoft (3.7/5) overall. EPAM Systems is the better choice for large enterprises, regulated industries, multi-team AI programs. ScienceSoft is the stronger option for regulated companies wanting a documented hiring process. 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, USA | McKinney, Texas, USA |
| Team size | 61,000+ | 750+ |
| Rating | 4.6 / 5 | 3.7 / 5 |
| Primary differentiator | Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes | Publishes its staff augmentation timeline and process |
| Pricing model | Time and materials for augmented engineers; dedicated team and managed program contracts; rates on request | Hourly or monthly rates shared with CVs; time and materials |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Azure ML, AWS |
| Industries served | Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing | Healthcare & life sciences, Financial services, Manufacturing, Retail & e-commerce |
EPAM Systems vs ScienceSoft: overview
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with roughly 61,000 employees across delivery centers in Europe, the Americas and Asia. It is a public company listed on the New York Stock Exchange. On its Q2 2026 earnings call, management said EPAM had more than 5,700 Anthropic-certified engineers and was among the five largest certified partners worldwide, with AI-native work making up about 11% of revenue. EPAM employs its engineers directly and sells them as augmented capacity, dedicated teams or managed programs, though most large accounts end up in the managed model.
ScienceSoft
ScienceSoft dates its IT work to 1989 and is headquartered in McKinney, Texas. It says its staff augmentation pool covers more than 750 professionals, including data scientists with long industry experience, and it publishes a fast hiring sequence: CVs with rates within a day, interviews in two to four days and starts in one to two weeks (per company website; independently unverifiable). AI is one of many service areas alongside its long-standing healthcare and finance work.
Services and capabilities: EPAM Systems vs ScienceSoft
| Capability | EPAM Systems | ScienceSoft |
|---|---|---|
| LLM / GenAI engineers | ✓ | ✗ |
| MLOps & deployment | ✓ | ✗ |
| Computer vision | ✗ | ✗ |
| Data engineering | ✓ | ✓ |
| AI agent development | ✓ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Risk-free trial period | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
Tech stack comparison: EPAM Systems vs ScienceSoft
| Framework / platform | EPAM Systems | ScienceSoft |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Databricks | ✓ | N/A |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: EPAM Systems vs ScienceSoft
| Criterion | EPAM Systems | ScienceSoft |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Full-time dedicated engineers, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs ScienceSoft
| Dimension | EPAM Systems | ScienceSoft |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Financial services, Healthcare & life sciences, Retail & e-commerce | Healthcare & life sciences, Financial services, Manufacturing |
| Best use cases | Adding a 15-person GenAI squad to a bank's existing platform team, Rolling out agentic workflows across several business units under one master agreement | Adding a data scientist to a healthcare analytics team, Staffing BI and ML roles for a manufacturer |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
EPAM Systems vs ScienceSoft: pros and cons
| EPAM Systems | |
|---|---|
| + | A Q2 2026 earnings call put its Anthropic-certified engineer count above 5,700, the largest verified GenAI bench on this list |
| + | Public-company reporting, audited financials and mature security reviews make vendor onboarding easier at banks and insurers |
| + | Can staff ten or more AI engineers in parallel across several time zones without running out of senior people |
| + | Deep data-platform practice means LLM work and the data engineering under it can come from one supplier |
| - | Rates are among the highest on this list and are only shared after scoping |
| - | Small requests for one or two engineers rarely get the same attention as large programs |
| - | Engagements tend to drift toward managed delivery, which moves decisions away from your own team |
| ScienceSoft | |
|---|---|
| + | Shares rates together with candidate CVs |
| + | Long history in healthcare and finance |
| + | Clear published hiring timeline |
| - | AI is a small part of a very wide catalog |
| - | Fewer GenAI specialists than AI-focused firms |
| - | Speed figures come from its own marketing |
Who should choose EPAM Systems?
A typical fit: adding a 15-person GenAI squad to a bank's existing platform team.
Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing.
Who should choose ScienceSoft?
A typical fit: adding a data scientist to a healthcare analytics team.
Publishes its staff augmentation timeline and process. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare & life sciences, Financial services, Manufacturing, Retail & e-commerce.
Decision matrix: EPAM Systems vs ScienceSoft
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; EPAM Systems rates higher overall |
| You want the supplier to own delivery as well as staffing | EPAM Systems |
| You need one expert part-time | Neither lists part-time experts; ask about reduced hours |
| You want to test an engineer before signing for months | Neither publishes a trial; ask for a short first term |
| Your budget is at the lower end | Compare: EPAM Systems (Not published) vs ScienceSoft (Not published) |
| You need overlap with U.S. working hours | Neither is nearshore; agree overlap hours up front |
| You need specialist depth in a specific vertical | EPAM Systems |
Use case fit: EPAM Systems vs ScienceSoft
| Use case | EPAM Systems fit | ScienceSoft fit | Winner |
|---|---|---|---|
| Adding a 15-person GenAI squad to a bank's existing platform team | Strong | Strong | Both equally |
| Rolling out agentic workflows across several business units under one master agreement | Strong | Limited | EPAM Systems |
| Adding a data scientist to a healthcare analytics team | Strong | Strong | Both equally |
| Staffing BI and ML roles for a manufacturer | Limited | Strong | ScienceSoft |
Verdict: EPAM Systems vs ScienceSoft
EPAM Systems (4.6/5) is the stronger overall choice for most AI Staff Augmentation projects. Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes.
ScienceSoft (3.7/5) is worth a look if you need staffing BI and ML roles for a manufacturer. If your situation matches that, ScienceSoft is a competitive option.
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EPAM Systems vs ScienceSoft FAQ
Is EPAM Systems better than ScienceSoft?
EPAM Systems (4.6/5) scores higher overall, but "better" depends on your use case. EPAM Systems's strongest advantage: a Q2 2026 earnings call put its Anthropic-certified engineer count above 5,700, the largest verified GenAI bench on this list. ScienceSoft's strongest advantage: shares rates together with candidate CVs.
How do EPAM Systems and ScienceSoft differ in pricing?
EPAM Systems uses time and materials for augmented engineers; dedicated team and managed program contracts; rates on request pricing. ScienceSoft uses hourly or monthly rates shared with cvs; time and materials pricing. 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: thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. ScienceSoft's primary differentiator is: publishes its staff augmentation timeline and process. They also differ in team size (61,000+ vs 750+), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Healthcare & life sciences vs Healthcare & life sciences, Financial services).
Verify all details directly with each company before making a decision.