EPAM Systems vs STX Next: full comparison for 2026
Quick verdict
EPAM Systems (4.6/5) edges ahead of STX Next (3.9/5) overall. EPAM Systems is the better choice for large enterprises, regulated industries, multi-team AI programs. STX Next is the stronger option for python codebases adding LLM and data engineers. The right choice depends on your project size, budget, and required tech stack.
EPAM Systems vs STX Next: head-to-head summary
| Criterion | EPAM Systems | STX Next |
|---|---|---|
| Founded | 1993 | 2005 |
| HQ | Newtown, Pennsylvania, USA | Poznań, Poland |
| Team size | 61,000+ | 250–500 |
| Rating | 4.6 / 5 | 3.9 / 5 |
| Primary differentiator | Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes | Python specialization applied to data and AI delivery |
| Pricing model | Time and materials for augmented engineers; dedicated team and managed program contracts; rates on request | Time and materials; dedicated teams; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Django, FastAPI |
| Industries served | Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing | Financial services, Software & SaaS, Media, Healthcare & life sciences |
EPAM Systems vs STX Next: 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.
STX Next
STX Next was founded in Poznań in March 2005 and built its reputation as one of Europe's largest Python software houses. Its 2025 anniversary release cites about 500 staff and more than 1,000 delivered projects, with delivery centers in Poland and Mexico. The firm now presents itself as a data and AI consultancy, and Python's dominance in ML makes its bench a natural fit for model and data work.
Services and capabilities: EPAM Systems vs STX Next
| Capability | EPAM Systems | STX Next |
|---|---|---|
| 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 STX Next
| Framework / platform | EPAM Systems | STX Next |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | ✓ |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Databricks | ✓ | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: EPAM Systems vs STX Next
| Criterion | EPAM Systems | STX Next |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Full-time dedicated engineers, Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs STX Next
| Dimension | EPAM Systems | STX Next |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Financial services, Healthcare & life sciences, Retail & e-commerce | Financial services, Software & SaaS, Media |
| 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 LLM features to a Django product, Building data jobs in Python for analytics |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
EPAM Systems vs STX Next: 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 |
| STX Next | |
|---|---|
| + | Python depth fits most AI codebases |
| + | Delivery from both Poland and Mexico |
| + | Long history of extending client teams |
| - | AI positioning is recent compared with its Python history |
| - | Polish rates are above Ukrainian and Latin American options |
| - | Fewer specialist roles such as computer vision |
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 STX Next?
A typical fit: adding LLM features to a Django product.
Python specialization applied to data and AI delivery. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Software & SaaS, Media, Healthcare & life sciences.
Decision matrix: EPAM Systems vs STX Next
| 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 | Both; EPAM Systems rates higher overall |
| 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 STX Next (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 STX Next
| Use case | EPAM Systems fit | STX Next 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 LLM features to a Django product | Strong | Strong | Both equally |
| Building data jobs in Python for analytics | Limited | Strong | STX Next |
Verdict: EPAM Systems vs STX Next
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.
STX Next (3.9/5) is worth a look if you need building data jobs in Python for analytics. If your situation matches that, STX Next is a competitive option.
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EPAM Systems vs STX Next FAQ
Is EPAM Systems better than STX Next?
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. STX Next's strongest advantage: python depth fits most AI codebases.
How do EPAM Systems and STX Next differ in pricing?
EPAM Systems uses time and materials for augmented engineers; dedicated team and managed program contracts; rates on request pricing. STX Next uses time and materials; dedicated teams; rates on request 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 STX Next?
STX Next 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 STX Next?
EPAM Systems's primary differentiator is: thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. STX Next's primary differentiator is: python specialization applied to data and AI delivery. They also differ in team size (61,000+ vs 250–500), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Healthcare & life sciences vs Financial services, Software & SaaS).
Verify all details directly with each company before making a decision.