EPAM Systems vs nCube: full comparison for 2026
Quick verdict
EPAM Systems (4.6/5) edges ahead of nCube (3.9/5) overall. EPAM Systems is the better choice for large enterprises, regulated industries, multi-team AI programs. nCube is the stronger option for companies building a long-term offshore AI team. The right choice depends on your project size, budget, and required tech stack.
EPAM Systems vs nCube: head-to-head summary
| Criterion | EPAM Systems | nCube |
|---|---|---|
| Founded | 1993 | 2008 |
| HQ | Newtown, Pennsylvania, USA | London, UK |
| Team size | 61,000+ | 50–249 staff; large external talent pool (per company) |
| 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 | Builds and runs a client-branded R&D team, including HR and office setup |
| Pricing model | Time and materials for augmented engineers; dedicated team and managed program contracts; rates on request | Monthly per-engineer team pricing; free vetting until candidates are chosen; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, PyTorch, TensorFlow |
| Industries served | Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing | Software & SaaS, Media, Financial services, Manufacturing |
EPAM Systems vs nCube: 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.
nCube
nCube was founded in 2008 and is registered in London, with its core R&D office in Kyiv and development offices in Warsaw and São Paulo. It builds dedicated teams and nearshore R&D centers, handling hiring, payroll, legal and HR for the client. The company says it can show first AI candidate profiles within 48 hours and build a team in two to six weeks, drawing on a pool of more than 50,000 AI, ML and data specialists (per company website; independently unverifiable). Named AI clients include Veritone and Fetch.ai.
Services and capabilities: EPAM Systems vs nCube
| Capability | EPAM Systems | nCube |
|---|---|---|
| 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 nCube
| Framework / platform | EPAM Systems | nCube |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Databricks | ✓ | N/A |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | ✓ |
Pricing comparison: EPAM Systems vs nCube
| Criterion | EPAM Systems | nCube |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Dedicated team, Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs nCube
| Dimension | EPAM Systems | nCube |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Financial services, Healthcare & life sciences, Retail & e-commerce | Software & SaaS, Media, Financial services |
| 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 | Setting up a five-person ML team in Eastern Europe, Building a computer-vision team for a media analytics product |
| Typical project type | Full-time dedicated engineers | Dedicated team |
EPAM Systems vs nCube: 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 |
| nCube | |
|---|---|
| + | Handles the HR, payroll and legal side of a remote team |
| + | AI client list includes Veritone and Fetch.ai |
| + | Vetting is free until you pick candidates |
| - | Core team is small relative to the talent pool it advertises |
| - | Two to six weeks is slower than marketplace matching |
| - | Contract notice terms are not published |
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 nCube?
A typical fit: setting up a five-person ML team in Eastern Europe.
Builds and runs a client-branded R&D team, including HR and office setup. Minimum engagement is not publicly disclosed. Works best with clients in Software & SaaS, Media, Financial services, Manufacturing.
Decision matrix: EPAM Systems vs nCube
| 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 nCube (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 nCube
| Use case | EPAM Systems fit | nCube fit | Winner |
|---|---|---|---|
| Adding a 15-person GenAI squad to a bank's existing platform team | Strong | Limited | EPAM Systems |
| Rolling out agentic workflows across several business units under one master agreement | Strong | Limited | EPAM Systems |
| Setting up a five-person ML team in Eastern Europe | Limited | Strong | nCube |
| Building a computer-vision team for a media analytics product | Limited | Strong | nCube |
Verdict: EPAM Systems vs nCube
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.
nCube (3.9/5) is worth a look if you need building a computer-vision team for a media analytics product. If your situation matches that, nCube is a competitive option.
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EPAM Systems vs nCube FAQ
Is EPAM Systems better than nCube?
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. nCube's strongest advantage: handles the HR, payroll and legal side of a remote team.
How do EPAM Systems and nCube differ in pricing?
EPAM Systems uses time and materials for augmented engineers; dedicated team and managed program contracts; rates on request pricing. nCube uses monthly per-engineer team pricing; free vetting until candidates are chosen; 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 nCube?
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 nCube?
EPAM Systems's primary differentiator is: thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. nCube's primary differentiator is: builds and runs a client-branded R&D team, including HR and office setup. They also differ in team size (61,000+ vs 50–249 staff; large external talent pool (per company)), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Healthcare & life sciences vs Software & SaaS, Media).
Verify all details directly with each company before making a decision.