EPAM Systems vs Globant: full comparison for 2026
Quick verdict
EPAM Systems (4.6/5) edges ahead of Globant (4.1/5) overall. EPAM Systems is the better choice for large enterprises, regulated industries, multi-team AI programs. Globant is the stronger option for enterprises wanting AI capacity on a subscription model. The right choice depends on your project size, budget, and required tech stack.
EPAM Systems vs Globant: head-to-head summary
| Criterion | EPAM Systems | Globant |
|---|---|---|
| Founded | 1993 | 2003 |
| HQ | Newtown, Pennsylvania, USA | Luxembourg |
| Team size | 61,000+ | 28,000+ |
| Rating | 4.6 / 5 | 4.1 / 5 |
| Primary differentiator | Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes | Subscription-based AI Pods as an alternative to per-engineer billing |
| Pricing model | Time and materials for augmented engineers; dedicated team and managed program contracts; rates on request | AI Pods monthly subscription with token-based capacity; staff augmentation and SOW contracts; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, OpenAI, Azure ML |
| Industries served | Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing | Media, Financial services, Travel, Retail & e-commerce, Healthcare & life sciences |
EPAM Systems vs Globant: 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.
Globant
Globant was founded in Buenos Aires in 2003 and is now headquartered in Luxembourg. The NYSE-listed company reported 28,773 employees at the end of 2025. In 2025 it launched AI Pods, a monthly subscription for AI-assisted engineering capacity metered by tokens. Third-party reviews say classic staff augmentation runs mainly through Belatrix, a firm Globant acquired, while large accounts usually buy managed pods or statements of work.
Services and capabilities: EPAM Systems vs Globant
| Capability | EPAM Systems | Globant |
|---|---|---|
| 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 Globant
| Framework / platform | EPAM Systems | Globant |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Databricks | ✓ | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | ✓ |
Pricing comparison: EPAM Systems vs Globant
| Criterion | EPAM Systems | Globant |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Dedicated team, Managed delivery, Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs Globant
| Dimension | EPAM Systems | Globant |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Financial services, Healthcare & life sciences, Retail & e-commerce | Media, Financial services, Travel |
| 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 | Buying a monthly AI engineering pod for a marketing-tech roadmap, Staffing agent development across several brands |
| Typical project type | Full-time dedicated engineers | Dedicated team |
EPAM Systems vs Globant: 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 |
| Globant | |
|---|---|
| + | AI Pods give finance teams a predictable monthly cost |
| + | Large Latin American delivery footprint on U.S.-friendly hours |
| + | Public-company governance suits procurement-heavy buyers |
| - | Individual staff augmentation is a side channel run largely through the acquired Belatrix business |
| - | Headcount fell about 8% during 2025, according to Bloomberg Línea |
| - | Pod and token-based pricing is hard to compare with per-engineer quotes |
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 Globant?
A typical fit: buying a monthly AI engineering pod for a marketing-tech roadmap.
Subscription-based AI Pods as an alternative to per-engineer billing. Minimum engagement is not publicly disclosed. Works best with clients in Media, Financial services, Travel, Retail & e-commerce, Healthcare & life sciences.
Decision matrix: EPAM Systems vs Globant
| 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 Globant (Not published) |
| You need overlap with U.S. working hours | Globant |
| You need specialist depth in a specific vertical | EPAM Systems |
Use case fit: EPAM Systems vs Globant
| Use case | EPAM Systems fit | Globant 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 |
| Buying a monthly AI engineering pod for a marketing-tech roadmap | Limited | Strong | Globant |
| Staffing agent development across several brands | Limited | Strong | Globant |
Verdict: EPAM Systems vs Globant
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.
Globant (4.1/5) is worth a look if you need staffing agent development across several brands. If your situation matches that, Globant is a competitive option.
Related comparisons
EPAM Systems vs Globant FAQ
Is EPAM Systems better than Globant?
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. Globant's strongest advantage: AI Pods give finance teams a predictable monthly cost.
How do EPAM Systems and Globant differ in pricing?
EPAM Systems uses time and materials for augmented engineers; dedicated team and managed program contracts; rates on request pricing. Globant uses ai pods monthly subscription with token-based capacity; staff augmentation and sow contracts; 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 Globant?
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 Globant?
EPAM Systems's primary differentiator is: thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. Globant's primary differentiator is: subscription-based AI Pods as an alternative to per-engineer billing. They also differ in team size (61,000+ vs 28,000+), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Healthcare & life sciences vs Media, Financial services).
Verify all details directly with each company before making a decision.