Globant vs nCube: full comparison for 2026
Quick verdict
Globant (4.1/5) edges ahead of nCube (3.9/5) overall. Globant is the better choice for enterprises wanting AI capacity on a subscription model. 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.
Globant vs nCube: head-to-head summary
| Criterion | Globant | nCube |
|---|---|---|
| Founded | 2003 | 2008 |
| HQ | Luxembourg | London, UK |
| Team size | 28,000+ | 50–249 staff; large external talent pool (per company) |
| Rating | 4.1 / 5 | 3.9 / 5 |
| Primary differentiator | Subscription-based AI Pods as an alternative to per-engineer billing | Builds and runs a client-branded R&D team, including HR and office setup |
| Pricing model | AI Pods monthly subscription with token-based capacity; staff augmentation and SOW 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, OpenAI, Azure ML | Python, PyTorch, TensorFlow |
| Industries served | Media, Financial services, Travel, Retail & e-commerce, Healthcare & life sciences | Software & SaaS, Media, Financial services, Manufacturing |
Globant vs nCube: overview
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.
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: Globant vs nCube
| Capability | Globant | 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: Globant vs nCube
| Framework / platform | Globant | nCube |
|---|---|---|
| PyTorch | N/A | ✓ |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | 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: Globant vs nCube
| Criterion | Globant | nCube |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated team, Managed delivery, Full-time dedicated engineers | Dedicated team, Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Globant vs nCube
| Dimension | Globant | nCube |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Media, Financial services, Travel | Software & SaaS, Media, Financial services |
| Best use cases | Buying a monthly AI engineering pod for a marketing-tech roadmap, Staffing agent development across several brands | Setting up a five-person ML team in Eastern Europe, Building a computer-vision team for a media analytics product |
| Typical project type | Dedicated team | Dedicated team |
Globant vs nCube: pros and cons
| 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 |
| 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 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.
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: Globant vs nCube
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; Globant rates higher overall |
| You want the supplier to own delivery as well as staffing | Globant |
| 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: Globant (Not published) vs nCube (Not published) |
| You need overlap with U.S. working hours | Globant |
| You need specialist depth in a specific vertical | Globant |
Use case fit: Globant vs nCube
| Use case | Globant fit | nCube fit | Winner |
|---|---|---|---|
| Buying a monthly AI engineering pod for a marketing-tech roadmap | Strong | Limited | Globant |
| Staffing agent development across several brands | Strong | Limited | Globant |
| 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: Globant vs nCube
Globant (4.1/5) is the stronger overall choice for most AI Staff Augmentation projects. Subscription-based AI Pods as an alternative to per-engineer billing.
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.
Related comparisons
Globant vs nCube FAQ
Is Globant better than nCube?
Globant (4.1/5) scores higher overall, but "better" depends on your use case. Globant's strongest advantage: AI Pods give finance teams a predictable monthly cost. nCube's strongest advantage: handles the HR, payroll and legal side of a remote team.
How do Globant and nCube differ in pricing?
Globant uses ai pods monthly subscription with token-based capacity; staff augmentation and sow 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: Globant or nCube?
nCube 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 Globant and nCube?
Globant's primary differentiator is: subscription-based AI Pods as an alternative to per-engineer billing. 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 (28,000+ vs 50–249 staff; large external talent pool (per company)), minimum engagement (Not published vs Not published), and primary industries served (Media, Financial services vs Software & SaaS, Media).
Verify all details directly with each company before making a decision.