Globant vs Xenoss: full comparison for 2026
Quick verdict
Globant (4.1/5) edges ahead of Xenoss (3.8/5) overall. Globant is the better choice for enterprises wanting AI capacity on a subscription model. Xenoss is the stronger option for AdTech and MarTech firms needing real-time data plus AI. The right choice depends on your project size, budget, and required tech stack.
Globant vs Xenoss: head-to-head summary
| Criterion | Globant | Xenoss |
|---|---|---|
| Founded | 2003 | 2013 |
| HQ | Luxembourg | New York, New York, USA |
| Team size | 28,000+ | 100–200 |
| Rating | 4.1 / 5 | 3.8 / 5 |
| Primary differentiator | Subscription-based AI Pods as an alternative to per-engineer billing | Real-time, high-load data engineering from AdTech roots |
| Pricing model | AI Pods monthly subscription with token-based capacity; staff augmentation and SOW contracts; rates on request | Team extension and project pricing; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, OpenAI, Azure ML | Python, Kafka, Spark |
| Industries served | Media, Financial services, Travel, Retail & e-commerce, Healthcare & life sciences | Media, Retail & e-commerce, Software & SaaS |
Globant vs Xenoss: 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.
Xenoss
Xenoss was founded in 2013 by AdTech veterans and lists its headquarters in New York, with CEO Dmitry Sverdlik. Directories put headcount between 100 and 200. It specializes in AI and data engineering, including AI agents, real-time data systems and LLM knowledge bases, and favors small senior teams. Team extension appears in its history, but it does not run a dedicated staff augmentation offer.
Services and capabilities: Globant vs Xenoss
| Capability | Globant | Xenoss |
|---|---|---|
| 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 Xenoss
| Framework / platform | Globant | Xenoss |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| 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 | ✓ | N/A |
Pricing comparison: Globant vs Xenoss
| Criterion | Globant | Xenoss |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | 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: Globant vs Xenoss
| Dimension | Globant | Xenoss |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Media, Financial services, Travel | Media, Retail & e-commerce, Software & SaaS |
| Best use cases | Buying a monthly AI engineering pod for a marketing-tech roadmap, Staffing agent development across several brands | Adding real-time feature engineering for a bidding model, Building an LLM knowledge base on marketing data |
| Typical project type | Dedicated team | Dedicated team |
Globant vs Xenoss: 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 |
| Xenoss | |
|---|---|
| + | High-load, real-time data experience |
| + | Small senior teams with low management overhead |
| + | Builds agents and knowledge bases on its own data work |
| - | No dedicated staff augmentation page |
| - | Industry focus is narrow outside AdTech and MarTech |
| - | Headcount data varies |
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 Xenoss?
A typical fit: adding real-time feature engineering for a bidding model.
Real-time, high-load data engineering from AdTech roots. Minimum engagement is not publicly disclosed. Works best with clients in Media, Retail & e-commerce, Software & SaaS.
Decision matrix: Globant vs Xenoss
| 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 | Both; Globant 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: Globant (Not published) vs Xenoss (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 Xenoss
| Use case | Globant fit | Xenoss 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 |
| Adding real-time feature engineering for a bidding model | Limited | Strong | Xenoss |
| Building an LLM knowledge base on marketing data | Limited | Strong | Xenoss |
Verdict: Globant vs Xenoss
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.
Xenoss (3.8/5) is worth a look if you need building an LLM knowledge base on marketing data. If your situation matches that, Xenoss is a competitive option.
Related comparisons
Globant vs Xenoss FAQ
Is Globant better than Xenoss?
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. Xenoss's strongest advantage: High-load, real-time data experience.
How do Globant and Xenoss differ in pricing?
Globant uses ai pods monthly subscription with token-based capacity; staff augmentation and sow contracts; rates on request pricing. Xenoss uses team extension and project pricing; 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 Xenoss?
Xenoss 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 Xenoss?
Globant's primary differentiator is: subscription-based AI Pods as an alternative to per-engineer billing. Xenoss's primary differentiator is: Real-time, high-load data engineering from AdTech roots. They also differ in team size (28,000+ vs 100–200), minimum engagement (Not published vs Not published), and primary industries served (Media, Financial services vs Media, Retail & e-commerce).
Verify all details directly with each company before making a decision.