Xenoss vs Globant: full comparison for 2026
Quick verdict
Xenoss (4.3/5) edges ahead of Globant (3.9/5) overall. Xenoss is the better choice for ad-tech and high-volume data teams. Globant is the stronger option for enterprises open to outcome-priced AI delivery. The right choice depends on your project size, budget, and required tech stack.
Xenoss vs Globant: head-to-head summary
| Criterion | Xenoss | Globant |
|---|---|---|
| Founded | 2013 | 2003 |
| HQ | New York, USA | Luxembourg (operations centered in Buenos Aires) |
| Team size | 50–249 | 28,500 |
| Rating | 4.3 / 5 | 3.9 / 5 |
| Primary differentiator | Data engineers with ad-tech throughput experience | Token-subscription pricing in place of seat-based staffing |
| Pricing model | Time and materials; staff augmentation; rates on request | AI Pods subscription based on token consumption; traditional dedicated teams |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, Apache Spark, Kafka | Claude, OpenAI, Gemini |
| Industries served | Ad tech, Media, Fintech, Retail and e-commerce | Media, Fintech, Retail, Travel, Healthcare |
Xenoss vs Globant: overview
Xenoss
Xenoss was founded in 2013 by ad-tech veterans led by CEO Dmitry Sverdlik and is based in New York, with offices in London and Kyiv. It describes itself as a specialized AI and data-engineering company, and Clutch places it in the 50–249 employee band. Client reviews describe staff augmentation in practice: one London ad-tech client hired Xenoss after failing to find engineers locally, and Xenoss sourced candidates from Ukraine and integrated them into the in-house team. Its background in high-throughput ad-tech systems shows in its data-engineering work.
Globant
Globant was founded in Buenos Aires in 2003 and is incorporated in Luxembourg, with about 28,500 employees as of mid-2026. Since June 2025 it has sold AI Pods, a subscription priced on token consumption in which Globant experts supervise AI-agent workflows that produce software. In June 2026 it announced a multi-year alliance with Anthropic and joined the Claude Partner Network as a preferred services partner. The pod model is managed delivery, so buyers looking for classic seat-based staffing should ask about it specifically.
Services and capabilities: Xenoss vs Globant
| Capability | Xenoss | 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: Xenoss vs Globant
| Framework / platform | Xenoss | Globant |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | ✓ |
| Databricks | ✓ | N/A |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Xenoss vs Globant
| Criterion | Xenoss | Globant |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Xenoss vs Globant
| Dimension | Xenoss | Globant |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Ad tech, Media, Fintech | Media, Fintech, Retail |
| Best use cases | Adding streaming-data engineers ahead of an ML launch, Placing ML engineers in a bidding or attribution product | Buying AI-assisted engineering capacity on a subscription, Large LatAm-based teams for media and entertainment companies |
| Typical project type | Full-time dedicated engineers | Dedicated team |
Xenoss vs Globant: pros and cons
| Xenoss | |
|---|---|
| + | Strong on real-time data infrastructure that ML features depend on |
| + | Has placed engineers into UK teams that struggled to hire locally |
| + | Senior leadership comes from the industry it serves most |
| + | Covers both data engineering and model work |
| - | Ad-tech focus is narrower than general AI staffing |
| - | Mid-sized bench |
| - | Rates are not public |
| Globant | |
|---|---|
| + | Novel pricing model tied to delivered output |
| + | Large LatAm workforce in U.S.-friendly time zones |
| + | Anthropic alliance gives early access to Claude tooling |
| - | Pods are managed delivery; individual augmentation is secondary |
| - | Company is in the middle of a strategy shift after a steep share-price fall |
| - | Enterprise sales cycle |
Who should choose Xenoss?
A typical fit: adding streaming-data engineers ahead of an ML launch.
Data engineers with ad-tech throughput experience. Minimum engagement is not publicly disclosed. Works best with clients in Ad tech, Media, Fintech, Retail and e-commerce.
Who should choose Globant?
A typical fit: buying AI-assisted engineering capacity on a subscription.
Token-subscription pricing in place of seat-based staffing. Minimum engagement is not publicly disclosed. Works best with clients in Media, Fintech, Retail, Travel, Healthcare.
Decision matrix: Xenoss vs Globant
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | Xenoss |
| Your budget is at the lower end | Compare: Xenoss (Not disclosed) vs Globant (Not disclosed) |
| You need specialist depth in a specific vertical | Globant |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: Xenoss vs Globant
| Use case | Xenoss fit | Globant fit | Winner |
|---|---|---|---|
| Adding streaming-data engineers ahead of an ML launch | Strong | Limited | Xenoss |
| Placing ML engineers in a bidding or attribution product | Strong | Limited | Xenoss |
| Buying AI-assisted engineering capacity on a subscription | Limited | Strong | Globant |
| Large LatAm-based teams for media and entertainment companies | Limited | Strong | Globant |
Verdict: Xenoss vs Globant
Xenoss (4.3/5) is the stronger overall choice for most AI Staffing projects. Data engineers with ad-tech throughput experience.
Globant (3.9/5) is worth a look if you need large LatAm-based teams for media and entertainment companies. If your situation matches that, Globant is a competitive option.
Related comparisons
Xenoss vs Globant FAQ
Is Xenoss better than Globant?
Xenoss (4.3/5) scores higher overall, but "better" depends on your use case. Xenoss's strongest advantage: strong on real-time data infrastructure that ML features depend on. Globant's strongest advantage: novel pricing model tied to delivered output.
How do Xenoss and Globant differ in pricing?
Xenoss uses time and materials; staff augmentation; rates on request pricing. Globant uses ai pods subscription based on token consumption; traditional dedicated teams pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Xenoss or Globant?
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 agency before shortlisting.
What are the main differences between Xenoss and Globant?
Xenoss's primary differentiator is: data engineers with ad-tech throughput experience. Globant's primary differentiator is: token-subscription pricing in place of seat-based staffing. They also differ in team size (50–249 vs 28,500), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Ad tech, Media vs Media, Fintech).
Verify all details directly with each agency before making a decision.