Globant vs DataArt: full comparison for 2026
Quick verdict
Globant (4.1/5) edges ahead of DataArt (4.0/5) overall. Globant is the better choice for enterprises wanting AI capacity on a subscription model. DataArt is the stronger option for finance and healthcare firms extending data and AI teams. The right choice depends on your project size, budget, and required tech stack.
Globant vs DataArt: head-to-head summary
| Criterion | Globant | DataArt |
|---|---|---|
| Founded | 2003 | 1997 |
| HQ | Luxembourg | New York, New York, USA |
| Team size | 28,000+ | 5,000+ |
| Rating | 4.1 / 5 | 4.0 / 5 |
| Primary differentiator | Subscription-based AI Pods as an alternative to per-engineer billing | Nearly three decades of domain work in finance, healthcare and travel |
| Pricing model | AI Pods monthly subscription with token-based capacity; staff augmentation and SOW contracts; rates on request | Time and materials; dedicated teams; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, OpenAI, Azure ML | Python, Spark, Databricks |
| Industries served | Media, Financial services, Travel, Retail & e-commerce, Healthcare & life sciences | Financial services, Healthcare & life sciences, Travel, Media |
Globant vs DataArt: 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.
DataArt
DataArt was founded in New York in 1997 by Eugene Goland, who still leads it. Reported headcount ranges from about 4,000 to more than 6,000 across 30 to 40 locations. The firm builds data, analytics and AI platforms and works heavily in finance, healthcare and travel. Clients can bring in DataArt engineers as part of their own team or contract a full delivery team.
Services and capabilities: Globant vs DataArt
| Capability | Globant | DataArt |
|---|---|---|
| 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 DataArt
| Framework / platform | Globant | DataArt |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Databricks | ✓ | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Globant vs DataArt
| Criterion | Globant | DataArt |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated team, Managed delivery, Full-time dedicated engineers | 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 DataArt
| Dimension | Globant | DataArt |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Media, Financial services, Travel | Financial services, Healthcare & life sciences, Travel |
| Best use cases | Buying a monthly AI engineering pod for a marketing-tech roadmap, Staffing agent development across several brands | Extending a trading firm's data team with ML engineers, Building a clinical data platform before adding models |
| Typical project type | Dedicated team | Full-time dedicated engineers |
Globant vs DataArt: 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 |
| DataArt | |
|---|---|
| + | Deep domain knowledge in regulated sectors |
| + | Strong data-platform engineering supports AI work |
| + | Long client relationships suggest stable delivery |
| - | AI specialists are a small share of a broad workforce |
| - | Headcount figures vary considerably between sources |
| - | Engagements often lean toward managed delivery |
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 DataArt?
A typical fit: extending a trading firm's data team with ML engineers.
Nearly three decades of domain work in finance, healthcare and travel. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare & life sciences, Travel, Media.
Decision matrix: Globant vs DataArt
| 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 DataArt (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 DataArt
| Use case | Globant fit | DataArt 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 |
| Extending a trading firm's data team with ML engineers | Limited | Strong | DataArt |
| Building a clinical data platform before adding models | Limited | Strong | DataArt |
Verdict: Globant vs DataArt
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.
DataArt (4.0/5) is worth a look if you need building a clinical data platform before adding models. If your situation matches that, DataArt is a competitive option.
Related comparisons
Globant vs DataArt FAQ
Is Globant better than DataArt?
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. DataArt's strongest advantage: deep domain knowledge in regulated sectors.
How do Globant and DataArt differ in pricing?
Globant uses ai pods monthly subscription with token-based capacity; staff augmentation and sow contracts; rates on request pricing. DataArt uses time and materials; dedicated teams; 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 DataArt?
Globant 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 DataArt?
Globant's primary differentiator is: subscription-based AI Pods as an alternative to per-engineer billing. DataArt's primary differentiator is: nearly three decades of domain work in finance, healthcare and travel. They also differ in team size (28,000+ vs 5,000+), minimum engagement (Not published vs Not published), and primary industries served (Media, Financial services vs Financial services, Healthcare & life sciences).
Verify all details directly with each company before making a decision.