DataArt vs Globant: full comparison for 2026
Quick verdict
DataArt (4.0/5) edges ahead of Globant (3.9/5) overall. DataArt is the better choice for financial and travel firms needing long-lived dedicated 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.
DataArt vs Globant: head-to-head summary
| Criterion | DataArt | Globant |
|---|---|---|
| Founded | 1997 | 2003 |
| HQ | New York, USA | Luxembourg (operations centered in Buenos Aires) |
| Team size | 5,000–6,000 | 28,500 |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Dedicated development centers with nearly 30 years of history | Token-subscription pricing in place of seat-based staffing |
| Pricing model | Dedicated development center; time and materials; rates on request | AI Pods subscription based on token consumption; traditional dedicated teams |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, Azure ML, AWS | Claude, OpenAI, Gemini |
| Industries served | Fintech, Travel, Healthcare, Media | Media, Fintech, Retail, Travel, Healthcare |
DataArt vs Globant: overview
DataArt
DataArt was founded in New York in 1997 by Eugene Goland and now employs between 5,000 and 6,000 people across more than 40 locations. Its Dedicated Development Center model staffs a team that works on one client's project only. An AI/ML group is actively hiring, with recent roles including a lead AI/ML engineer for an HR copilot built for a French SaaS client.
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: DataArt vs Globant
| Capability | DataArt | 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: DataArt vs Globant
| Framework / platform | DataArt | Globant |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | ✓ |
| AWS SageMaker | N/A | N/A |
| Azure ML | ✓ | ✓ |
| Databricks | ✓ | N/A |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: DataArt vs Globant
| Criterion | DataArt | Globant |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Managed delivery | Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataArt vs Globant
| Dimension | DataArt | Globant |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Fintech, Travel, Healthcare | Media, Fintech, Retail |
| Best use cases | Setting up a long-term dedicated team that includes ML engineers, Adding an LLM engineer to a SaaS copilot project | Buying AI-assisted engineering capacity on a subscription, Large LatAm-based teams for media and entertainment companies |
| Typical project type | Dedicated team | Dedicated team |
DataArt vs Globant: pros and cons
| DataArt | |
|---|---|
| + | Long-running dedicated teams with low churn |
| + | Strong presence in finance and travel |
| + | Wide location choice |
| - | Built for multi-year centers more than quick single hires |
| - | AI/ML group is still growing |
| - | Enterprise pricing |
| 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 DataArt?
A typical fit: setting up a long-term dedicated team that includes ML engineers.
Dedicated development centers with nearly 30 years of history. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Travel, Healthcare, Media.
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: DataArt 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 | DataArt |
| Your budget is at the lower end | Compare: DataArt (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: DataArt vs Globant
| Use case | DataArt fit | Globant fit | Winner |
|---|---|---|---|
| Setting up a long-term dedicated team that includes ML engineers | Strong | Limited | DataArt |
| Adding an LLM engineer to a SaaS copilot project | Strong | Limited | DataArt |
| Buying AI-assisted engineering capacity on a subscription | Limited | Strong | Globant |
| Large LatAm-based teams for media and entertainment companies | Limited | Strong | Globant |
Verdict: DataArt vs Globant
DataArt (4.0/5) is the stronger overall choice for most AI Staffing projects. Dedicated development centers with nearly 30 years of history.
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
DataArt vs Globant FAQ
Is DataArt better than Globant?
DataArt (4.0/5) scores higher overall, but "better" depends on your use case. DataArt's strongest advantage: long-running dedicated teams with low churn. Globant's strongest advantage: novel pricing model tied to delivered output.
How do DataArt and Globant differ in pricing?
DataArt uses dedicated development center; time and materials; 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: DataArt or Globant?
DataArt 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 DataArt and Globant?
DataArt's primary differentiator is: dedicated development centers with nearly 30 years of history. Globant's primary differentiator is: token-subscription pricing in place of seat-based staffing. They also differ in team size (5,000–6,000 vs 28,500), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Fintech, Travel vs Media, Fintech).
Verify all details directly with each agency before making a decision.