DataArt vs Nearsure: full comparison for 2026
Quick verdict
DataArt (4.0/5) edges ahead of Nearsure (3.9/5) overall. DataArt is the better choice for finance and healthcare firms extending data and AI teams. Nearsure is the stronger option for U.S. teams adding Latin American GenAI developers. The right choice depends on your project size, budget, and required tech stack.
DataArt vs Nearsure: head-to-head summary
| Criterion | DataArt | Nearsure |
|---|---|---|
| Founded | 1997 | 2018 |
| HQ | New York, New York, USA | Montevideo, Uruguay (U.S.-incorporated) |
| Team size | 5,000+ | 500–850 (sources vary) |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Nearly three decades of domain work in finance, healthcare and travel | Augmentation-first business model with a growing AI studio |
| Pricing model | Time and materials; dedicated teams; rates on request | Monthly staff augmentation rates; project development; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Spark, Databricks | Python, OpenAI, AWS |
| Industries served | Financial services, Healthcare & life sciences, Travel, Media | Software & SaaS, Healthcare & life sciences, Financial services |
DataArt vs Nearsure: overview
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.
Nearsure
Nearsure started operations in 2018 under co-founder and CEO Giuliana Corbo and is described by Bloomberg as a Uruguayan IT services company, though it is incorporated in the United States. Bloomberg reported a 2024 plan to grow to about 850 staff. Remote staff augmentation for U.S. clients is its core business, and the service list has widened to generative AI, cloud migration and Salesforce work through a Data & AI studio.
Services and capabilities: DataArt vs Nearsure
| Capability | DataArt | Nearsure |
|---|---|---|
| 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 Nearsure
| Framework / platform | DataArt | Nearsure |
|---|---|---|
| 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 | ✓ | N/A |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: DataArt vs Nearsure
| Criterion | DataArt | Nearsure |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Full-time dedicated engineers, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataArt vs Nearsure
| Dimension | DataArt | Nearsure |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Healthcare & life sciences, Travel | Software & SaaS, Healthcare & life sciences, Financial services |
| Best use cases | Extending a trading firm's data team with ML engineers, Building a clinical data platform before adding models | Adding a GenAI developer to a U.S. SaaS team, Staffing data engineers for a cloud migration |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
DataArt vs Nearsure: pros and cons
| 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 |
| Nearsure | |
|---|---|
| + | Staff augmentation is the main business, so processes are built around it |
| + | Latin American engineers on U.S. hours |
| + | Has been profitable since early in its history, per AméricaEconomía |
| - | AI is a newer studio inside a general staffing company |
| - | Headcount reports vary between 525 and 850 |
| - | HQ location differs between sources |
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.
Who should choose Nearsure?
A typical fit: adding a GenAI developer to a U.S. SaaS team.
Augmentation-first business model with a growing AI studio. Minimum engagement is not publicly disclosed. Works best with clients in Software & SaaS, Healthcare & life sciences, Financial services.
Decision matrix: DataArt vs Nearsure
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; DataArt rates higher overall |
| You want the supplier to own delivery as well as staffing | DataArt |
| 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: DataArt (Not published) vs Nearsure (Not published) |
| You need overlap with U.S. working hours | Nearsure |
| You need specialist depth in a specific vertical | DataArt |
Use case fit: DataArt vs Nearsure
| Use case | DataArt fit | Nearsure fit | Winner |
|---|---|---|---|
| Extending a trading firm's data team with ML engineers | Strong | Limited | DataArt |
| Building a clinical data platform before adding models | Strong | Limited | DataArt |
| Adding a GenAI developer to a U.S. SaaS team | Strong | Strong | Both equally |
| Staffing data engineers for a cloud migration | Limited | Strong | Nearsure |
Verdict: DataArt vs Nearsure
DataArt (4.0/5) is the stronger overall choice for most AI Staff Augmentation projects. Nearly three decades of domain work in finance, healthcare and travel.
Nearsure (3.9/5) is worth a look if you need staffing data engineers for a cloud migration. If your situation matches that, Nearsure is a competitive option.
Related comparisons
DataArt vs Nearsure FAQ
Is DataArt better than Nearsure?
DataArt (4.0/5) scores higher overall, but "better" depends on your use case. DataArt's strongest advantage: deep domain knowledge in regulated sectors. Nearsure's strongest advantage: staff augmentation is the main business, so processes are built around it.
How do DataArt and Nearsure differ in pricing?
DataArt uses time and materials; dedicated teams; rates on request pricing. Nearsure uses monthly staff augmentation rates; project development; 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: DataArt or Nearsure?
Nearsure 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 DataArt and Nearsure?
DataArt's primary differentiator is: nearly three decades of domain work in finance, healthcare and travel. Nearsure's primary differentiator is: augmentation-first business model with a growing AI studio. They also differ in team size (5,000+ vs 500–850 (sources vary)), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Healthcare & life sciences vs Software & SaaS, Healthcare & life sciences).
Verify all details directly with each company before making a decision.