Azumo vs DataArt: full comparison for 2026
Quick verdict
Azumo (4.1/5) edges ahead of DataArt (4.0/5) overall. Azumo is the better choice for nearshore LLM and NLP builds for U.S. mid-market. 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.
Azumo vs DataArt: head-to-head summary
| Criterion | Azumo | DataArt |
|---|---|---|
| Founded | 2016 | 1997 |
| HQ | San Francisco, California, USA | New York, New York, USA |
| Team size | 100–500 (sources vary) | 5,000+ |
| Rating | 4.1 / 5 | 4.0 / 5 |
| Primary differentiator | A nearshore team that also builds its own NLP products | Nearly three decades of domain work in finance, healthcare and travel |
| Pricing model | Monthly rates for augmented engineers; dedicated teams; project pricing; rates on request | Time and materials; dedicated teams; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, LangChain, OpenAI | Python, Spark, Databricks |
| Industries served | Healthcare & life sciences, Media, Software & SaaS, Financial services | Financial services, Healthcare & life sciences, Travel, Media |
Azumo vs DataArt: overview
Azumo
Azumo is headquartered in San Francisco and has built AI-driven applications since 2016, with most of its engineers in Latin America. Directory headcounts range from under 100 to several hundred people. It offers staff augmentation, dedicated teams and full product outsourcing, and it also maintains its own AI products, including an NLU toolkit. Named clients include Meta and UnitedHealth (per company website; independently unverifiable).
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: Azumo vs DataArt
| Capability | Azumo | 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: Azumo vs DataArt
| Framework / platform | Azumo | DataArt |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Azumo vs DataArt
| Criterion | Azumo | DataArt |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, 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: Azumo vs DataArt
| Dimension | Azumo | DataArt |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare & life sciences, Media, Software & SaaS | Financial services, Healthcare & life sciences, Travel |
| Best use cases | Adding a conversational-AI engineer to a healthcare app team, Building a document-search assistant on internal knowledge | Extending a trading firm's data team with ML engineers, Building a clinical data platform before adding models |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
Azumo vs DataArt: pros and cons
| Azumo | |
|---|---|
| + | Its own AI products show applied NLP experience |
| + | Latin American engineers share U.S. working hours |
| + | Flexible mix of augmentation and project delivery |
| - | Headcount reports vary widely, so ask how many AI engineers are actually on staff |
| - | Smaller bench than the large nearshore firms on this list |
| - | Founding year differs across sources (2013 or 2016) |
| 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 Azumo?
A typical fit: adding a conversational-AI engineer to a healthcare app team.
A nearshore team that also builds its own NLP products. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare & life sciences, Media, Software & SaaS, Financial services.
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: Azumo vs DataArt
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; Azumo rates higher overall |
| You want the supplier to own delivery as well as staffing | Both; Azumo 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: Azumo (Not published) vs DataArt (Not published) |
| You need overlap with U.S. working hours | Azumo |
| You need specialist depth in a specific vertical | Azumo |
Use case fit: Azumo vs DataArt
| Use case | Azumo fit | DataArt fit | Winner |
|---|---|---|---|
| Adding a conversational-AI engineer to a healthcare app team | Strong | Strong | Both equally |
| Building a document-search assistant on internal knowledge | Strong | Strong | Both equally |
| Extending a trading firm's data team with ML engineers | Strong | Strong | Both equally |
| Building a clinical data platform before adding models | Strong | Strong | Both equally |
Verdict: Azumo vs DataArt
Azumo (4.1/5) is the stronger overall choice for most AI Staff Augmentation projects. A nearshore team that also builds its own NLP products.
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
Azumo vs DataArt FAQ
Is Azumo better than DataArt?
Azumo (4.1/5) scores higher overall, but "better" depends on your use case. Azumo's strongest advantage: its own AI products show applied NLP experience. DataArt's strongest advantage: deep domain knowledge in regulated sectors.
How do Azumo and DataArt differ in pricing?
Azumo uses monthly rates for augmented engineers; dedicated teams; project pricing; 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: Azumo or DataArt?
Azumo 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 Azumo and DataArt?
Azumo's primary differentiator is: a nearshore team that also builds its own NLP products. DataArt's primary differentiator is: nearly three decades of domain work in finance, healthcare and travel. They also differ in team size (100–500 (sources vary) vs 5,000+), minimum engagement (Not published vs Not published), and primary industries served (Healthcare & life sciences, Media vs Financial services, Healthcare & life sciences).
Verify all details directly with each company before making a decision.