Azumo vs DataArt: full comparison for 2026
Quick verdict
Azumo (4.5/5) edges ahead of DataArt (4.0/5) overall. Azumo is the better choice for startups adding GenAI engineers on U.S. hours. DataArt is the stronger option for financial and travel firms needing long-lived dedicated 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, USA | New York, USA |
| Team size | 100–249 | 5,000–6,000 |
| Rating | 4.5 / 5 | 4.0 / 5 |
| Primary differentiator | Nearshore staffing with a hiring focus on GenAI and agent roles | Dedicated development centers with nearly 30 years of history |
| Pricing model | Monthly per engineer; dedicated team; project-based; rates on request | Dedicated development center; time and materials; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | OpenAI, LangChain, Hugging Face | Python, Azure ML, AWS |
| Industries served | SaaS, Fintech, Healthcare, Media | Fintech, Travel, Healthcare, Media |
Azumo vs DataArt: overview
Azumo
Azumo was founded in San Francisco in 2016 by former investment banker Chike Agbai, whose first client was Twitter. Its engineers are based in more than 20 Latin American countries and work U.S. hours. The company sells three formats: staff augmentation alongside an existing team, dedicated teams, and project delivery, and its recent hiring is weighted toward generative-AI, agent and forward-deployed engineering roles. Headcount estimates range from about 80 to just over 100 depending on the source.
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.
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 | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | ✓ |
| AWS SageMaker | N/A | N/A |
| Azure ML | ✓ | ✓ |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | N/A | ✓ |
Pricing comparison: Azumo vs DataArt
| Criterion | Azumo | DataArt |
|---|---|---|
| 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: Azumo vs DataArt
| Dimension | Azumo | DataArt |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, Healthcare | Fintech, Travel, Healthcare |
| Best use cases | Adding an LLM engineer to ship a first GenAI feature, Hiring an agent developer to prototype internal automation | Setting up a long-term dedicated team that includes ML engineers, Adding an LLM engineer to a SaaS copilot project |
| Typical project type | Full-time dedicated engineers | Dedicated team |
Azumo vs DataArt: pros and cons
| Azumo | |
|---|---|
| + | Hiring pattern shows real investment in LLM and agent engineering, beyond generic web developers |
| + | No long-term commitment required for augmentation seats |
| + | U.S. time zones and a U.S.-based management team |
| + | Small enough that founders and senior staff stay involved in client accounts |
| - | Headcount is modest, so very large teams may take longer to assemble |
| - | Public detail on how candidates are technically screened is thin |
| - | No published rates |
| 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 |
Who should choose Azumo?
A typical fit: adding an LLM engineer to ship a first GenAI feature.
Nearshore staffing with a hiring focus on GenAI and agent roles. Minimum engagement is not publicly disclosed. Works best with clients in SaaS, Fintech, Healthcare, Media.
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.
Decision matrix: Azumo vs DataArt
| 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 | Azumo |
| Your budget is at the lower end | Compare: Azumo (Not disclosed) vs DataArt (Not disclosed) |
| You need specialist depth in a specific vertical | Azumo |
| 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: Azumo vs DataArt
| Use case | Azumo fit | DataArt fit | Winner |
|---|---|---|---|
| Adding an LLM engineer to ship a first GenAI feature | Strong | Strong | Both equally |
| Hiring an agent developer to prototype internal automation | Strong | Limited | Azumo |
| Setting up a long-term dedicated team that includes ML engineers | Limited | Strong | DataArt |
| Adding an LLM engineer to a SaaS copilot project | Strong | Strong | Both equally |
Verdict: Azumo vs DataArt
Azumo (4.5/5) is the stronger overall choice for most AI Staffing projects. Nearshore staffing with a hiring focus on GenAI and agent roles.
DataArt (4.0/5) is worth a look if you need adding an LLM engineer to a SaaS copilot project. If your situation matches that, DataArt is a competitive option.
Related comparisons
Azumo vs DataArt FAQ
Is Azumo better than DataArt?
Azumo (4.5/5) scores higher overall, but "better" depends on your use case. Azumo's strongest advantage: hiring pattern shows real investment in LLM and agent engineering, beyond generic web developers. DataArt's strongest advantage: long-running dedicated teams with low churn.
How do Azumo and DataArt differ in pricing?
Azumo uses monthly per engineer; dedicated team; project-based; rates on request pricing. DataArt uses dedicated development center; time and materials; 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?
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 Azumo and DataArt?
Azumo's primary differentiator is: nearshore staffing with a hiring focus on GenAI and agent roles. DataArt's primary differentiator is: dedicated development centers with nearly 30 years of history. They also differ in team size (100–249 vs 5,000–6,000), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (SaaS, Fintech vs Fintech, Travel).
Verify all details directly with each agency before making a decision.