Xenoss vs DataArt: full comparison for 2026
Quick verdict
Xenoss (4.3/5) edges ahead of DataArt (4.0/5) overall. Xenoss is the better choice for ad-tech and high-volume data teams. 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.
Xenoss vs DataArt: head-to-head summary
| Criterion | Xenoss | DataArt |
|---|---|---|
| Founded | 2013 | 1997 |
| HQ | New York, USA | New York, USA |
| Team size | 50–249 | 5,000–6,000 |
| Rating | 4.3 / 5 | 4.0 / 5 |
| Primary differentiator | Data engineers with ad-tech throughput experience | Dedicated development centers with nearly 30 years of history |
| Pricing model | Time and materials; staff augmentation; rates on request | Dedicated development center; time and materials; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, Apache Spark, Kafka | Python, Azure ML, AWS |
| Industries served | Ad tech, Media, Fintech, Retail and e-commerce | Fintech, Travel, Healthcare, Media |
Xenoss vs DataArt: overview
Xenoss
Xenoss was founded in 2013 by ad-tech veterans led by CEO Dmitry Sverdlik and is based in New York, with offices in London and Kyiv. It describes itself as a specialized AI and data-engineering company, and Clutch places it in the 50–249 employee band. Client reviews describe staff augmentation in practice: one London ad-tech client hired Xenoss after failing to find engineers locally, and Xenoss sourced candidates from Ukraine and integrated them into the in-house team. Its background in high-throughput ad-tech systems shows in its data-engineering work.
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: Xenoss vs DataArt
| Capability | Xenoss | 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: Xenoss vs DataArt
| Framework / platform | Xenoss | DataArt |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | ✓ |
| Databricks | ✓ | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | ✓ |
Pricing comparison: Xenoss vs DataArt
| Criterion | Xenoss | 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: Xenoss vs DataArt
| Dimension | Xenoss | DataArt |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Ad tech, Media, Fintech | Fintech, Travel, Healthcare |
| Best use cases | Adding streaming-data engineers ahead of an ML launch, Placing ML engineers in a bidding or attribution product | 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 |
Xenoss vs DataArt: pros and cons
| Xenoss | |
|---|---|
| + | Strong on real-time data infrastructure that ML features depend on |
| + | Has placed engineers into UK teams that struggled to hire locally |
| + | Senior leadership comes from the industry it serves most |
| + | Covers both data engineering and model work |
| - | Ad-tech focus is narrower than general AI staffing |
| - | Mid-sized bench |
| - | Rates are not public |
| 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 Xenoss?
A typical fit: adding streaming-data engineers ahead of an ML launch.
Data engineers with ad-tech throughput experience. Minimum engagement is not publicly disclosed. Works best with clients in Ad tech, Media, Fintech, Retail and e-commerce.
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: Xenoss 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 | Xenoss |
| Your budget is at the lower end | Compare: Xenoss (Not disclosed) vs DataArt (Not disclosed) |
| You need specialist depth in a specific vertical | Xenoss |
| 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: Xenoss vs DataArt
| Use case | Xenoss fit | DataArt fit | Winner |
|---|---|---|---|
| Adding streaming-data engineers ahead of an ML launch | Strong | Strong | Both equally |
| Placing ML engineers in a bidding or attribution product | Strong | Limited | Xenoss |
| 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: Xenoss vs DataArt
Xenoss (4.3/5) is the stronger overall choice for most AI Staffing projects. Data engineers with ad-tech throughput experience.
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
Xenoss vs DataArt FAQ
Is Xenoss better than DataArt?
Xenoss (4.3/5) scores higher overall, but "better" depends on your use case. Xenoss's strongest advantage: strong on real-time data infrastructure that ML features depend on. DataArt's strongest advantage: long-running dedicated teams with low churn.
How do Xenoss and DataArt differ in pricing?
Xenoss uses time and materials; staff augmentation; 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: Xenoss 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 Xenoss and DataArt?
Xenoss's primary differentiator is: data engineers with ad-tech throughput experience. DataArt's primary differentiator is: dedicated development centers with nearly 30 years of history. They also differ in team size (50–249 vs 5,000–6,000), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Ad tech, Media vs Fintech, Travel).
Verify all details directly with each agency before making a decision.