DataRoot Labs vs Xebia: full comparison for 2026
Last updated: July 2026
Quick verdict
DataRoot Labs (4.6/5) edges ahead of Xebia (4.0/5) overall. DataRoot Labs is the better choice for startups and mid-market companies wanting a senior, AI-only team for LLM fine-tuning, computer vision, or reinforcement-learning projects.. Xebia is the stronger option for enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery.. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs Xebia: head-to-head summary
| Criterion | DataRoot Labs | Xebia |
|---|---|---|
| Founded | 2016 | 2001 |
| HQ | Kyiv, Ukraine | Amsterdam, Netherlands (US HQ: Atlanta, USA) |
| Team size | 51–200 | 5,001–10,000 |
| Rating | 4.6 / 5 | 4.0 / 5 |
| Best for | Startups and mid-market companies wanting a senior, AI-only team for LLM fine-tuning, computer vision, or reinforcement-learning projects. | Enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery. |
| Pricing model | Time & Material, project-based | Not published; enterprise project engagements |
| Min. engagement | $10,000+ | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Cloud ML platforms (AWS/Azure/GCP), MLOps tooling |
| Industries served | E-commerce, Healthcare, Enterprise software, Robotics | Financial services, Retail, Manufacturing, Public sector |
DataRoot Labs vs Xebia: overview
DataRoot Labs
DataRoot Labs is a Ukraine-founded machine learning consultancy established in 2016 that has remained AI/ML-only since inception, in contrast to firms that added AI as a service line later. The company offers AI consulting, custom model development and training, solution architecture, and deployment/monitoring, with stated specializations in large language model fine-tuning, computer vision, reinforcement learning, and vector databases. Publicly named clients include OLX, IBM, Databand, and Moxie (Embodied). The company also runs DataRoot University, a training program it states has produced over 6,000 machine learning graduates (per company website; independently unverifiable), which functions as a talent pipeline and community credibility signal.
Xebia
Xebia was founded in 2001 by Rob Dielemans and Daan Teunissen in the Netherlands and has grown into a global consultancy spanning data and AI, cloud, automation, and software engineering. The Xebia Group reports between 5,000 and 10,000 employees, with corporate headquarters activity in both the Netherlands and Atlanta, Georgia. Its Data & AI Hub practice focuses on turning AI strategy into production-ready solutions, reflecting a repositioning from Xebia's original software craftsmanship and training-company roots toward an AI-first identity.
Services and capabilities: DataRoot Labs vs Xebia
| Capability | DataRoot Labs | Xebia |
|---|---|---|
| Custom model training | ✓ | ✓ |
| Fine-tuning & adaptation | ✓ | ✗ |
| MLOps pipeline | ✗ | ✓ |
| Model deployment & serving | ✓ | ✗ |
| Data engineering for ML | ✗ | ✓ |
| ML infrastructure management | ✗ | ✗ |
| Computer vision | ✓ | ✗ |
| NLP & LLM development | ✓ | ✗ |
| Forecasting & time-series modeling | ✗ | ✗ |
| ML strategy consulting | ✗ | ✓ |
Tech stack comparison: DataRoot Labs vs Xebia
| Framework / platform | DataRoot Labs | Xebia |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| MLflow | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Amazon Bedrock | N/A | N/A |
| Google Cloud | N/A | N/A |
| Microsoft Azure | N/A | N/A |
| Kubernetes | ✓ | ✓ |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: DataRoot Labs vs Xebia
| Criterion | DataRoot Labs | Xebia |
|---|---|---|
| Minimum engagement | $10,000+ | Not published |
| Engagement models | Time & Material, Fixed project, Dedicated team | Enterprise project engagement, Dedicated team, Training/enablement |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: DataRoot Labs vs Xebia
| Dimension | DataRoot Labs | Xebia |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | E-commerce, Healthcare, Enterprise software | Financial services, Retail, Manufacturing |
| Best use cases | Fine-tuning an open-source LLM for a domain-specific internal tool, Building a computer vision model for retail or logistics quality inspection | Turning an existing AI strategy or pilot into a production-ready, monitored system, Combining technical training/enablement with hands-on AI model development |
| Typical project type | Time & Material | Enterprise project engagement |
DataRoot Labs vs Xebia: pros and cons
| DataRoot Labs | |
|---|---|
| + | Clutch rating of 4.9/5 across 23 verified reviews, among the highest in this comparison set. |
| + | Named, checkable clients (OLX, IBM, Databand, Moxie) rather than anonymized case studies only. |
| + | Full IP transfer to clients is cited as standard practice in reviews. |
| + | AI-only focus since 2016 avoids the generalist dilution seen in broader software houses. |
| - | Small team (51–200) constrains capacity for large, multi-team enterprise rollouts. |
| - | Delivery is concentrated in Ukraine, which some risk-averse enterprise buyers may flag for business-continuity planning. |
| - | Public tech-stack disclosure is limited beyond high-level specialization claims. |
| - | Minimum engagement of $10K+ is accessible, but larger programs will need custom scoping not published on the site. |
| Xebia | |
|---|---|
| + | 25-year software engineering and technical training pedigree underpins its AI delivery credibility. |
| + | Large scale (5,000–10,000 employees) supports substantial enterprise program capacity. |
| + | Explicit focus on production-ready AI rather than strategy-only advisory work. |
| + | Dual US/EU headquarters presence supports transatlantic enterprise clients. |
| - | AI-first repositioning is relatively recent, so its dedicated AI/ML track record is shorter than its overall company history suggests. |
| - | No clearly located aggregate Clutch/G2 star rating in available public sources. |
| - | Pricing model and minimum engagement are not published. |
| - | Large, multi-practice organization means AI/ML delivery quality may vary by regional team. |
Who should choose DataRoot Labs?
DataRoot Labs is the right choice for startups and mid-market companies wanting a senior, AI-only team for LLM fine-tuning, computer vision, or reinforcement-learning projects..
Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University).. Minimum engagement starts at $10,000+. Works best with clients in E-commerce, Healthcare, Enterprise software, Robotics.
Who should choose Xebia?
Xebia is the right choice for enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery..
Quarter-century software craftsmanship and technical training heritage now applied specifically to production AI/ML delivery rather than AI strategy alone.. Minimum engagement starts at Not published. Works best with clients in Financial services, Retail, Manufacturing, Public sector.
Decision matrix: DataRoot Labs vs Xebia
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DataRoot Labs |
| You need a large dedicated team for an ongoing programme | DataRoot Labs |
| Your budget is at the lower end | Compare: DataRoot Labs ($10,000+) vs Xebia (Not published) |
| You need specialist depth in a specific vertical | DataRoot Labs |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Xebia |
Use case fit: DataRoot Labs vs Xebia
| Use case | DataRoot Labs fit | Xebia fit | Winner |
|---|---|---|---|
| Fine-tuning an open-source LLM for a domain-specific internal tool | Strong | Limited | DataRoot Labs |
| Building a computer vision model for retail or logistics quality inspection | Strong | Limited | DataRoot Labs |
| Turning an existing AI strategy or pilot into a production-ready, monitored system | Limited | Strong | Xebia |
| Combining technical training/enablement with hands-on AI model development | Limited | Strong | Xebia |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs Xebia
DataRoot Labs (4.6/5) is the stronger overall choice for most ML Model Development projects. Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University).. It is best for startups and mid-market companies wanting a senior, AI-only team for LLM fine-tuning, computer vision, or reinforcement-learning projects..
Xebia (4.0/5) is the better choice when enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery.. If your situation matches those criteria, Xebia is a competitive option.
Related comparisons
DataRoot Labs vs Xebia FAQ
Is DataRoot Labs better than Xebia?
DataRoot Labs (4.6/5) scores higher overall, but "better" depends on your use case. DataRoot Labs is better for startups and mid-market companies wanting a senior, AI-only team for LLM fine-tuning, computer vision, or reinforcement-learning projects.. Xebia is better for enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery..
How do DataRoot Labs and Xebia differ in pricing?
DataRoot Labs uses time & material, project-based pricing with a minimum engagement of $10,000+. Xebia uses not published; enterprise project engagements pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: DataRoot Labs or Xebia?
Xebia 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 DataRoot Labs and Xebia?
DataRoot Labs's primary differentiator is: has never diversified beyond ai/ml services, and backs its delivery bench with an in-house ml training program (dataroot university).. Xebia's primary differentiator is: quarter-century software craftsmanship and technical training heritage now applied specifically to production ai/ml delivery rather than ai strategy alone.. They also differ in team size (51–200 vs 5,001–10,000), minimum engagement ($10,000+ vs Not published), and primary industries served (E-commerce, Healthcare vs Financial services, Retail).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.