InData Labs vs Xebia: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.3/5) edges ahead of Xebia (4.0/5) overall. InData Labs is the better choice for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks.. 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.
InData Labs vs Xebia: head-to-head summary
| Criterion | InData Labs | Xebia |
|---|---|---|
| Founded | 2014 | 2001 |
| HQ | Nicosia, Cyprus (delivery center: Minsk, Belarus) | Amsterdam, Netherlands (US HQ: Atlanta, USA) |
| Team size | 51–200 | 5,001–10,000 |
| Rating | 4.3 / 5 | 4.0 / 5 |
| Best for | Companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks. | Enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery. |
| Pricing model | Project-based | Not published; enterprise project engagements |
| Min. engagement | $25,000 | Not published |
| Primary tech stack | Python, Computer vision frameworks, NLP toolkits | Python, Cloud ML platforms (AWS/Azure/GCP), MLOps tooling |
| Industries served | Transportation/logistics, Retail, Finance | Financial services, Retail, Manufacturing, Public sector |
InData Labs vs Xebia: overview
InData Labs
InData Labs is a data science consultancy founded in 2014 by Marat Karpeko, with a registered headquarters in Nicosia, Cyprus, and its primary research and development center in Minsk, Belarus. The company focuses on predictive analytics, natural language processing, and computer vision, delivering custom AI model development for clients ranging from logistics to retail. Published case studies include a freight-rate prediction model for a transportation company and a dog-face-identification model reporting 91.96 percent accuracy, giving it more quantified, checkable outcome data than many peers of similar size.
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: InData Labs vs Xebia
| Capability | InData 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: InData Labs vs Xebia
| Framework / platform | InData Labs | Xebia |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | 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 | N/A | ✓ |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: InData Labs vs Xebia
| Criterion | InData Labs | Xebia |
|---|---|---|
| Minimum engagement | $25,000 | Not published |
| Engagement models | Fixed project, Time & Material | Enterprise project engagement, Dedicated team, Training/enablement |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs Xebia
| Dimension | InData Labs | Xebia |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | Transportation/logistics, Retail, Finance | Financial services, Retail, Manufacturing |
| Best use cases | Building a predictive pricing or demand-forecasting model for logistics or transportation, Developing a computer-vision classification model with a documented accuracy target | 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 | Fixed project | Enterprise project engagement |
InData Labs vs Xebia: pros and cons
| InData Labs | |
|---|---|
| + | Case studies include specific, quantified model accuracy figures rather than vague outcome claims. |
| + | Featured among Clutch's broader provider directory with a positive review sentiment on delivery timeliness. |
| + | Focused specialization in predictive analytics and computer vision avoids service-line dilution. |
| + | Recognized in a 2016 "Top 100 Big Data" listing, indicating an established track record. |
| - | Team size figures are inconsistent across sources (roughly 50–80 depending on source), so exact headcount is uncertain. |
| - | Registered HQ (Cyprus) differs from the primary delivery center (Belarus), which some buyers may want clarified given regional considerations. |
| - | Public tech-stack disclosure is limited beyond high-level specialization areas. |
| - | Fewer large, brand-name enterprise clients named publicly compared to bigger peers. |
| 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 InData Labs?
InData Labs is the right choice for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks..
Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.. Minimum engagement starts at $25,000. Works best with clients in Transportation/logistics, Retail, Finance.
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: InData Labs vs Xebia
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | InData Labs |
| You need a large dedicated team for an ongoing programme | Xebia |
| Your budget is at the lower end | Compare: InData Labs ($25,000) vs Xebia (Not published) |
| You need specialist depth in a specific vertical | Xebia |
| 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: InData Labs vs Xebia
| Use case | InData Labs fit | Xebia fit | Winner |
|---|---|---|---|
| Building a predictive pricing or demand-forecasting model for logistics or transportation | Strong | Limited | InData Labs |
| Developing a computer-vision classification model with a documented accuracy target | Strong | Limited | InData 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: InData Labs vs Xebia
InData Labs (4.3/5) is the stronger overall choice for most ML Model Development projects. Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.. It is best for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks..
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
InData Labs vs Xebia FAQ
Is InData Labs better than Xebia?
InData Labs (4.3/5) scores higher overall, but "better" depends on your use case. InData Labs is better for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks.. Xebia is better for enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery..
How do InData Labs and Xebia differ in pricing?
InData Labs uses project-based pricing with a minimum engagement of $25,000. 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: InData 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 InData Labs and Xebia?
InData Labs's primary differentiator is: publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.. 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 ($25,000 vs Not published), and primary industries served (Transportation/logistics, Retail vs Financial services, Retail).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.