ELEKS vs Xebia: full comparison for 2026
Last updated: July 2026
Quick verdict
ELEKS (4.1/5) edges ahead of Xebia (4.0/5) overall. ELEKS is the better choice for enterprises wanting a long-established European software engineering partner with an added data science practice rather than an AI-only startup vendor.. 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.
ELEKS vs Xebia: head-to-head summary
| Criterion | ELEKS | Xebia |
|---|---|---|
| Founded | 1991 | 2001 |
| HQ | Tallinn, Estonia (engineering hub: Lviv, Ukraine) | Amsterdam, Netherlands (US HQ: Atlanta, USA) |
| Team size | 1,001–5,000 | 5,001–10,000 |
| Rating | 4.1 / 5 | 4.0 / 5 |
| Best for | Enterprises wanting a long-established European software engineering partner with an added data science practice rather than an AI-only startup vendor. | Enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery. |
| Pricing model | Time & Material, Fixed project | Not published; enterprise project engagements |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Cloud ML platforms (AWS/Azure/GCP), Data engineering tooling | Python, Cloud ML platforms (AWS/Azure/GCP), MLOps tooling |
| Industries served | Financial services, Healthcare, Manufacturing, Insurance | Financial services, Retail, Manufacturing, Public sector |
ELEKS vs Xebia: overview
ELEKS
ELEKS is a long-running European software engineering company founded in 1991, with corporate presence in Tallinn, Estonia and its largest engineering hub in Lviv, Ukraine, alongside additional offices across Europe and North America. The company reports more than 2,000 employees and operates a dedicated data science and AI practice layered onto its broader enterprise software engineering services. Its history predates the modern AI/ML consulting wave by roughly three decades, giving it an unusually long operating track record compared to most peers in this list.
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: ELEKS vs Xebia
| Capability | ELEKS | 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: ELEKS vs Xebia
| Framework / platform | ELEKS | 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 | ✓ | ✓ |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: ELEKS vs Xebia
| Criterion | ELEKS | Xebia |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Time & Material, Fixed project, Dedicated team | Enterprise project engagement, Dedicated team, Training/enablement |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: ELEKS vs Xebia
| Dimension | ELEKS | Xebia |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | Financial services, Healthcare, Manufacturing | Financial services, Retail, Manufacturing |
| Best use cases | Running an enterprise-scale data science initiative alongside a broader software modernization program, Engaging a long-tenured, stable partner for a multi-year digital transformation that includes ML components | 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 |
ELEKS vs Xebia: pros and cons
| ELEKS | |
|---|---|
| + | Over three decades of continuous operation, unusually long for this category. |
| + | Large engineering bench (2,000+ employees) supports substantial delivery capacity. |
| + | Data science practice is embedded within a mature enterprise software engineering organization. |
| + | Multi-region European and North American office footprint. |
| - | AI/ML is one practice area within a much broader enterprise software portfolio, not the company's primary specialization. |
| - | Specific, named ML case studies with quantified outcomes are limited in available public sources. |
| - | Pricing minimums are not published. |
| - | Long operating history does not necessarily translate into deep modern ML/LLM specialization relative to newer, AI-first boutiques. |
| 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 ELEKS?
ELEKS is the right choice for enterprises wanting a long-established European software engineering partner with an added data science practice rather than an AI-only startup vendor..
One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades.. Minimum engagement starts at Not published. Works best with clients in Financial services, Healthcare, Manufacturing, Insurance.
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: ELEKS vs Xebia
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | ELEKS |
| You need a large dedicated team for an ongoing programme | ELEKS |
| Your budget is at the lower end | Compare: ELEKS (Not published) vs Xebia (Not published) |
| You need specialist depth in a specific vertical | ELEKS |
| 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: ELEKS vs Xebia
| Use case | ELEKS fit | Xebia fit | Winner |
|---|---|---|---|
| Running an enterprise-scale data science initiative alongside a broader software modernization program | Strong | Strong | Both equally |
| Engaging a long-tenured, stable partner for a multi-year digital transformation that includes ML components | Strong | Strong | Both equally |
| 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 | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Limited | Both equally |
Verdict: ELEKS vs Xebia
ELEKS (4.1/5) is the stronger overall choice for most ML Model Development projects. One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades.. It is best for enterprises wanting a long-established European software engineering partner with an added data science practice rather than an AI-only startup vendor..
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
ELEKS vs Xebia FAQ
Is ELEKS better than Xebia?
ELEKS (4.1/5) scores higher overall, but "better" depends on your use case. ELEKS is better for enterprises wanting a long-established European software engineering partner with an added data science practice rather than an AI-only startup vendor.. Xebia is better for enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery..
How do ELEKS and Xebia differ in pricing?
ELEKS uses time & material, fixed project pricing with a minimum engagement of Not published. 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: ELEKS 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 ELEKS and Xebia?
ELEKS's primary differentiator is: one of the longest operating histories (since 1991) among firms researched for this list, predating the ai consulting boom by decades.. 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 (1,001–5,000 vs 5,001–10,000), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Healthcare vs Financial services, Retail).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.