Xebia vs Infosys: full comparison for 2026
Last updated: July 2026
Quick verdict
Xebia (4.0/5) edges ahead of Infosys (3.9/5) overall. Xebia is the better choice for enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery.. Infosys is the stronger option for very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch.. The right choice depends on your project size, budget, and required tech stack.
Xebia vs Infosys: head-to-head summary
| Criterion | Xebia | Infosys |
|---|---|---|
| Founded | 2001 | 1981 |
| HQ | Amsterdam, Netherlands (US HQ: Atlanta, USA) | Bengaluru, India |
| Team size | 5,001–10,000 | 10,000+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Best for | Enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery. | Very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch. |
| Pricing model | Not published; enterprise project engagements | Not published; enterprise project engagements |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Cloud ML platforms (AWS/Azure/GCP), MLOps tooling | Infosys Topaz (proprietary), Topaz Fabric (proprietary), Cloud ML platforms (AWS/Azure/GCP) |
| Industries served | Financial services, Retail, Manufacturing, Public sector | Banking and financial services, Manufacturing, Retail, Telecommunications |
Xebia vs Infosys: overview
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.
Infosys
Infosys was founded in 1981 in Pune by seven engineers including N.R. Narayana Murthy and Nandan Nilekani, and is headquartered in Bengaluru with more than 330,000 employees worldwide, trading publicly on the NYSE under INFY. Its AI practice, branded Infosys Topaz, reports more than 12,000 AI assets, over 150 pre-trained AI models, and more than ten AI platforms supporting machine learning, generative AI, conversational AI, and intelligent automation work across industry verticals. The company recently launched Topaz Fabric, a composable stack of AI agents, services, and models intended to accelerate enterprise AI investment value.
Services and capabilities: Xebia vs Infosys
| Capability | Xebia | Infosys |
|---|---|---|
| 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: Xebia vs Infosys
| Framework / platform | Xebia | Infosys |
|---|---|---|
| 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: Xebia vs Infosys
| Criterion | Xebia | Infosys |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Enterprise project engagement, Dedicated team, Training/enablement | Enterprise project engagement, Managed AI services, Composable agent platform (Topaz Fabric) |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Xebia vs Infosys
| Dimension | Xebia | Infosys |
|---|---|---|
| Best company size | Enterprise | Enterprise |
| Best industries | Financial services, Retail, Manufacturing | Banking and financial services, Manufacturing, Retail |
| Best use cases | Turning an existing AI strategy or pilot into a production-ready, monitored system, Combining technical training/enablement with hands-on AI model development | Very large enterprises wanting to accelerate AI delivery using a large library of pre-built models and assets, Deploying composable AI agents via the Topaz Fabric platform across multiple business functions |
| Typical project type | Enterprise project engagement | Enterprise project engagement |
Xebia vs Infosys: pros and cons
| 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. |
| Infosys | |
|---|---|
| + | Largest disclosed pre-built AI asset library in this comparison (12,000+ assets, 150+ pre-trained models) can materially speed up delivery. |
| + | New Topaz Fabric composable AI agent platform reflects continued investment in productized AI tooling. |
| + | Publicly traded (NYSE: INFY) with more than four decades of operating history and strong financial transparency. |
| + | Very large global workforce (330,000+) supports substantial multi-region program capacity. |
| - | Specific founding date, headquarters, and team size for the Topaz practice itself are not separately disclosed from the parent company in available public sources. |
| - | No clearly located aggregate Clutch/G2 star rating specific to its AI practice. |
| - | Pricing model and minimum engagement are not published, and typical minimums are substantial for enterprise engagements. |
| - | Heavy reliance on pre-built assets may be less suited to clients needing a fully custom, from-scratch model architecture. |
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.
Who should choose Infosys?
Infosys is the right choice for very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch..
Largest disclosed library of reusable, pre-trained AI assets in this comparison (12,000+ assets, 150+ pre-trained models), positioned to accelerate delivery versus fully bespoke builds.. Minimum engagement starts at Not published. Works best with clients in Banking and financial services, Manufacturing, Retail, Telecommunications.
Decision matrix: Xebia vs Infosys
| 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 | Xebia |
| Your budget is at the lower end | Compare: Xebia (Not published) vs Infosys (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: Xebia vs Infosys
| Use case | Xebia fit | Infosys fit | Winner |
|---|---|---|---|
| Turning an existing AI strategy or pilot into a production-ready, monitored system | Strong | Limited | Xebia |
| Combining technical training/enablement with hands-on AI model development | Strong | Limited | Xebia |
| Very large enterprises wanting to accelerate AI delivery using a large library of pre-built models and assets | Limited | Strong | Infosys |
| Deploying composable AI agents via the Topaz Fabric platform across multiple business functions | Limited | Strong | Infosys |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Limited | Both equally |
Verdict: Xebia vs Infosys
Xebia (4.0/5) is the stronger overall choice for most ML Model Development projects. Quarter-century software craftsmanship and technical training heritage now applied specifically to production AI/ML delivery rather than AI strategy alone.. It is best for enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery..
Infosys (3.9/5) is the better choice when very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch.. If your situation matches those criteria, Infosys is a competitive option.
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Xebia vs Infosys FAQ
Is Xebia better than Infosys?
Xebia (4.0/5) scores higher overall, but "better" depends on your use case. Xebia is better for enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has repositioned around production-ready AI delivery.. Infosys is better for very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch..
How do Xebia and Infosys differ in pricing?
Xebia uses not published; enterprise project engagements pricing with a minimum engagement of Not published. Infosys 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: Xebia or Infosys?
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 Xebia and Infosys?
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.. Infosys's primary differentiator is: largest disclosed library of reusable, pre-trained ai assets in this comparison (12,000+ assets, 150+ pre-trained models), positioned to accelerate delivery versus fully bespoke builds.. They also differ in team size (5,001–10,000 vs 10,000+), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Retail vs Banking and financial services, Manufacturing).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.