N-iX vs Infosys: full comparison for 2026
Last updated: July 2026
Quick verdict
N-iX (4.4/5) edges ahead of Infosys (3.9/5) overall. N-iX is the better choice for enterprise buyers wanting a large, heavily certified engineering partner for combined data platform and ML 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.
N-iX vs Infosys: head-to-head summary
| Criterion | N-iX | Infosys |
|---|---|---|
| Founded | 2002 | 1981 |
| HQ | Lviv, Ukraine (registered HQ: Valletta, Malta) | Bengaluru, India |
| Team size | 1,001–5,000 | 10,000+ |
| Rating | 4.4 / 5 | 3.9 / 5 |
| Best for | Enterprise buyers wanting a large, heavily certified engineering partner for combined data platform and ML 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 | Time & Material, Fixed project | Not published; enterprise project engagements |
| Min. engagement | $100,000+ | Not published |
| Primary tech stack | AWS, Microsoft Azure, Google Cloud | Infosys Topaz (proprietary), Topaz Fabric (proprietary), Cloud ML platforms (AWS/Azure/GCP) |
| Industries served | Automotive, Telecom, Manufacturing, Transportation | Banking and financial services, Manufacturing, Retail, Telecommunications |
N-iX vs Infosys: overview
N-iX
N-iX began as Novellix in 2002, building product applications for Novell's Linux platform out of Lviv, Ukraine, and has since grown into a broader software engineering company with a corporate registration in Malta and delivery hubs across Ukraine, Poland, Sweden, and beyond. The company reports more than 2,400 engineers company-wide and states it holds over 350 active cloud certifications across Microsoft, AWS, Google Cloud, Palantir, SAP, and Snowflake. Its dedicated data and AI practice covers machine learning, MLOps, generative AI consulting, and data warehouse/lake architecture, with publicly named enterprise clients including Bosch, Siemens, AutoScout24, and Lebara.
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: N-iX vs Infosys
| Capability | N-iX | 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: N-iX vs Infosys
| Framework / platform | N-iX | 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 |
| Microsoft Azure | ✓ | N/A |
| Kubernetes | ✓ | N/A |
| Snowflake | ✓ | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: N-iX vs Infosys
| Criterion | N-iX | Infosys |
|---|---|---|
| Minimum engagement | $100,000+ | Not published |
| Engagement models | Time & Material, Fixed project, Dedicated team | Enterprise project engagement, Managed AI services, Composable agent platform (Topaz Fabric) |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Enterprise | Mid-market |
Target audience comparison: N-iX vs Infosys
| Dimension | N-iX | Infosys |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | Automotive, Telecom, Manufacturing | Banking and financial services, Manufacturing, Retail |
| Best use cases | Building an enterprise-scale data lake or warehouse to feed downstream ML models, Running a large, multi-workstream MLOps implementation across several business units | 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 | Time & Material | Enterprise project engagement |
N-iX vs Infosys: pros and cons
| N-iX | |
|---|---|
| + | Clutch rating of 4.8/5 across 35 verified reviews. |
| + | Named, verifiable enterprise clients including Bosch, Siemens, and AutoScout24. |
| + | Broadest multi-cloud certification depth (350+) among the companies researched for this list. |
| + | Maintained delivery continuity through significant regional disruption, per company and press reporting. |
| - | High minimum engagement ($100K+) excludes smaller buyers and early-stage startups. |
| - | Legal HQ (Malta) differs from primary engineering hub (Ukraine), which buyers should clarify during contracting. |
| - | As a multi-service engineering firm, ML/AI competes with several other practice areas for account attention. |
| - | Company-wide headcount (2,400+) makes it harder to gauge the actual size of the ML-specific delivery 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 N-iX?
N-iX is the right choice for enterprise buyers wanting a large, heavily certified engineering partner for combined data platform and ML delivery..
Broadest cloud certification footprint in this comparison (350+ across five major platforms), backed by a 200+ person dedicated data practice.. Minimum engagement starts at $100,000+. Works best with clients in Automotive, Telecom, Manufacturing, Transportation.
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: N-iX vs Infosys
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | N-iX |
| You need a large dedicated team for an ongoing programme | N-iX |
| Your budget is at the lower end | Compare: N-iX ($100,000+) vs Infosys (Not published) |
| You need specialist depth in a specific vertical | N-iX |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Infosys |
Use case fit: N-iX vs Infosys
| Use case | N-iX fit | Infosys fit | Winner |
|---|---|---|---|
| Building an enterprise-scale data lake or warehouse to feed downstream ML models | Strong | Limited | N-iX |
| Running a large, multi-workstream MLOps implementation across several business units | Strong | Limited | N-iX |
| Very large enterprises wanting to accelerate AI delivery using a large library of pre-built models and assets | Strong | Strong | Both equally |
| 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 | Strong | Limited | N-iX |
Verdict: N-iX vs Infosys
N-iX (4.4/5) is the stronger overall choice for most ML Model Development projects. Broadest cloud certification footprint in this comparison (350+ across five major platforms), backed by a 200+ person dedicated data practice.. It is best for enterprise buyers wanting a large, heavily certified engineering partner for combined data platform and ML 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.
Related comparisons
N-iX vs Infosys FAQ
Is N-iX better than Infosys?
N-iX (4.4/5) scores higher overall, but "better" depends on your use case. N-iX is better for enterprise buyers wanting a large, heavily certified engineering partner for combined data platform and ML 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 N-iX and Infosys differ in pricing?
N-iX uses time & material, fixed project pricing with a minimum engagement of $100,000+. 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: N-iX or Infosys?
N-iX 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 N-iX and Infosys?
N-iX's primary differentiator is: broadest cloud certification footprint in this comparison (350+ across five major platforms), backed by a 200+ person dedicated data practice.. 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 (1,001–5,000 vs 10,000+), minimum engagement ($100,000+ vs Not published), and primary industries served (Automotive, Telecom vs Banking and financial services, Manufacturing).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.