N-iX vs Keyrus: full comparison for 2026
Last updated: July 2026
Quick verdict
N-iX (4.4/5) edges ahead of Keyrus (3.8/5) overall. N-iX is the better choice for enterprise teams needing multidisciplinary ML and cloud engineering with strong European delivery. Keyrus is the stronger option for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience. The right choice depends on your project size, budget, and required tech stack.
N-iX vs Keyrus: head-to-head summary
| Criterion | N-iX | Keyrus |
|---|---|---|
| Founded | 2002 | 2000 |
| HQ | Wrocław, Poland | Paris, France |
| Team size | 2,400+ | 3,500+ |
| Rating | 4.4 / 5 | 3.8 / 5 |
| Best for | Enterprise teams needing multidisciplinary ML and cloud engineering with strong European delivery | International enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience |
| Pricing model | T&M, dedicated team | T&M, retainer |
| Min. engagement | $25K+ | $50K+ |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, Tableau, Power BI |
| Industries served | financial, healthcare, logistics, manufacturing, retail, telecommunications | financial, retail, healthcare, manufacturing, media |
N-iX vs Keyrus: overview
N-iX
N-iX was founded in 2002 and is headquartered in Wrocław, Poland, with 2,400+ engineers across Europe, the Americas, and APAC. The company helps enterprise clients — including several Fortune 500 organisations — across 17 industries with machine learning consulting, AI integration, cloud solutions, analytics, and intelligent automation. (Team size and client segment per N-iX official website and LinkedIn.)
Keyrus
Keyrus is an international consulting group founded in 2000, headquartered in Paris, France, and operating in over 20 countries with 3,500+ professionals. The company positions itself at the intersection of business, data, and AI — helping clients move from experimental AI to industrial-grade ML systems in production. Services span data strategy, BI, analytics, AI testing, and ML deployment. (Employee count and global footprint per Keyrus official website.)
Services and capabilities: N-iX vs Keyrus
| Capability | N-iX | Keyrus |
|---|---|---|
| Custom ML build | ✓ | ✗ |
| ML consulting | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP / LLM | ✓ | ✗ |
| Predictive analytics | ✗ | ✓ |
| MLOps | ✓ | ✓ |
| Data engineering | ✓ | ✓ |
| Generative AI | ✗ | ✗ |
| Staff augmentation | ✗ | ✗ |
| Fixed-price projects | ✗ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: N-iX vs Keyrus
| Framework / platform | N-iX | Keyrus |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
Pricing comparison: N-iX vs Keyrus
| Criterion | N-iX | Keyrus |
|---|---|---|
| Minimum engagement | $25K+ | $50K+ |
| Engagement models | T&M, Dedicated team, Retainer | T&M, Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: N-iX vs Keyrus
| Dimension | N-iX | Keyrus |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | financial, healthcare, logistics | financial, retail, healthcare |
| Best use cases | Enterprise ML platform build on AWS or Azure, Intelligent automation programme for manufacturing | Industrial AI deployment at enterprise scale, Analytics and ML platform for financial services |
| Typical project type | T&M | T&M |
N-iX vs Keyrus: pros and cons
| N-iX | |
|---|---|
| + | Large engineering capacity: 2,400+ engineers across multiple disciplines |
| + | Fortune 500 track record across 17 industry verticals |
| + | Covers ML, cloud, data engineering, and analytics in one organisation |
| + | European delivery base with North American client focus |
| + | Strong MLOps and intelligent automation capability |
| - | Large firm structure can mean slower ramp and more overhead than boutiques |
| - | ML is one capability among many — not a pure ML specialist |
| Keyrus | |
|---|---|
| + | Global footprint: 20+ countries, 3,500+ professionals |
| + | Industrial-AI focus — moves clients from PoC to production scale |
| + | Strong analytics and BI alongside ML for full data stack coverage |
| + | AI testing and validation capability |
| - | Large-firm pricing not suited to startup or SMB budgets |
| - | AI is one offering within broader data consulting — not ML-first |
Who should choose N-iX?
N-iX is the right choice for enterprise teams needing multidisciplinary ML and cloud engineering with strong European delivery.
2,400+ engineers covering ML, cloud, and data under one firm — strong for large multi-track programmes. Minimum engagement starts at $25K+. Works best with clients in financial, healthcare, logistics, manufacturing, retail, telecommunications.
Who should choose Keyrus?
Keyrus is the right choice for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience.
From experimental AI to industrial AI — consulting group specialising in productionising ML for large organisations. Minimum engagement starts at $50K+. Works best with clients in financial, retail, healthcare, manufacturing, media.
Decision matrix: N-iX vs Keyrus
| 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 | N-iX |
| Your budget is at the lower end | N-iX |
| 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 | N-iX |
Use case fit: N-iX vs Keyrus
| Use case | N-iX fit | Keyrus fit | Winner |
|---|---|---|---|
| Enterprise ML platform build on AWS or Azure | Strong | Strong | Both equally |
| Intelligent automation programme for manufacturing | Strong | Limited | N-iX |
| Industrial AI deployment at enterprise scale | Limited | Strong | Keyrus |
| Analytics and ML platform for financial services | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: N-iX vs Keyrus
N-iX (4.4/5) is the stronger overall choice for most Machine Learning projects. 2,400+ engineers covering ML, cloud, and data under one firm — strong for large multi-track programmes. It is best for enterprise teams needing multidisciplinary ML and cloud engineering with strong European delivery.
Keyrus (3.8/5) is the better choice when international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience. If your situation matches those criteria, Keyrus is a competitive option.
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N-iX vs Keyrus FAQ
Is N-iX better than Keyrus?
N-iX (4.4/5) scores higher overall, but "better" depends on your use case. N-iX is better for enterprise teams needing multidisciplinary ML and cloud engineering with strong European delivery. Keyrus is better for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience.
How do N-iX and Keyrus differ in pricing?
N-iX uses t&m, dedicated team pricing with a minimum engagement of $25K+. Keyrus uses t&m, retainer pricing with a minimum engagement of $50K+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: N-iX or Keyrus?
Keyrus is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.
What are the main differences between N-iX and Keyrus?
N-iX's primary differentiator is: 2,400+ engineers covering ml, cloud, and data under one firm — strong for large multi-track programmes. Keyrus's primary differentiator is: from experimental ai to industrial ai — consulting group specialising in productionising ml for large organisations. They also differ in team size (2,400+ vs 3,500+), minimum engagement ($25K+ vs $50K+), and primary industries served (financial, healthcare vs financial, retail).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.