Artefact vs N-iX: full comparison for 2026
Quick verdict
Artefact (4.5/5) edges ahead of N-iX (4.4/5) overall. Artefact is the better choice for large enterprises, industrial-scale ML and data strategy. N-iX is the stronger option for enterprise teams, ML plus cloud engineering, European delivery. The right choice depends on your project size, budget, and required tech stack.
Artefact vs N-iX: head-to-head summary
| Criterion | Artefact | N-iX |
|---|---|---|
| Founded | 2014 | 2002 |
| HQ | Paris, France | Wrocław, Poland |
| Team size | 1,500 | 2,400+ |
| Rating | 4.5 / 5 | 4.4 / 5 |
| Primary differentiator | Enterprise ML at 1,500-consultant scale across 26 countries — strategy, deployment, and AI factory in one firm | 2,400+ engineers covering ML, cloud, and data under one firm — strong for large multi-track programmes |
| Pricing model | T&M, retainer | T&M, dedicated team |
| Min. engagement | $50K+ | $25K+ |
| Primary tech stack | Python, Vertex AI, Azure ML | Python, TensorFlow, PyTorch |
| Industries served | retail, healthcare, fintech, media, telecommunications, FMCG | financial, healthcare, logistics, manufacturing, retail, telecommunications |
Artefact vs N-iX: overview
Artefact
Artefact is a global consulting company founded in 2014, headquartered in Paris, with 1,500 employees across 33 offices in 26 countries. The firm partners with 1,000+ clients including Samsung, L'Oréal, Orange, and Sanofi, providing services spanning data strategy, ML model development, AI factory deployments, and cloud AI platforms. Artefact covers end-to-end ML lifecycles for large enterprises seeking industrial-scale AI adoption. (Employee count and client names per Artefact official website.)
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.)
Services and capabilities: Artefact vs N-iX
| Capability | Artefact | N-iX |
|---|---|---|
| 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: Artefact vs N-iX
| Framework / platform | Artefact | N-iX |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | N/A |
Pricing comparison: Artefact vs N-iX
| Criterion | Artefact | N-iX |
|---|---|---|
| Minimum engagement | $50K+ | $25K+ |
| Engagement models | T&M, Retainer, Dedicated team | T&M, Dedicated team, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Artefact vs N-iX
| Dimension | Artefact | N-iX |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | retail, healthcare, fintech | financial, healthcare, logistics |
| Best use cases | Enterprise AI strategy and ML roadmap, AI factory deployment for CPG brand | Enterprise ML platform build on AWS or Azure, Intelligent automation programme for manufacturing |
| Typical project type | T&M | T&M |
Artefact vs N-iX: pros and cons
| Artefact | |
|---|---|
| + | Global delivery footprint: 33 offices in 26 countries |
| + | Named clients include Samsung, L'Oréal, Orange, and Sanofi |
| + | End-to-end: from data strategy to production AI factory |
| + | Strong on cloud AI platforms: Vertex AI, Azure ML, AWS SageMaker |
| + | Industry-specific ML expertise across retail, healthcare, and FMCG |
| - | Minimum engagement well above startup budgets — best suited to large programmes |
| - | Less suited to short fixed-price ML projects or prototypes |
| 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 |
Who should choose Artefact?
A typical fit: enterprise AI strategy and ML roadmap.
Enterprise ML at 1,500-consultant scale across 26 countries — strategy, deployment, and AI factory in one firm. Minimum engagement starts at $50K+. Works best with clients in retail, healthcare, fintech, media, telecommunications, FMCG.
Who should choose N-iX?
A typical fit: enterprise ML platform build on AWS or Azure.
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.
Decision matrix: Artefact vs N-iX
| 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 | Artefact |
| Your budget is at the lower end | N-iX |
| You need specialist depth in a specific vertical | Artefact |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Artefact |
Use case fit: Artefact vs N-iX
| Use case | Artefact fit | N-iX fit | Winner |
|---|---|---|---|
| Enterprise AI strategy and ML roadmap | Strong | Strong | Both equally |
| AI factory deployment for CPG brand | Strong | Strong | Both equally |
| Enterprise ML platform build on AWS or Azure | Strong | Strong | Both equally |
| Intelligent automation programme for manufacturing | Limited | Strong | N-iX |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Artefact vs N-iX
Artefact (4.5/5) is the stronger overall choice for most Machine Learning projects. Enterprise ML at 1,500-consultant scale across 26 countries — strategy, deployment, and AI factory in one firm.
N-iX (4.4/5) is worth a look if you need intelligent automation programme for manufacturing. If your situation matches that, N-iX is a competitive option.
Related comparisons
Artefact vs N-iX FAQ
Is Artefact better than N-iX?
Artefact (4.5/5) scores higher overall, but "better" depends on your use case. Artefact's strongest advantage: global delivery footprint: 33 offices in 26 countries. N-iX's strongest advantage: large engineering capacity: 2,400+ engineers across multiple disciplines.
How do Artefact and N-iX differ in pricing?
Artefact uses t&m, retainer pricing with a minimum engagement of $50K+. N-iX uses t&m, dedicated team pricing with a minimum engagement of $25K+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Artefact or N-iX?
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 agency before shortlisting.
What are the main differences between Artefact and N-iX?
Artefact's primary differentiator is: enterprise ML at 1,500-consultant scale across 26 countries — strategy, deployment, and AI factory in one firm. N-iX's primary differentiator is: 2,400+ engineers covering ML, cloud, and data under one firm — strong for large multi-track programmes. They also differ in team size (1,500 vs 2,400+), minimum engagement ($50K+ vs $25K+), and primary industries served (retail, healthcare vs financial, healthcare).