Artefact vs N-iX: full comparison for 2026
Last updated: July 2026
Quick verdict
Artefact (4.5/5) edges ahead of N-iX (4.4/5) overall. Artefact is the better choice for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. N-iX is the stronger option for enterprise teams needing multidisciplinary ML and cloud engineering with strong 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 |
| Best for | Large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy | Enterprise teams needing multidisciplinary ML and cloud engineering with strong European delivery |
| 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?
Artefact is the right choice for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy.
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?
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.
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. It is best for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy.
N-iX (4.4/5) is the better choice when enterprise teams needing multidisciplinary ML and cloud engineering with strong European delivery. If your situation matches those criteria, 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 is better for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. N-iX is better for enterprise teams needing multidisciplinary ML and cloud engineering with strong European delivery.
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).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.