Artefact vs Keyrus: full comparison for 2026
Last updated: July 2026
Quick verdict
Artefact (4.5/5) edges ahead of Keyrus (3.8/5) overall. Artefact is the better choice for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. 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.
Artefact vs Keyrus: head-to-head summary
| Criterion | Artefact | Keyrus |
|---|---|---|
| Founded | 2014 | 2000 |
| HQ | Paris, France | Paris, France |
| Team size | 1,500 | 3,500+ |
| Rating | 4.5 / 5 | 3.8 / 5 |
| Best for | Large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy | International enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience |
| Pricing model | T&M, retainer | T&M, retainer |
| Min. engagement | $50K+ | $50K+ |
| Primary tech stack | Python, Vertex AI, Azure ML | Python, Tableau, Power BI |
| Industries served | retail, healthcare, fintech, media, telecommunications, FMCG | financial, retail, healthcare, manufacturing, media |
Artefact vs Keyrus: 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.)
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: Artefact vs Keyrus
| Capability | Artefact | 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: Artefact vs Keyrus
| Framework / platform | Artefact | Keyrus |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | N/A |
Pricing comparison: Artefact vs Keyrus
| Criterion | Artefact | Keyrus |
|---|---|---|
| Minimum engagement | $50K+ | $50K+ |
| Engagement models | T&M, Retainer, Dedicated team | T&M, Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Artefact vs Keyrus
| Dimension | Artefact | Keyrus |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | retail, healthcare, fintech | financial, retail, healthcare |
| Best use cases | Enterprise AI strategy and ML roadmap, AI factory deployment for CPG brand | Industrial AI deployment at enterprise scale, Analytics and ML platform for financial services |
| Typical project type | T&M | T&M |
Artefact vs Keyrus: 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 |
| 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 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 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: Artefact 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 | Artefact |
| Your budget is at the lower end | Artefact |
| 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 Keyrus
| Use case | Artefact fit | Keyrus fit | Winner |
|---|---|---|---|
| Enterprise AI strategy and ML roadmap | Strong | Strong | Both equally |
| AI factory deployment for CPG brand | Strong | Strong | Both equally |
| Industrial AI deployment at enterprise scale | Limited | Strong | Keyrus |
| Analytics and ML platform for financial services | Limited | Strong | Keyrus |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Artefact vs Keyrus
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.
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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Artefact vs Keyrus FAQ
Is Artefact better than Keyrus?
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. Keyrus is better for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience.
How do Artefact and Keyrus differ in pricing?
Artefact uses t&m, retainer pricing with a minimum engagement of $50K+. 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: Artefact 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 Artefact and Keyrus?
Artefact's primary differentiator is: enterprise ml at 1,500-consultant scale across 26 countries — strategy, deployment, and ai factory in one firm. 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 (1,500 vs 3,500+), minimum engagement ($50K+ vs $50K+), and primary industries served (retail, healthcare vs financial, retail).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.