Artefact vs Yalantis: full comparison for 2026
Last updated: July 2026
Quick verdict
Artefact (4.5/5) edges ahead of Yalantis (3.9/5) overall. Artefact is the better choice for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. Yalantis is the stronger option for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering. The right choice depends on your project size, budget, and required tech stack.
Artefact vs Yalantis: head-to-head summary
| Criterion | Artefact | Yalantis |
|---|---|---|
| Founded | 2014 | 2008 |
| HQ | Paris, France | Kyiv, Ukraine |
| Team size | 1,500 | 200–400 |
| Rating | 4.5 / 5 | 3.9 / 5 |
| Best for | Large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy | Healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering |
| Pricing model | T&M, retainer | Fixed project, T&M |
| Min. engagement | $50K+ | $25K+ |
| Primary tech stack | Python, Vertex AI, Azure ML | Python, TensorFlow, PyTorch |
| Industries served | retail, healthcare, fintech, media, telecommunications, FMCG | healthcare, fintech, saas, logistics, manufacturing |
Artefact vs Yalantis: 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.)
Yalantis
Yalantis was founded in 2008 and operates with a focus on compliance-first IoT and software engineering alongside machine learning consulting. The company's ML team provides domain-specific consulting, model deployment, and ongoing support, with depth in regulated industries including healthcare and fintech. ML consultants hold master's degrees in machine learning and have production data science experience. (Founded year per Tracxn; specialisation per Yalantis official website.)
Services and capabilities: Artefact vs Yalantis
| Capability | Artefact | Yalantis |
|---|---|---|
| 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 Yalantis
| Framework / platform | Artefact | Yalantis |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | N/A |
Pricing comparison: Artefact vs Yalantis
| Criterion | Artefact | Yalantis |
|---|---|---|
| Minimum engagement | $50K+ | $25K+ |
| Engagement models | T&M, Retainer, Dedicated team | Fixed project, T&M, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Artefact vs Yalantis
| Dimension | Artefact | Yalantis |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | retail, healthcare, fintech | healthcare, fintech, saas |
| Best use cases | Enterprise AI strategy and ML roadmap, AI factory deployment for CPG brand | Compliance-aware ML model for healthcare data, Predictive analytics for fintech risk management |
| Typical project type | T&M | Fixed project |
Artefact vs Yalantis: 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 |
| Yalantis | |
|---|---|
| + | Compliance-first approach for regulated healthcare and fintech projects |
| + | Full-lifecycle ML: from consulting through deployment and support |
| + | Master's-qualified ML consultants — verifiable technical depth |
| + | IoT integration experience alongside ML — rare combination |
| - | Ukraine-based delivery carries geographic risk considerations for some clients |
| - | Less suited to pure data science research or exploratory projects |
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 Yalantis?
Yalantis is the right choice for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering.
Compliance-first ML delivery — particularly strong for healthcare and regulated fintech with IoT integration needs. Minimum engagement starts at $25K+. Works best with clients in healthcare, fintech, saas, logistics, manufacturing.
Decision matrix: Artefact vs Yalantis
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Yalantis |
| You need a large dedicated team for an ongoing programme | Artefact |
| Your budget is at the lower end | Yalantis |
| 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 Yalantis
| Use case | Artefact fit | Yalantis fit | Winner |
|---|---|---|---|
| Enterprise AI strategy and ML roadmap | Strong | Strong | Both equally |
| AI factory deployment for CPG brand | Strong | Limited | Artefact |
| Compliance-aware ML model for healthcare data | Limited | Strong | Yalantis |
| Predictive analytics for fintech risk management | Limited | Strong | Yalantis |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Artefact vs Yalantis
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.
Yalantis (3.9/5) is the better choice when healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering. If your situation matches those criteria, Yalantis is a competitive option.
Related comparisons
Artefact vs Yalantis FAQ
Is Artefact better than Yalantis?
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. Yalantis is better for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering.
How do Artefact and Yalantis differ in pricing?
Artefact uses t&m, retainer pricing with a minimum engagement of $50K+. Yalantis uses fixed project, t&m 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 Yalantis?
Yalantis 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 Yalantis?
Artefact's primary differentiator is: enterprise ml at 1,500-consultant scale across 26 countries — strategy, deployment, and ai factory in one firm. Yalantis's primary differentiator is: compliance-first ml delivery — particularly strong for healthcare and regulated fintech with iot integration needs. They also differ in team size (1,500 vs 200–400), minimum engagement ($50K+ vs $25K+), and primary industries served (retail, healthcare vs healthcare, fintech).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.