Tensorway vs Artefact: full comparison for 2026
Quick verdict
Tensorway (4.8/5) edges ahead of Artefact (4.5/5) overall. Tensorway is the better choice for mid-market teams, full-lifecycle ML ownership. Artefact is the stronger option for large enterprises, industrial-scale ML and data strategy. The right choice depends on your project size, budget, and required tech stack.
Tensorway vs Artefact: head-to-head summary
| Criterion | Tensorway | Artefact |
|---|---|---|
| Founded | 2019 | 2014 |
| HQ | Alicante, Spain | Paris, France |
| Team size | 50+ | 1,500 |
| Rating | 4.8 / 5 | 4.5 / 5 |
| Primary differentiator | Full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team | Enterprise ML at 1,500-consultant scale across 26 countries — strategy, deployment, and AI factory in one firm |
| Pricing model | Fixed project, T&M, retainer, dedicated team | T&M, retainer |
| Min. engagement | $10K | $50K+ |
| Primary tech stack | Python, scikit-learn, XGBoost | Python, Vertex AI, Azure ML |
| Industries served | e-commerce, logistics, fintech, healthcare, travel | retail, healthcare, fintech, media, telecommunications, FMCG |
Tensorway vs Artefact: overview
Tensorway
Tensorway is a machine learning engineering firm operating as a dedicated ML-focused unit of its parent company, a software development firm established in 2001. It specialises in custom ML product builds that require sustained ownership — covering model design, training infrastructure, MLOps pipelines, and ongoing drift monitoring under one team. Its core stack includes Python (scikit-learn, XGBoost, LightGBM), Prophet for time-series, and cloud platforms such as AWS SageMaker and Azure ML. Industries served include e-commerce, logistics, fintech, healthcare, and online travel.
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.)
Services and capabilities: Tensorway vs Artefact
| Capability | Tensorway | Artefact |
|---|---|---|
| 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: Tensorway vs Artefact
| Framework / platform | Tensorway | Artefact |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | N/A | ✓ |
| PyTorch | N/A | ✓ |
| AWS SageMaker | ✓ | ✓ |
| Azure ML | ✓ | ✓ |
Pricing comparison: Tensorway vs Artefact
| Criterion | Tensorway | Artefact |
|---|---|---|
| Minimum engagement | $10K | $50K+ |
| Engagement models | Fixed project, T&M, Retainer, Dedicated team | T&M, Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Tensorway vs Artefact
| Dimension | Tensorway | Artefact |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | e-commerce, logistics, fintech | retail, healthcare, fintech |
| Best use cases | Computer vision model for medical imaging diagnostics, NLP-based guest experience assistant for hospitality brands | Enterprise AI strategy and ML roadmap, AI factory deployment for CPG brand |
| Typical project type | Fixed project | T&M |
Tensorway vs Artefact: pros and cons
| Tensorway | |
|---|---|
| + | Boutique team structure — clients work directly with senior deep learning engineers, not account managers |
| + | Hands-on production ML delivery on AWS across computer vision and NLP workloads |
| + | Deep specialisation in deep learning, NLP, computer vision, and agentic AI rather than broad ML generalism |
| + | Established project-management and QA processes for predictable, well-documented delivery |
| + | Strong delivery track record in deep learning and NLP, with client references available under NDA |
| - | Smaller specialist team (50+) — less suited to very large enterprise programmes needing broad staffing |
| - | AWS-centric delivery — teams standardized on other clouds may need added integration effort |
| 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 |
Who should choose Tensorway?
A typical fit: computer vision model for medical imaging diagnostics.
Full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team. Minimum engagement starts at $10K. Works best with clients in e-commerce, logistics, fintech, healthcare, travel.
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.
Decision matrix: Tensorway vs Artefact
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Tensorway |
| You need a large dedicated team for an ongoing programme | Tensorway |
| Your budget is at the lower end | Tensorway |
| You need specialist depth in a specific vertical | Artefact |
| You need staff augmentation or team extension | Tensorway |
| You need consulting before committing to a build | Tensorway |
Use case fit: Tensorway vs Artefact
| Use case | Tensorway fit | Artefact fit | Winner |
|---|---|---|---|
| Computer vision model for medical imaging diagnostics | Strong | Limited | Tensorway |
| NLP-based guest experience assistant for hospitality brands | Strong | Limited | Tensorway |
| Enterprise AI strategy and ML roadmap | Limited | Strong | Artefact |
| AI factory deployment for CPG brand | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Tensorway vs Artefact
Tensorway (4.8/5) is the stronger overall choice for most Machine Learning projects. Full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team.
Artefact (4.5/5) is worth a look if you need AI factory deployment for CPG brand. If your situation matches that, Artefact is a competitive option.
Related comparisons
Tensorway vs Artefact FAQ
Is Tensorway better than Artefact?
Tensorway (4.8/5) scores higher overall, but "better" depends on your use case. Tensorway's strongest advantage: boutique team structure — clients work directly with senior deep learning engineers, not account managers. Artefact's strongest advantage: global delivery footprint: 33 offices in 26 countries.
How do Tensorway and Artefact differ in pricing?
Tensorway uses fixed project, t&m, retainer, dedicated team pricing with a minimum engagement of $10K. Artefact 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: Tensorway or Artefact?
Artefact 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 Tensorway and Artefact?
Tensorway's primary differentiator is: full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team. Artefact's primary differentiator is: enterprise ML at 1,500-consultant scale across 26 countries — strategy, deployment, and AI factory in one firm. They also differ in team size (50+ vs 1,500), minimum engagement ($10K vs $50K+), and primary industries served (e-commerce, logistics vs retail, healthcare).