Artefact vs Turing: full comparison for 2026
Last updated: July 2026
Quick verdict
Artefact (4.5/5) edges ahead of Turing (3.8/5) overall. Artefact is the better choice for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. Turing is the stronger option for companies needing rapid access to vetted ML engineers or data scientists for staff augmentation or team extension. The right choice depends on your project size, budget, and required tech stack.
Artefact vs Turing: head-to-head summary
| Criterion | Artefact | Turing |
|---|---|---|
| Founded | 2014 | 2018 |
| HQ | Paris, France | Palo Alto, CA |
| Team size | 1,500 | 6,859 |
| Rating | 4.5 / 5 | 3.8 / 5 |
| Best for | Large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy | Companies needing rapid access to vetted ML engineers or data scientists for staff augmentation or team extension |
| Pricing model | T&M, retainer | Dedicated team, T&M |
| Min. engagement | $50K+ | Not disclosed |
| Primary tech stack | Python, Vertex AI, Azure ML | Python, TensorFlow, PyTorch |
| Industries served | retail, healthcare, fintech, media, telecommunications, FMCG | saas, fintech, healthcare, retail, financial |
Artefact vs Turing: 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.)
Turing
Turing was founded in 2018 by Jonathan Siddharth and Rohan Aroe and is headquartered in Palo Alto, California. The company operates as an AI-powered talent marketplace and technology services firm with a network of 4M+ vetted software engineers, data scientists, and STEM experts. Turing has raised $247M at a $2.2B valuation from WestBridge Capital and Foundation Capital, and serves 1,000+ clients including Fortune 500 companies and governments. Note: Turing is primarily a talent marketplace — clients provide direction; Turing supplies vetted engineers rather than owning ML delivery outcomes. (Funding, valuation, and client count per Turing official website and Crunchbase.)
Services and capabilities: Artefact vs Turing
| Capability | Artefact | Turing |
|---|---|---|
| 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 Turing
| Framework / platform | Artefact | Turing |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | N/A |
Pricing comparison: Artefact vs Turing
| Criterion | Artefact | Turing |
|---|---|---|
| Minimum engagement | $50K+ | Not disclosed |
| Engagement models | T&M, Retainer, Dedicated team | T&M, Dedicated team |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: Artefact vs Turing
| Dimension | Artefact | Turing |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | retail, healthcare, fintech | saas, fintech, healthcare |
| Best use cases | Enterprise AI strategy and ML roadmap, AI factory deployment for CPG brand | Staff augmentation for ML engineering team, Rapid placement of vetted data scientists |
| Typical project type | T&M | T&M |
Artefact vs Turing: 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 |
| Turing | |
|---|---|
| + | 4M+ AI-vetted engineers — largest pre-screened ML talent pool in the category |
| + | $2.2B valuation with $247M raised — stable platform with institutional backing |
| + | 1,000+ clients including Fortune 500 and government organisations |
| + | Fastest path to pre-screened ML engineer placement |
| - | Talent marketplace model — Turing supplies engineers; client provides direction and owns outcomes |
| - | Less suited to projects needing a delivery firm with end-to-end accountability |
| - | Delivery quality depends on client PM capability — not owned by Turing |
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 Turing?
Turing is the right choice for companies needing rapid access to vetted ML engineers or data scientists for staff augmentation or team extension.
AI-vetted 4M+ developer network — fastest route to pre-screened ML talent for staff augmentation. Minimum engagement starts at Not disclosed. Works best with clients in saas, fintech, healthcare, retail, financial.
Decision matrix: Artefact vs Turing
| 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 | Compare: Artefact ($50K+) vs Turing (Not disclosed) |
| You need specialist depth in a specific vertical | Artefact |
| You need staff augmentation or team extension | Turing |
| You need consulting before committing to a build | Artefact |
Use case fit: Artefact vs Turing
| Use case | Artefact fit | Turing fit | Winner |
|---|---|---|---|
| Enterprise AI strategy and ML roadmap | Strong | Limited | Artefact |
| AI factory deployment for CPG brand | Strong | Strong | Both equally |
| Staff augmentation for ML engineering team | Limited | Strong | Turing |
| Rapid placement of vetted data scientists | Limited | Strong | Turing |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Strong | Turing |
Verdict: Artefact vs Turing
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.
Turing (3.8/5) is the better choice when companies needing rapid access to vetted ML engineers or data scientists for staff augmentation or team extension. If your situation matches those criteria, Turing is a competitive option.
Related comparisons
Artefact vs Turing FAQ
Is Artefact better than Turing?
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. Turing is better for companies needing rapid access to vetted ML engineers or data scientists for staff augmentation or team extension.
How do Artefact and Turing differ in pricing?
Artefact uses t&m, retainer pricing with a minimum engagement of $50K+. Turing uses dedicated team, t&m pricing with a minimum engagement of Not disclosed. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Artefact or Turing?
Turing 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 Turing?
Artefact's primary differentiator is: enterprise ml at 1,500-consultant scale across 26 countries — strategy, deployment, and ai factory in one firm. Turing's primary differentiator is: ai-vetted 4m+ developer network — fastest route to pre-screened ml talent for staff augmentation. They also differ in team size (1,500 vs 6,859), minimum engagement ($50K+ vs Not disclosed), and primary industries served (retail, healthcare vs saas, fintech).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.