Artefact vs DataArt: full comparison for 2026
Last updated: July 2026
Quick verdict
Artefact (4.5/5) edges ahead of DataArt (3.9/5) overall. Artefact is the better choice for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. DataArt is the stronger option for enterprises wanting ML services from a large, established software engineering firm with fintech or travel domain depth. The right choice depends on your project size, budget, and required tech stack.
Artefact vs DataArt: head-to-head summary
| Criterion | Artefact | DataArt |
|---|---|---|
| Founded | 2014 | 1997 |
| HQ | Paris, France | New York, NY |
| Team size | 1,500 | 5,700+ |
| Rating | 4.5 / 5 | 3.9 / 5 |
| Best for | Large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy | Enterprises wanting ML services from a large, established software engineering firm with fintech or travel domain depth |
| Pricing model | T&M, retainer | T&M, dedicated team |
| Min. engagement | $50K+ | $50K+ |
| Primary tech stack | Python, Vertex AI, Azure ML | Python, TensorFlow, PyTorch |
| Industries served | retail, healthcare, fintech, media, telecommunications, FMCG | fintech, healthcare, travel, media, retail |
Artefact vs DataArt: 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.)
DataArt
DataArt was founded in 1997 by Eugene Goland and is headquartered in New York, with offices across 15 global locations and 5,700+ employees. The company delivers AI and ML services — predictive analytics, NLP, data mining, and computer vision — alongside broader software engineering for clients in fintech, healthcare, and travel. DataArt was named an Inc. 5000 honoree in 2024. ML is one service line among many in DataArt's broad software engineering portfolio. (Employee count and founding year per DataArt Wikipedia and official website.)
Services and capabilities: Artefact vs DataArt
| Capability | Artefact | DataArt |
|---|---|---|
| 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 DataArt
| Framework / platform | Artefact | DataArt |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | N/A |
Pricing comparison: Artefact vs DataArt
| Criterion | Artefact | DataArt |
|---|---|---|
| Minimum engagement | $50K+ | $50K+ |
| 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 DataArt
| Dimension | Artefact | DataArt |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | retail, healthcare, fintech | fintech, healthcare, travel |
| Best use cases | Enterprise AI strategy and ML roadmap, AI factory deployment for CPG brand | ML feature integration into existing fintech platform, Travel recommendation engine |
| Typical project type | T&M | T&M |
Artefact vs DataArt: 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 |
| DataArt | |
|---|---|
| + | 5,700+ engineers — sufficient capacity for large parallel programmes |
| + | 29 years of software delivery history — low company risk |
| + | Strong fintech and travel sector domain depth |
| + | Inc. 5000 2024 — verified revenue growth |
| + | 15 global offices for enterprise procurement alignment |
| - | ML is one practice among many — not a pure ML specialist |
| - | Minimum engagement and overhead suited to enterprise, not startups |
| - | Large firm processes can reduce speed relative to boutique ML agencies |
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 DataArt?
DataArt is the right choice for enterprises wanting ML services from a large, established software engineering firm with fintech or travel domain depth.
1997-founded, 5,700-engineer global firm — enterprise scale and continuity across ML and software in fintech and travel. Minimum engagement starts at $50K+. Works best with clients in fintech, healthcare, travel, media, retail.
Decision matrix: Artefact vs DataArt
| 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 DataArt
| Use case | Artefact fit | DataArt fit | Winner |
|---|---|---|---|
| Enterprise AI strategy and ML roadmap | Strong | Strong | Both equally |
| AI factory deployment for CPG brand | Strong | Limited | Artefact |
| ML feature integration into existing fintech platform | Strong | Strong | Both equally |
| Travel recommendation engine | Limited | Strong | DataArt |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Artefact vs DataArt
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.
DataArt (3.9/5) is the better choice when enterprises wanting ML services from a large, established software engineering firm with fintech or travel domain depth. If your situation matches those criteria, DataArt is a competitive option.
Related comparisons
Artefact vs DataArt FAQ
Is Artefact better than DataArt?
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. DataArt is better for enterprises wanting ML services from a large, established software engineering firm with fintech or travel domain depth.
How do Artefact and DataArt differ in pricing?
Artefact uses t&m, retainer pricing with a minimum engagement of $50K+. DataArt uses t&m, dedicated team 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 DataArt?
DataArt 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 DataArt?
Artefact's primary differentiator is: enterprise ml at 1,500-consultant scale across 26 countries — strategy, deployment, and ai factory in one firm. DataArt's primary differentiator is: 1997-founded, 5,700-engineer global firm — enterprise scale and continuity across ml and software in fintech and travel. They also differ in team size (1,500 vs 5,700+), minimum engagement ($50K+ vs $50K+), and primary industries served (retail, healthcare vs fintech, healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.