Artefact vs Modak: full comparison for 2026
Last updated: July 2026
Quick verdict
Artefact (4.5/5) edges ahead of Modak (3.7/5) overall. Artefact is the better choice for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. Modak is the stronger option for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption. The right choice depends on your project size, budget, and required tech stack.
Artefact vs Modak: head-to-head summary
| Criterion | Artefact | Modak |
|---|---|---|
| Founded | 2014 | 2016 |
| HQ | Paris, France | San Jose, CA |
| Team size | 1,500 | 100–200 |
| Rating | 4.5 / 5 | 3.7 / 5 |
| Best for | Large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy | Large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption |
| Pricing model | T&M, retainer | T&M, retainer |
| Min. engagement | $50K+ | $50K+ |
| Primary tech stack | Python, Vertex AI, Azure ML | Python, Apache Spark, Databricks |
| Industries served | retail, healthcare, fintech, media, telecommunications, FMCG | financial, healthcare, manufacturing, logistics, saas |
Artefact vs Modak: 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.)
Modak
Modak is an AI-native data engineering company headquartered in San Jose, California, founded in 2016. The company uses machine learning techniques to transform how structured and unstructured enterprise data is prepared, consumed, and shared — focusing on AI-driven data modernisation for large organisations. Global consulting services help enterprises modernise data infrastructure, accelerate AI readiness, and drive measurable business outcomes. (Founding year and approach per Modak official website and ZoomInfo.)
Services and capabilities: Artefact vs Modak
| Capability | Artefact | Modak |
|---|---|---|
| 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 Modak
| Framework / platform | Artefact | Modak |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | N/A |
Pricing comparison: Artefact vs Modak
| Criterion | Artefact | Modak |
|---|---|---|
| Minimum engagement | $50K+ | $50K+ |
| Engagement models | T&M, Retainer, Dedicated team | T&M, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Artefact vs Modak
| Dimension | Artefact | Modak |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | retail, healthcare, fintech | financial, healthcare, manufacturing |
| Best use cases | Enterprise AI strategy and ML roadmap, AI factory deployment for CPG brand | Enterprise data modernisation for AI readiness, ML-powered ETL and data prep pipeline |
| Typical project type | T&M | T&M |
Artefact vs Modak: 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 |
| Modak | |
|---|---|
| + | ML applied to data engineering itself — accelerates data prep for ML programmes |
| + | AI-native from inception — not a repositioned data warehouse firm |
| + | Strong on unstructured data processing for AI readiness |
| + | San Jose HQ with enterprise client focus |
| - | Data engineering focus — not suited to custom ML model development or computer vision |
| - | Minimum engagement oriented toward large enterprise programmes |
| - | Less suited to companies without an existing large data estate |
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 Modak?
Modak is the right choice for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption.
ML-powered data engineering — uses ML itself to accelerate data prep and modernisation at enterprise scale. Minimum engagement starts at $50K+. Works best with clients in financial, healthcare, manufacturing, logistics, saas.
Decision matrix: Artefact vs Modak
| 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 Modak
| Use case | Artefact fit | Modak fit | Winner |
|---|---|---|---|
| Enterprise AI strategy and ML roadmap | Strong | Strong | Both equally |
| AI factory deployment for CPG brand | Strong | Strong | Both equally |
| Enterprise data modernisation for AI readiness | Strong | Strong | Both equally |
| ML-powered ETL and data prep pipeline | Limited | Strong | Modak |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Artefact vs Modak
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.
Modak (3.7/5) is the better choice when large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption. If your situation matches those criteria, Modak is a competitive option.
Related comparisons
Artefact vs Modak FAQ
Is Artefact better than Modak?
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. Modak is better for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption.
How do Artefact and Modak differ in pricing?
Artefact uses t&m, retainer pricing with a minimum engagement of $50K+. Modak 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 Modak?
Modak 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 Modak?
Artefact's primary differentiator is: enterprise ml at 1,500-consultant scale across 26 countries — strategy, deployment, and ai factory in one firm. Modak's primary differentiator is: ml-powered data engineering — uses ml itself to accelerate data prep and modernisation at enterprise scale. They also differ in team size (1,500 vs 100–200), minimum engagement ($50K+ vs $50K+), and primary industries served (retail, healthcare vs financial, healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.