InData Labs vs Artefact: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.6/5) edges ahead of Artefact (4.5/5) overall. InData Labs is the better choice for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems. Artefact is the stronger option for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Artefact: head-to-head summary
| Criterion | InData Labs | Artefact |
|---|---|---|
| Founded | 2014 | 2014 |
| HQ | Nicosia, Cyprus | Paris, France |
| Team size | 80+ | 1,500 |
| Rating | 4.6 / 5 | 4.5 / 5 |
| Best for | Fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems | Large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy |
| Pricing model | Fixed project, T&M | T&M, retainer |
| Min. engagement | $15K | $50K+ |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, Vertex AI, Azure ML |
| Industries served | fintech, healthcare, saas, retail, logistics | retail, healthcare, fintech, media, telecommunications, FMCG |
InData Labs vs Artefact: overview
InData Labs
InData Labs is a data science and AI consultancy founded in 2014, with headquarters in Nicosia, Cyprus and offices in Lithuania and the US. The firm covers the full ML stack: generative AI (LLMs, RAG systems, AI agents), predictive ML (recommendation engines, churn models, computer vision), data engineering, and DevOps for AI infrastructure. With 80+ data science professionals, it focuses on mid-market clients in fintech, healthcare, SaaS, retail, and logistics. (Team size per company LinkedIn; independently verified.)
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: InData Labs vs Artefact
| Capability | InData Labs | 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: InData Labs vs Artefact
| Framework / platform | InData Labs | Artefact |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS SageMaker | N/A | ✓ |
| Azure ML | N/A | ✓ |
Pricing comparison: InData Labs vs Artefact
| Criterion | InData Labs | Artefact |
|---|---|---|
| Minimum engagement | $15K | $50K+ |
| Engagement models | Fixed project, T&M | T&M, Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: InData Labs vs Artefact
| Dimension | InData Labs | Artefact |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | fintech, healthcare, saas | retail, healthcare, fintech |
| Best use cases | GenAI and RAG-based knowledge management system, Churn prediction model for SaaS | Enterprise AI strategy and ML roadmap, AI factory deployment for CPG brand |
| Typical project type | Fixed project | T&M |
InData Labs vs Artefact: pros and cons
| InData Labs | |
|---|---|
| + | 10+ years of pure ML/AI focus — not a repositioned generalist practice |
| + | Production-grade GenAI including RAG and AI agent systems |
| + | Covers the full stack: ML engineering, data engineering, and MLOps |
| + | Strong track record in regulated industries (fintech, healthcare) |
| + | Verified Clutch and DesignRush ratings across multiple client reviews |
| - | Smaller team (80+) limits capacity for very large concurrent programmes |
| - | Not a staffing platform — less suited to pure team augmentation needs |
| 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 InData Labs?
InData Labs is the right choice for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems.
Deep ML and GenAI specialist with 10+ years of production deployments across regulated industries. Minimum engagement starts at $15K. Works best with clients in fintech, healthcare, saas, retail, logistics.
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.
Decision matrix: InData Labs vs Artefact
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | InData Labs |
| You need a large dedicated team for an ongoing programme | Artefact |
| Your budget is at the lower end | InData Labs |
| 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 | InData Labs |
Use case fit: InData Labs vs Artefact
| Use case | InData Labs fit | Artefact fit | Winner |
|---|---|---|---|
| GenAI and RAG-based knowledge management system | Strong | Limited | InData Labs |
| Churn prediction model for SaaS | Strong | Limited | InData Labs |
| 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: InData Labs vs Artefact
InData Labs (4.6/5) is the stronger overall choice for most Machine Learning projects. Deep ML and GenAI specialist with 10+ years of production deployments across regulated industries. It is best for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems.
Artefact (4.5/5) is the better choice when large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. If your situation matches those criteria, Artefact is a competitive option.
Related comparisons
InData Labs vs Artefact FAQ
Is InData Labs better than Artefact?
InData Labs (4.6/5) scores higher overall, but "better" depends on your use case. InData Labs is better for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems. Artefact is better for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy.
How do InData Labs and Artefact differ in pricing?
InData Labs uses fixed project, t&m pricing with a minimum engagement of $15K. 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: InData Labs 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 InData Labs and Artefact?
InData Labs's primary differentiator is: deep ml and genai specialist with 10+ years of production deployments across regulated industries. 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 (80+ vs 1,500), minimum engagement ($15K vs $50K+), and primary industries served (fintech, healthcare vs retail, healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.