Artefact vs Miquido: full comparison for 2026
Last updated: July 2026
Quick verdict
Artefact (4.5/5) edges ahead of Miquido (4.2/5) overall. Artefact is the better choice for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. Miquido is the stronger option for product companies and scale-ups needing ML features embedded within polished mobile or web products. The right choice depends on your project size, budget, and required tech stack.
Artefact vs Miquido: head-to-head summary
| Criterion | Artefact | Miquido |
|---|---|---|
| Founded | 2014 | 2011 |
| HQ | Paris, France | Kraków, Poland |
| Team size | 1,500 | 200+ |
| Rating | 4.5 / 5 | 4.2 / 5 |
| Best for | Large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy | Product companies and scale-ups needing ML features embedded within polished mobile or web products |
| Pricing model | T&M, retainer | Fixed project, T&M |
| Min. engagement | $50K+ | $25K+ |
| Primary tech stack | Python, Vertex AI, Azure ML | Python, TensorFlow, PyTorch |
| Industries served | retail, healthcare, fintech, media, telecommunications, FMCG | saas, media, retail, healthcare, fintech |
Artefact vs Miquido: 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.)
Miquido
Miquido was founded in 2011 and is headquartered in Kraków, Poland, with 200+ engineers. The company specialises in AI and ML development integrated within mobile and web product engineering, serving clients including Skyscanner and Abbey Road Studios (per Miquido Clutch profile and official website). Miquido is known for combining UI/UX engineering with AI capabilities — particularly computer vision, recommendation systems, and NLP — for product-driven clients.
Services and capabilities: Artefact vs Miquido
| Capability | Artefact | Miquido |
|---|---|---|
| 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 Miquido
| Framework / platform | Artefact | Miquido |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | N/A |
Pricing comparison: Artefact vs Miquido
| Criterion | Artefact | Miquido |
|---|---|---|
| Minimum engagement | $50K+ | $25K+ |
| Engagement models | T&M, Retainer, Dedicated team | Fixed project, T&M, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Artefact vs Miquido
| Dimension | Artefact | Miquido |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | retail, healthcare, fintech | saas, media, retail |
| Best use cases | Enterprise AI strategy and ML roadmap, AI factory deployment for CPG brand | AI features within mobile travel app, Recommendation system for media platform |
| Typical project type | T&M | Fixed project |
Artefact vs Miquido: 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 |
| Miquido | |
|---|---|
| + | Strong integration of ML with product and UI engineering — rare combination |
| + | Named clients include Skyscanner and Abbey Road Studios |
| + | Full product lifecycle capability: design to ML to mobile/web delivery |
| + | Kraków studio with transparent pricing and verifiable Clutch reviews |
| + | Computer vision and NLP experience in production applications |
| - | Less suitable for standalone ML research or data science consulting |
| - | Product engineering focus means less depth in MLOps or large-scale data infrastructure |
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 Miquido?
Miquido is the right choice for product companies and scale-ups needing ML features embedded within polished mobile or web products.
AI-plus-product development — ML capabilities integrated with UX engineering, not delivered as a standalone model. Minimum engagement starts at $25K+. Works best with clients in saas, media, retail, healthcare, fintech.
Decision matrix: Artefact vs Miquido
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Miquido |
| You need a large dedicated team for an ongoing programme | Artefact |
| Your budget is at the lower end | Miquido |
| 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 Miquido
| Use case | Artefact fit | Miquido fit | Winner |
|---|---|---|---|
| Enterprise AI strategy and ML roadmap | Strong | Limited | Artefact |
| AI factory deployment for CPG brand | Strong | Strong | Both equally |
| AI features within mobile travel app | Strong | Strong | Both equally |
| Recommendation system for media platform | Limited | Strong | Miquido |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Artefact vs Miquido
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.
Miquido (4.2/5) is the better choice when product companies and scale-ups needing ML features embedded within polished mobile or web products. If your situation matches those criteria, Miquido is a competitive option.
Related comparisons
Artefact vs Miquido FAQ
Is Artefact better than Miquido?
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. Miquido is better for product companies and scale-ups needing ML features embedded within polished mobile or web products.
How do Artefact and Miquido differ in pricing?
Artefact uses t&m, retainer pricing with a minimum engagement of $50K+. Miquido uses fixed project, t&m pricing with a minimum engagement of $25K+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Artefact or Miquido?
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 Artefact and Miquido?
Artefact's primary differentiator is: enterprise ml at 1,500-consultant scale across 26 countries — strategy, deployment, and ai factory in one firm. Miquido's primary differentiator is: ai-plus-product development — ml capabilities integrated with ux engineering, not delivered as a standalone model. They also differ in team size (1,500 vs 200+), minimum engagement ($50K+ vs $25K+), and primary industries served (retail, healthcare vs saas, media).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.