Artefact vs Azumo: full comparison for 2026
Last updated: July 2026
Quick verdict
Artefact (4.5/5) edges ahead of Azumo (3.8/5) overall. Artefact is the better choice for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy. Azumo is the stronger option for uS companies seeking cost-effective nearshore ML development with Latin American time-zone alignment. The right choice depends on your project size, budget, and required tech stack.
Artefact vs Azumo: head-to-head summary
| Criterion | Artefact | Azumo |
|---|---|---|
| Founded | 2014 | 2016 |
| HQ | Paris, France | San Francisco, CA |
| Team size | 1,500 | 100–250 |
| Rating | 4.5 / 5 | 3.8 / 5 |
| Best for | Large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy | US companies seeking cost-effective nearshore ML development with Latin American time-zone alignment |
| Pricing model | T&M, retainer | T&M, dedicated team |
| Min. engagement | $50K+ | $25K+ |
| Primary tech stack | Python, Vertex AI, Azure ML | Python, TensorFlow, PyTorch |
| Industries served | retail, healthcare, fintech, media, telecommunications, FMCG | saas, fintech, healthcare, retail, logistics |
Artefact vs Azumo: 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.)
Azumo
Azumo was founded in 2016 and is headquartered in San Francisco, with its development centre in Latin America. The company positions itself as a nearshore AI and ML engineering partner for US companies, providing cost-effective development with US time-zone alignment. Azumo offers AI vision models for mobile, web, and edge devices alongside general ML engineering. (Founding year, HQ, and delivery model per Azumo official website.)
Services and capabilities: Artefact vs Azumo
| Capability | Artefact | Azumo |
|---|---|---|
| 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 Azumo
| Framework / platform | Artefact | Azumo |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | ✓ |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | N/A |
Pricing comparison: Artefact vs Azumo
| Criterion | Artefact | Azumo |
|---|---|---|
| Minimum engagement | $50K+ | $25K+ |
| Engagement models | T&M, Retainer, Dedicated team | T&M, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Artefact vs Azumo
| Dimension | Artefact | Azumo |
|---|---|---|
| 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 | Computer vision for edge or mobile device, ML model for mobile fintech app |
| Typical project type | T&M | T&M |
Artefact vs Azumo: 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 |
| Azumo | |
|---|---|
| + | Latin American nearshore team — US time-zone alignment without premium on-shore costs |
| + | Computer vision and mobile ML specialisation |
| + | US-headquartered leadership for accountability and IP clarity |
| + | Edge device and mobile ML deployment experience |
| - | Nearshore delivery model requires strong async communication discipline |
| - | Less depth in data engineering or MLOps compared to larger ML firms |
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 Azumo?
Azumo is the right choice for uS companies seeking cost-effective nearshore ML development with Latin American time-zone alignment.
Latin American nearshore delivery — US time-zone alignment with rates below fully on-shore alternatives. Minimum engagement starts at $25K+. Works best with clients in saas, fintech, healthcare, retail, logistics.
Decision matrix: Artefact vs Azumo
| 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 | Azumo |
| 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 Azumo
| Use case | Artefact fit | Azumo fit | Winner |
|---|---|---|---|
| Enterprise AI strategy and ML roadmap | Strong | Limited | Artefact |
| AI factory deployment for CPG brand | Strong | Strong | Both equally |
| Computer vision for edge or mobile device | Limited | Strong | Azumo |
| ML model for mobile fintech app | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Strong | Azumo |
Verdict: Artefact vs Azumo
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.
Azumo (3.8/5) is the better choice when uS companies seeking cost-effective nearshore ML development with Latin American time-zone alignment. If your situation matches those criteria, Azumo is a competitive option.
Related comparisons
Artefact vs Azumo FAQ
Is Artefact better than Azumo?
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. Azumo is better for uS companies seeking cost-effective nearshore ML development with Latin American time-zone alignment.
How do Artefact and Azumo differ in pricing?
Artefact uses t&m, retainer pricing with a minimum engagement of $50K+. Azumo uses t&m, dedicated team 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 Azumo?
Azumo 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 Azumo?
Artefact's primary differentiator is: enterprise ml at 1,500-consultant scale across 26 countries — strategy, deployment, and ai factory in one firm. Azumo's primary differentiator is: latin american nearshore delivery — us time-zone alignment with rates below fully on-shore alternatives. They also differ in team size (1,500 vs 100–250), minimum engagement ($50K+ vs $25K+), and primary industries served (retail, healthcare vs saas, fintech).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.