ML6 vs DEPT: full comparison for 2026
Last updated: July 2026
Quick verdict
ML6 (4.7/5) edges ahead of DEPT (4.0/5) overall. ML6 is the better choice for enterprises needing production MLOps infrastructure and multi-cloud AI engineering at scale. DEPT is the stronger option for large enterprise brands needing ML-driven marketing personalization at global scale. The right choice depends on your project size, budget, and required tech stack.
ML6 vs DEPT: head-to-head summary
| Criterion | ML6 | DEPT |
|---|---|---|
| Founded | 2013 | 2015 |
| HQ | Ghent, Belgium | Amsterdam, Netherlands |
| Team size | 51–200 | 1000+ |
| Rating | 4.7 / 5 | 4.0 / 5 |
| Best for | Enterprises needing production MLOps infrastructure and multi-cloud AI engineering at scale | Large enterprise brands needing ML-driven marketing personalization at global scale |
| Pricing model | Dedicated team, fixed project, retainer | Retainer, dedicated team |
| Min. engagement | $40K | $75K |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, GCP, AWS |
| Industries served | Enterprise, Financial Services, Retail, Manufacturing, Public Sector | Retail, Media, Enterprise, E-commerce |
ML6 vs DEPT: overview
ML6
ML6 is a Ghent, Belgium-headquartered AI engineering company founded in 2013 by Michael Lemmer and Nicolas Deruytter. With roughly 150 AI and ML specialists, ML6 is one of Europe's most established pure-play ML consultancies, known for MLOps, computer vision, and enterprise AI infrastructure work. The company was named an OpenAI Services Partner and is a Google Cloud partner, reflecting deep hands-on delivery experience across major model providers.
DEPT
DEPT, founded in Amsterdam in 2015, has grown into a global digital agency with over 4,000 digital specialists across more than 30 offices on five continents, backed by the Carlyle Group. DEPT's AI-enabled marketing technology platform, Ada, and its Engineering practice deliver machine learning-driven personalization, growth, and data engineering work for major brands including Google, TikTok, and eBay. As a large, private-equity-backed marketing and engineering agency, ML and AI here sits within a much broader full-service offering rather than being the firm's sole focus.
Services and capabilities: ML6 vs DEPT
| Capability | ML6 | DEPT |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✓ | ✓ |
| MLOps | ✓ | ✗ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: ML6 vs DEPT
| Framework / platform | ML6 | DEPT |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS | N/A | ✓ |
| Azure | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: ML6 vs DEPT
| Criterion | ML6 | DEPT |
|---|---|---|
| Minimum engagement | $40K | $75K |
| Engagement models | Dedicated team, Fixed project, Retainer | Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: ML6 vs DEPT
| Dimension | ML6 | DEPT |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Enterprise, Financial Services, Retail | Retail, Media, Enterprise |
| Best use cases | Building enterprise-scale MLOps pipelines, Deploying computer vision for manufacturing quality control | ML-driven marketing personalization at global brand scale, Enterprise data engineering supporting a large media or retail platform |
| Typical project type | Dedicated team | Retainer |
ML6 vs DEPT: pros and cons
| ML6 | |
|---|---|
| + | One of Europe's longest-running pure-play ML engineering firms, founded in 2013 |
| + | Official OpenAI Services Partner and Google Cloud partner |
| + | Deep MLOps and production infrastructure expertise, not just model prototyping |
| + | 150-person specialist team with dedicated practice areas across computer vision, NLP, and MLOps |
| - | Higher minimum engagement size than boutique competitors, less suited to small startups |
| - | Primarily Benelux-based delivery, fewer nearshore options for very tight budgets |
| DEPT | |
|---|---|
| + | Global scale of over 4,000 specialists across 30-plus offices, unmatched by any other firm on this list |
| + | Proprietary AI-enabled marketing technology platform, Ada, with proven enterprise brand clients |
| + | Carlyle Group backing provides financial stability for very large, long-term programmes |
| + | Named clients include Google, TikTok, KFC, and eBay, indicating enterprise-grade delivery capacity |
| - | ML and AI sits within a much broader marketing and full-service digital agency offering, not a dedicated ML practice |
| - | High minimum engagement size, inaccessible for startups or small businesses |
| - | Enterprise agency structure means less specialized, boutique-style ML research depth |
Who should choose ML6?
ML6 is the right choice for enterprises needing production MLOps infrastructure and multi-cloud AI engineering at scale.
Official OpenAI Services Partner status combined with over a decade of pure-play ML engineering focus. Minimum engagement starts at $40K. Works best with clients in Enterprise, Financial Services, Retail, Manufacturing, Public Sector.
Who should choose DEPT?
DEPT is the right choice for large enterprise brands needing ML-driven marketing personalization at global scale.
Proprietary AI marketing platform, Ada, and global scale of over 4,000 specialists across 30-plus offices, unmatched by any other firm on this list. Minimum engagement starts at $75K. Works best with clients in Retail, Media, Enterprise, E-commerce.
Decision matrix: ML6 vs DEPT
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | ML6 |
| You need a large dedicated team for an ongoing programme | ML6 |
| Your budget is at the lower end | ML6 |
| You need specialist depth in a specific vertical | ML6 |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | ML6 |
Use case fit: ML6 vs DEPT
| Use case | ML6 fit | DEPT fit | Winner |
|---|---|---|---|
| Building enterprise-scale MLOps pipelines | Strong | Limited | ML6 |
| Deploying computer vision for manufacturing quality control | Strong | Limited | ML6 |
| ML-driven marketing personalization at global brand scale | Limited | Strong | DEPT |
| Enterprise data engineering supporting a large media or retail platform | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: ML6 vs DEPT
ML6 (4.7/5) is the stronger overall choice for most Machine Learning Development projects. Official OpenAI Services Partner status combined with over a decade of pure-play ML engineering focus. It is best for enterprises needing production MLOps infrastructure and multi-cloud AI engineering at scale.
DEPT (4.0/5) is the better choice when large enterprise brands needing ML-driven marketing personalization at global scale. If your situation matches those criteria, DEPT is a competitive option.
Related comparisons
ML6 vs DEPT FAQ
Is ML6 better than DEPT?
ML6 (4.7/5) scores higher overall, but "better" depends on your use case. ML6 is better for enterprises needing production MLOps infrastructure and multi-cloud AI engineering at scale. DEPT is better for large enterprise brands needing ML-driven marketing personalization at global scale.
How do ML6 and DEPT differ in pricing?
ML6 uses dedicated team, fixed project, retainer pricing with a minimum engagement of $40K. DEPT uses retainer, dedicated team pricing with a minimum engagement of $75K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: ML6 or DEPT?
ML6 is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between ML6 and DEPT?
ML6's primary differentiator is: official openai services partner status combined with over a decade of pure-play ml engineering focus. DEPT's primary differentiator is: proprietary ai marketing platform, ada, and global scale of over 4,000 specialists across 30-plus offices, unmatched by any other firm on this list. They also differ in team size (51–200 vs 1000+), minimum engagement ($40K vs $75K), and primary industries served (Enterprise, Financial Services vs Retail, Media).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.