FELD M vs DEPT: full comparison for 2026
Quick verdict
FELD M (4.2/5) edges ahead of DEPT (4.0/5) overall. FELD M is the better choice for EU enterprises, long-established multi-country AI consulting. DEPT is the stronger option for large brands, ML-driven marketing personalization at scale. The right choice depends on your project size, budget, and required tech stack.
FELD M vs DEPT: head-to-head summary
| Criterion | FELD M | DEPT |
|---|---|---|
| Founded | 2002 | 2015 |
| HQ | Munich, Germany | Amsterdam, Netherlands |
| Team size | 51–200 | 1000+ |
| Rating | 4.2 / 5 | 4.0 / 5 |
| Primary differentiator | Over two decades of operating history since founding in 2002, with organic growth into a five-office pan-European practice | 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 |
| Pricing model | Retainer, fixed project | Retainer, dedicated team |
| Min. engagement | $25K | $75K |
| Primary tech stack | Python, Google Cloud, Azure | Python, GCP, AWS |
| Industries served | Retail, Media, Automotive, Financial Services | Retail, Media, Enterprise, E-commerce |
FELD M vs DEPT: overview
FELD M
FELD M was founded in 2002 in Munich as a one-person web analytics consultancy and has grown into a team of around 60 employees, with offices in Munich, Berlin, Hamburg, Warsaw (FELD M Poland), and Basel (FELD M Switzerland). The firm offers AI, data science, and machine learning product consulting for enterprise clients.
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: FELD M vs DEPT
| Capability | FELD M | DEPT |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✗ | ✓ |
| MLOps | ✗ | ✗ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: FELD M vs DEPT
| Framework / platform | FELD M | DEPT |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | N/A | ✓ |
| PyTorch | N/A | N/A |
| AWS | N/A | ✓ |
| Azure | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: FELD M vs DEPT
| Criterion | FELD M | DEPT |
|---|---|---|
| Minimum engagement | $25K | $75K |
| Engagement models | Retainer, Fixed project | Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: FELD M vs DEPT
| Dimension | FELD M | DEPT |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Retail, Media, Automotive | Retail, Media, Enterprise |
| Best use cases | Data and AI strategy consulting for an enterprise client, Predictive analytics for retail or media audience data | ML-driven marketing personalization at global brand scale, Enterprise data engineering supporting a large media or retail platform |
| Typical project type | Retainer | Retainer |
FELD M vs DEPT: pros and cons
| FELD M | |
|---|---|
| + | Over two decades of operating history since founding in 2002, among the longest-running firms on this list |
| + | Multi-country footprint across Germany, Poland, and Switzerland supports pan-European delivery |
| + | Grew organically from a single-client analytics practice into a full AI and data consultancy |
| + | Deep experience translating business analytics needs into ML and data science products |
| - | Roots in web analytics consulting mean ML engineering depth is narrower than pure-play ML specialists |
| - | Mid-size team of around 60 spread across five offices, which may limit concentration on any single project |
| 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 FELD M?
A typical fit: data and AI strategy consulting for an enterprise client.
Over two decades of operating history since founding in 2002, with organic growth into a five-office pan-European practice. Minimum engagement starts at $25K. Works best with clients in Retail, Media, Automotive, Financial Services.
Who should choose DEPT?
A typical fit: ML-driven marketing personalization at global brand 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: FELD M vs DEPT
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | FELD M |
| You need a large dedicated team for an ongoing programme | DEPT |
| Your budget is at the lower end | FELD M |
| You need specialist depth in a specific vertical | FELD M |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | FELD M |
Use case fit: FELD M vs DEPT
| Use case | FELD M fit | DEPT fit | Winner |
|---|---|---|---|
| Data and AI strategy consulting for an enterprise client | Strong | Strong | Both equally |
| Predictive analytics for retail or media audience data | Strong | Limited | FELD M |
| 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: FELD M vs DEPT
FELD M (4.2/5) is the stronger overall choice for most Machine Learning Development projects. Over two decades of operating history since founding in 2002, with organic growth into a five-office pan-European practice.
DEPT (4.0/5) is worth a look if you need enterprise data engineering supporting a large media or retail platform. If your situation matches that, DEPT is a competitive option.
Related comparisons
FELD M vs DEPT FAQ
Is FELD M better than DEPT?
FELD M (4.2/5) scores higher overall, but "better" depends on your use case. FELD M's strongest advantage: over two decades of operating history since founding in 2002, among the longest-running firms on this list. DEPT's strongest advantage: global scale of over 4,000 specialists across 30-plus offices, unmatched by any other firm on this list.
How do FELD M and DEPT differ in pricing?
FELD M uses retainer, fixed project pricing with a minimum engagement of $25K. 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: FELD M or DEPT?
FELD M 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 FELD M and DEPT?
FELD M's primary differentiator is: over two decades of operating history since founding in 2002, with organic growth into a five-office pan-European practice. 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 ($25K vs $75K), and primary industries served (Retail, Media vs Retail, Media).