Digica vs DEPT: full comparison for 2026
Quick verdict
Digica (4.1/5) edges ahead of DEPT (4.0/5) overall. Digica is the better choice for regulated industries, ML plus embedded systems. 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.
Digica vs DEPT: head-to-head summary
| Criterion | Digica | DEPT |
|---|---|---|
| Founded | 2009 | 2015 |
| HQ | Altrincham, UK | Amsterdam, Netherlands |
| Team size | 51–200 | 1000+ |
| Rating | 4.1 / 5 | 4.0 / 5 |
| Primary differentiator | Combines ML model development with embedded systems and IoT engineering for regulated hardware-adjacent industries | 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 | Fixed project, dedicated team | Retainer, dedicated team |
| Min. engagement | $30K | $75K |
| Primary tech stack | Python, C++, TensorFlow | Python, GCP, AWS |
| Industries served | Automotive, Defense, Medical Devices, Telecommunications | Retail, Media, Enterprise, E-commerce |
Digica vs DEPT: overview
Digica
Digica, founded in 2009 and legally headquartered in Altrincham, UK, provides AI and machine learning software services with additional delivery centers in Lodz, Poland; Berlin, Germany; and San Jose, California. With over 70 engineers, Digica has trained thousands of machine learning models (3,673 per company website; independently unverifiable) for regulated industries including automotive, defence, and medical devices.
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: Digica vs DEPT
| Capability | Digica | DEPT |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✗ | ✓ |
| MLOps | ✓ | ✗ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: Digica vs DEPT
| Framework / platform | Digica | DEPT |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Digica vs DEPT
| Criterion | Digica | DEPT |
|---|---|---|
| Minimum engagement | $30K | $75K |
| Engagement models | Fixed project, Dedicated team | Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Digica vs DEPT
| Dimension | Digica | DEPT |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Automotive, Defense, Medical Devices | Retail, Media, Enterprise |
| Best use cases | ML model development for automotive ADAS systems, Medical device AI software requiring regulatory compliance | ML-driven marketing personalization at global brand scale, Enterprise data engineering supporting a large media or retail platform |
| Typical project type | Fixed project | Retainer |
Digica vs DEPT: pros and cons
| Digica | |
|---|---|
| + | Over 15 years of operating history since founding in 2009, in regulated, safety-critical industries |
| + | Combines ML expertise with embedded systems and IoT engineering, unusual among ML-only firms |
| + | Multi-country delivery footprint across the UK, Poland, Germany, and the US for coverage flexibility |
| + | Legally headquartered in the UK with EU delivery centers for GDPR-relevant work |
| - | High-volume model-training claims, per company website, are not independently auditable |
| - | Regulated-industry focus may mean longer sales and compliance cycles than consumer-facing ML firms |
| - | Mid-size team of over 70 engineers spread across four countries |
| 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 Digica?
A typical fit: ML model development for automotive ADAS systems.
Combines ML model development with embedded systems and IoT engineering for regulated hardware-adjacent industries. Minimum engagement starts at $30K. Works best with clients in Automotive, Defense, Medical Devices, Telecommunications.
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: Digica vs DEPT
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Digica |
| You need a large dedicated team for an ongoing programme | Digica |
| Your budget is at the lower end | Digica |
| You need specialist depth in a specific vertical | Digica |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Digica |
Use case fit: Digica vs DEPT
| Use case | Digica fit | DEPT fit | Winner |
|---|---|---|---|
| ML model development for automotive ADAS systems | Strong | Strong | Both equally |
| Medical device AI software requiring regulatory compliance | Strong | Limited | Digica |
| ML-driven marketing personalization at global brand scale | Limited | Strong | DEPT |
| Enterprise data engineering supporting a large media or retail platform | Limited | Strong | DEPT |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Digica vs DEPT
Digica (4.1/5) is the stronger overall choice for most Machine Learning Development projects. Combines ML model development with embedded systems and IoT engineering for regulated hardware-adjacent industries.
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.
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Digica vs DEPT FAQ
Is Digica better than DEPT?
Digica (4.1/5) scores higher overall, but "better" depends on your use case. Digica's strongest advantage: over 15 years of operating history since founding in 2009, in regulated, safety-critical industries. 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 Digica and DEPT differ in pricing?
Digica uses fixed project, dedicated team pricing with a minimum engagement of $30K. 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: Digica or DEPT?
Digica 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 Digica and DEPT?
Digica's primary differentiator is: combines ML model development with embedded systems and IoT engineering for regulated hardware-adjacent industries. 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 ($30K vs $75K), and primary industries served (Automotive, Defense vs Retail, Media).