DataRoot Labs vs DEPT: full comparison for 2026
Last updated: July 2026
Quick verdict
DataRoot Labs (4.5/5) edges ahead of DEPT (4.0/5) overall. DataRoot Labs is the better choice for startups and SMBs needing a lean, senior custom ML team at competitive Eastern European rates. 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.
DataRoot Labs vs DEPT: head-to-head summary
| Criterion | DataRoot Labs | DEPT |
|---|---|---|
| Founded | 2016 | 2015 |
| HQ | Kyiv, Ukraine | Amsterdam, Netherlands |
| Team size | 11–50 | 1000+ |
| Rating | 4.5 / 5 | 4.0 / 5 |
| Best for | Startups and SMBs needing a lean, senior custom ML team at competitive Eastern European rates | Large enterprise brands needing ML-driven marketing personalization at global scale |
| Pricing model | Fixed project, dedicated team | Retainer, dedicated team |
| Min. engagement | $15K | $75K |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, GCP, AWS |
| Industries served | Healthcare, Retail, Logistics, E-commerce | Retail, Media, Enterprise, E-commerce |
DataRoot Labs vs DEPT: overview
DataRoot Labs
DataRoot Labs is an AI and machine learning development company founded in 2016 in Kyiv, Ukraine by Ivan Didur, Max Frolov, and Yuliya Sychikova. With a compact team of roughly 26 specialists, the studio builds custom ML solutions spanning computer vision, predictive analytics, and NLP for clients in healthcare, retail, and logistics. As an unfunded, founder-led company, it operates with lean overhead and close founder involvement on client projects.
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: DataRoot Labs vs DEPT
| Capability | DataRoot Labs | DEPT |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP | ✓ | ✗ |
| Generative AI / LLM integration | ✗ | ✓ |
| MLOps | ✗ | ✗ |
| AI strategy consulting | ✗ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: DataRoot Labs vs DEPT
| Framework / platform | DataRoot Labs | DEPT |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: DataRoot Labs vs DEPT
| Criterion | DataRoot Labs | DEPT |
|---|---|---|
| Minimum engagement | $15K | $75K |
| Engagement models | Fixed project, Dedicated team | Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: DataRoot Labs vs DEPT
| Dimension | DataRoot Labs | DEPT |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Retail, Logistics | Retail, Media, Enterprise |
| Best use cases | Computer vision for retail shelf and inventory monitoring, Predictive analytics for healthcare patient outcomes | ML-driven marketing personalization at global brand scale, Enterprise data engineering supporting a large media or retail platform |
| Typical project type | Fixed project | Retainer |
DataRoot Labs vs DEPT: pros and cons
| DataRoot Labs | |
|---|---|
| + | Nearly a decade of focused delivery experience since founding in 2016 |
| + | Founder-led team keeps senior expertise directly involved in client work |
| + | Competitive Eastern European pricing relative to Western European or US firms |
| + | Specific vertical depth in healthcare and retail computer vision use cases |
| - | Ukraine-based delivery carries geopolitical and operational-continuity risk clients should factor into vendor due diligence |
| - | Small team (around 26) limits capacity for large concurrent programmes |
| - | Remains unfunded and bootstrapped, which may limit scaling speed versus VC-backed peers |
| 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 DataRoot Labs?
DataRoot Labs is the right choice for startups and SMBs needing a lean, senior custom ML team at competitive Eastern European rates.
Founder-led, unfunded boutique with nearly a decade of focused custom ML delivery experience. Minimum engagement starts at $15K. Works best with clients in Healthcare, Retail, Logistics, E-commerce.
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: DataRoot Labs vs DEPT
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DataRoot Labs |
| You need a large dedicated team for an ongoing programme | DataRoot Labs |
| Your budget is at the lower end | DataRoot Labs |
| You need specialist depth in a specific vertical | DataRoot Labs |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | DEPT |
Use case fit: DataRoot Labs vs DEPT
| Use case | DataRoot Labs fit | DEPT fit | Winner |
|---|---|---|---|
| Computer vision for retail shelf and inventory monitoring | Strong | Limited | DataRoot Labs |
| Predictive analytics for healthcare patient outcomes | Strong | Limited | DataRoot Labs |
| 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: DataRoot Labs vs DEPT
DataRoot Labs (4.5/5) is the stronger overall choice for most Machine Learning Development projects. Founder-led, unfunded boutique with nearly a decade of focused custom ML delivery experience. It is best for startups and SMBs needing a lean, senior custom ML team at competitive Eastern European rates.
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
DataRoot Labs vs DEPT FAQ
Is DataRoot Labs better than DEPT?
DataRoot Labs (4.5/5) scores higher overall, but "better" depends on your use case. DataRoot Labs is better for startups and SMBs needing a lean, senior custom ML team at competitive Eastern European rates. DEPT is better for large enterprise brands needing ML-driven marketing personalization at global scale.
How do DataRoot Labs and DEPT differ in pricing?
DataRoot Labs uses fixed project, dedicated team pricing with a minimum engagement of $15K. 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: DataRoot Labs or DEPT?
DataRoot Labs 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 DataRoot Labs and DEPT?
DataRoot Labs's primary differentiator is: founder-led, unfunded boutique with nearly a decade of focused custom ml delivery experience. 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 (11–50 vs 1000+), minimum engagement ($15K vs $75K), and primary industries served (Healthcare, Retail vs Retail, Media).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.