DATAFOREST vs DEPT: full comparison for 2026
Last updated: July 2026
Quick verdict
DATAFOREST (4.1/5) edges ahead of DEPT (4.0/5) overall. DATAFOREST is the better choice for small and mid-market businesses needing data engineering plus ML analytics as a combined offering. 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.
DATAFOREST vs DEPT: head-to-head summary
| Criterion | DATAFOREST | DEPT |
|---|---|---|
| Founded | 2018 | 2015 |
| HQ | Kyiv, Ukraine | Amsterdam, Netherlands |
| Team size | 51–200 | 1000+ |
| Rating | 4.1 / 5 | 4.0 / 5 |
| Best for | Small and mid-market businesses needing data engineering plus ML analytics as a combined offering | 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, Airflow, AWS | Python, GCP, AWS |
| Industries served | E-commerce, SaaS, Fintech, Healthcare | Retail, Media, Enterprise, E-commerce |
DATAFOREST vs DEPT: overview
DATAFOREST
DATAFOREST is a data science and software development agency founded in 2018, headquartered in Kyiv, Ukraine, with an additional office in New York. The company, with an estimated 50 to 249 employees, provides ETL pipelines, data analytics, and custom machine learning solutions, and has been recognized by The Manifest as a top-reviewed IT agency in Ukraine, per company website; independently unverifiable.
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: DATAFOREST vs DEPT
| Capability | DATAFOREST | DEPT |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✗ | ✓ |
| MLOps | ✗ | ✗ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: DATAFOREST vs DEPT
| Framework / platform | DATAFOREST | DEPT |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | N/A | ✓ |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: DATAFOREST vs DEPT
| Criterion | DATAFOREST | 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: DATAFOREST vs DEPT
| Dimension | DATAFOREST | DEPT |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | E-commerce, SaaS, Fintech | Retail, Media, Enterprise |
| Best use cases | Building ETL pipelines feeding a downstream ML model, Predictive analytics for e-commerce customer behavior | ML-driven marketing personalization at global brand scale, Enterprise data engineering supporting a large media or retail platform |
| Typical project type | Fixed project | Retainer |
DATAFOREST vs DEPT: pros and cons
| DATAFOREST | |
|---|---|
| + | Combines core data engineering (ETL and pipelines) with ML analytics under one team |
| + | Growing review base and recognition from The Manifest as a top-reviewed Ukraine IT agency |
| + | Competitive pricing relative to Western European ML firms |
| + | New York office adds coverage for US-based clients |
| - | Kyiv, Ukraine-based delivery carries the same operational-continuity considerations as other Ukraine-linked firms |
| - | Founded in 2018, a shorter track record than more established European ML consultancies |
| - | Data engineering heritage means the ML practice is comparatively newer within the firm |
| 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 DATAFOREST?
DATAFOREST is the right choice for small and mid-market businesses needing data engineering plus ML analytics as a combined offering.
Combined data engineering (ETL) and ML analytics practice with a growing review base. Minimum engagement starts at $15K. Works best with clients in E-commerce, SaaS, Fintech, Healthcare.
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: DATAFOREST vs DEPT
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DATAFOREST |
| You need a large dedicated team for an ongoing programme | DATAFOREST |
| Your budget is at the lower end | DATAFOREST |
| You need specialist depth in a specific vertical | DATAFOREST |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | DATAFOREST |
Use case fit: DATAFOREST vs DEPT
| Use case | DATAFOREST fit | DEPT fit | Winner |
|---|---|---|---|
| Building ETL pipelines feeding a downstream ML model | Strong | Limited | DATAFOREST |
| Predictive analytics for e-commerce customer behavior | Strong | Limited | DATAFOREST |
| 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: DATAFOREST vs DEPT
DATAFOREST (4.1/5) is the stronger overall choice for most Machine Learning Development projects. Combined data engineering (ETL) and ML analytics practice with a growing review base. It is best for small and mid-market businesses needing data engineering plus ML analytics as a combined offering.
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
DATAFOREST vs DEPT FAQ
Is DATAFOREST better than DEPT?
DATAFOREST (4.1/5) scores higher overall, but "better" depends on your use case. DATAFOREST is better for small and mid-market businesses needing data engineering plus ML analytics as a combined offering. DEPT is better for large enterprise brands needing ML-driven marketing personalization at global scale.
How do DATAFOREST and DEPT differ in pricing?
DATAFOREST 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: DATAFOREST or DEPT?
DATAFOREST 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 DATAFOREST and DEPT?
DATAFOREST's primary differentiator is: combined data engineering (etl) and ml analytics practice with a growing review base. 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 ($15K vs $75K), and primary industries served (E-commerce, SaaS vs Retail, Media).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.