STX Next vs DEPT: full comparison for 2026
Last updated: July 2026
Quick verdict
STX Next (4.3/5) edges ahead of DEPT (4.0/5) overall. STX Next is the better choice for companies needing ML development paired with deep, large-scale Python software engineering capacity. 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.
STX Next vs DEPT: head-to-head summary
| Criterion | STX Next | DEPT |
|---|---|---|
| Founded | 2005 | 2015 |
| HQ | Poznan, Poland | Amsterdam, Netherlands |
| Team size | 201–500 | 1000+ |
| Rating | 4.3 / 5 | 4.0 / 5 |
| Best for | Companies needing ML development paired with deep, large-scale Python software engineering capacity | Large enterprise brands needing ML-driven marketing personalization at global scale |
| Pricing model | Dedicated team, staff augmentation, fixed project | Retainer, dedicated team |
| Min. engagement | $25K | $75K |
| Primary tech stack | Python, Django, FastAPI | Python, GCP, AWS |
| Industries served | SaaS, Fintech, Healthcare, E-commerce, Enterprise | Retail, Media, Enterprise, E-commerce |
STX Next vs DEPT: overview
STX Next
STX Next, founded in March 2005 in Poznan, Poland, grew from an 8-person startup into a nearly 500-person Python engineering firm with delivery centers across Poland and Mexico. Known primarily as one of Europe's largest dedicated Python engineering companies, STX Next has built out AI/ML and data engineering practices on top of its deep Python bench, making it a strong generalist option for ML projects that also require broader software engineering.
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: STX Next vs DEPT
| Capability | STX Next | DEPT |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✗ | ✓ |
| MLOps | ✗ | ✗ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✓ | ✗ |
Tech stack comparison: STX Next vs DEPT
| Framework / platform | STX Next | DEPT |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: STX Next vs DEPT
| Criterion | STX Next | DEPT |
|---|---|---|
| Minimum engagement | $25K | $75K |
| Engagement models | Dedicated team, Staff augmentation, Fixed project | Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: STX Next vs DEPT
| Dimension | STX Next | DEPT |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, Healthcare | Retail, Media, Enterprise |
| Best use cases | ML feature development inside a larger Python software platform, Scaling an engineering team with dedicated Python and ML staff | ML-driven marketing personalization at global brand scale, Enterprise data engineering supporting a large media or retail platform |
| Typical project type | Dedicated team | Retainer |
STX Next vs DEPT: pros and cons
| STX Next | |
|---|---|
| + | Two decades of operating history since founding in 2005 with proven scale of roughly 500 engineers |
| + | Deep Python engineering bench supports complex ML and software integration projects |
| + | Multiple delivery centers across Poland and Mexico for coverage flexibility |
| + | Established staff augmentation model for teams needing to scale quickly |
| - | ML and AI is one practice among several rather than the firm's sole focus |
| - | Larger organizational size may mean less founder-level attention than boutique specialists |
| - | Best fit skews toward Python-centric stacks rather than polyglot ML environments |
| 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 STX Next?
STX Next is the right choice for companies needing ML development paired with deep, large-scale Python software engineering capacity.
One of Europe's largest dedicated Python engineering companies, with ML and data practices built on that scale. Minimum engagement starts at $25K. Works best with clients in SaaS, Fintech, Healthcare, E-commerce, Enterprise.
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: STX Next vs DEPT
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | STX Next |
| You need a large dedicated team for an ongoing programme | STX Next |
| Your budget is at the lower end | STX Next |
| You need specialist depth in a specific vertical | STX Next |
| You need staff augmentation or team extension | STX Next |
| You need consulting before committing to a build | STX Next |
Use case fit: STX Next vs DEPT
| Use case | STX Next fit | DEPT fit | Winner |
|---|---|---|---|
| ML feature development inside a larger Python software platform | Strong | Strong | Both equally |
| Scaling an engineering team with dedicated Python and ML staff | Strong | Limited | STX Next |
| 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 | Strong | Limited | STX Next |
Verdict: STX Next vs DEPT
STX Next (4.3/5) is the stronger overall choice for most Machine Learning Development projects. One of Europe's largest dedicated Python engineering companies, with ML and data practices built on that scale. It is best for companies needing ML development paired with deep, large-scale Python software engineering capacity.
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
STX Next vs DEPT FAQ
Is STX Next better than DEPT?
STX Next (4.3/5) scores higher overall, but "better" depends on your use case. STX Next is better for companies needing ML development paired with deep, large-scale Python software engineering capacity. DEPT is better for large enterprise brands needing ML-driven marketing personalization at global scale.
How do STX Next and DEPT differ in pricing?
STX Next uses dedicated team, staff augmentation, 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: STX Next or DEPT?
STX Next 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 STX Next and DEPT?
STX Next's primary differentiator is: one of europe's largest dedicated python engineering companies, with ml and data practices built on that scale. 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 (201–500 vs 1000+), minimum engagement ($25K vs $75K), and primary industries served (SaaS, Fintech vs Retail, Media).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.