STX Next vs DATAFOREST: full comparison for 2026
Last updated: July 2026
Quick verdict
STX Next (4.3/5) edges ahead of DATAFOREST (4.1/5) overall. STX Next is the better choice for companies needing ML development paired with deep, large-scale Python software engineering capacity. DATAFOREST is the stronger option for small and mid-market businesses needing data engineering plus ML analytics as a combined offering. The right choice depends on your project size, budget, and required tech stack.
STX Next vs DATAFOREST: head-to-head summary
| Criterion | STX Next | DATAFOREST |
|---|---|---|
| Founded | 2005 | 2018 |
| HQ | Poznan, Poland | Kyiv, Ukraine |
| Team size | 201–500 | 51–200 |
| Rating | 4.3 / 5 | 4.1 / 5 |
| Best for | Companies needing ML development paired with deep, large-scale Python software engineering capacity | Small and mid-market businesses needing data engineering plus ML analytics as a combined offering |
| Pricing model | Dedicated team, staff augmentation, fixed project | Fixed project, dedicated team |
| Min. engagement | $25K | $15K |
| Primary tech stack | Python, Django, FastAPI | Python, Airflow, AWS |
| Industries served | SaaS, Fintech, Healthcare, E-commerce, Enterprise | E-commerce, SaaS, Fintech, Healthcare |
STX Next vs DATAFOREST: 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.
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.
Services and capabilities: STX Next vs DATAFOREST
| Capability | STX Next | DATAFOREST |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✓ | ✗ |
Tech stack comparison: STX Next vs DATAFOREST
| Framework / platform | STX Next | DATAFOREST |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: STX Next vs DATAFOREST
| Criterion | STX Next | DATAFOREST |
|---|---|---|
| Minimum engagement | $25K | $15K |
| Engagement models | Dedicated team, Staff augmentation, Fixed project | Fixed project, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: STX Next vs DATAFOREST
| Dimension | STX Next | DATAFOREST |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, Healthcare | E-commerce, SaaS, Fintech |
| Best use cases | ML feature development inside a larger Python software platform, Scaling an engineering team with dedicated Python and ML staff | Building ETL pipelines feeding a downstream ML model, Predictive analytics for e-commerce customer behavior |
| Typical project type | Dedicated team | Fixed project |
STX Next vs DATAFOREST: 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 |
| 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 |
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 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.
Decision matrix: STX Next vs DATAFOREST
| 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 | DATAFOREST |
| 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 DATAFOREST
| Use case | STX Next fit | DATAFOREST 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 |
| Building ETL pipelines feeding a downstream ML model | Limited | Strong | DATAFOREST |
| Predictive analytics for e-commerce customer behavior | Limited | Strong | DATAFOREST |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Strong | Limited | STX Next |
Verdict: STX Next vs DATAFOREST
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.
DATAFOREST (4.1/5) is the better choice when small and mid-market businesses needing data engineering plus ML analytics as a combined offering. If your situation matches those criteria, DATAFOREST is a competitive option.
Related comparisons
STX Next vs DATAFOREST FAQ
Is STX Next better than DATAFOREST?
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. DATAFOREST is better for small and mid-market businesses needing data engineering plus ML analytics as a combined offering.
How do STX Next and DATAFOREST differ in pricing?
STX Next uses dedicated team, staff augmentation, fixed project pricing with a minimum engagement of $25K. DATAFOREST uses fixed project, dedicated team pricing with a minimum engagement of $15K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: STX Next or DATAFOREST?
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 DATAFOREST?
STX Next's primary differentiator is: one of europe's largest dedicated python engineering companies, with ml and data practices built on that scale. DATAFOREST's primary differentiator is: combined data engineering (etl) and ml analytics practice with a growing review base. They also differ in team size (201–500 vs 51–200), minimum engagement ($25K vs $15K), and primary industries served (SaaS, Fintech vs E-commerce, SaaS).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.