nCube vs STX Next: full comparison for 2026
Quick verdict
nCube (3.9/5) edges ahead of STX Next (3.9/5) overall. nCube is the better choice for companies building a long-term offshore AI team. STX Next is the stronger option for python codebases adding LLM and data engineers. The right choice depends on your project size, budget, and required tech stack.
nCube vs STX Next: head-to-head summary
| Criterion | nCube | STX Next |
|---|---|---|
| Founded | 2008 | 2005 |
| HQ | London, UK | Poznań, Poland |
| Team size | 50–249 staff; large external talent pool (per company) | 250–500 |
| Rating | 3.9 / 5 | 3.9 / 5 |
| Primary differentiator | Builds and runs a client-branded R&D team, including HR and office setup | Python specialization applied to data and AI delivery |
| Pricing model | Monthly per-engineer team pricing; free vetting until candidates are chosen; rates on request | Time and materials; dedicated teams; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Django, FastAPI |
| Industries served | Software & SaaS, Media, Financial services, Manufacturing | Financial services, Software & SaaS, Media, Healthcare & life sciences |
nCube vs STX Next: overview
nCube
nCube was founded in 2008 and is registered in London, with its core R&D office in Kyiv and development offices in Warsaw and São Paulo. It builds dedicated teams and nearshore R&D centers, handling hiring, payroll, legal and HR for the client. The company says it can show first AI candidate profiles within 48 hours and build a team in two to six weeks, drawing on a pool of more than 50,000 AI, ML and data specialists (per company website; independently unverifiable). Named AI clients include Veritone and Fetch.ai.
STX Next
STX Next was founded in Poznań in March 2005 and built its reputation as one of Europe's largest Python software houses. Its 2025 anniversary release cites about 500 staff and more than 1,000 delivered projects, with delivery centers in Poland and Mexico. The firm now presents itself as a data and AI consultancy, and Python's dominance in ML makes its bench a natural fit for model and data work.
Services and capabilities: nCube vs STX Next
| Capability | nCube | STX Next |
|---|---|---|
| LLM / GenAI engineers | ✗ | ✓ |
| MLOps & deployment | ✓ | ✗ |
| Computer vision | ✓ | ✗ |
| Data engineering | ✗ | ✓ |
| AI agent development | ✗ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Risk-free trial period | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
Tech stack comparison: nCube vs STX Next
| Framework / platform | nCube | STX Next |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | N/A | ✓ |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: nCube vs STX Next
| Criterion | nCube | STX Next |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated team, Full-time dedicated engineers | Full-time dedicated engineers, Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: nCube vs STX Next
| Dimension | nCube | STX Next |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Software & SaaS, Media, Financial services | Financial services, Software & SaaS, Media |
| Best use cases | Setting up a five-person ML team in Eastern Europe, Building a computer-vision team for a media analytics product | Adding LLM features to a Django product, Building data jobs in Python for analytics |
| Typical project type | Dedicated team | Full-time dedicated engineers |
nCube vs STX Next: pros and cons
| nCube | |
|---|---|
| + | Handles the HR, payroll and legal side of a remote team |
| + | AI client list includes Veritone and Fetch.ai |
| + | Vetting is free until you pick candidates |
| - | Core team is small relative to the talent pool it advertises |
| - | Two to six weeks is slower than marketplace matching |
| - | Contract notice terms are not published |
| STX Next | |
|---|---|
| + | Python depth fits most AI codebases |
| + | Delivery from both Poland and Mexico |
| + | Long history of extending client teams |
| - | AI positioning is recent compared with its Python history |
| - | Polish rates are above Ukrainian and Latin American options |
| - | Fewer specialist roles such as computer vision |
Who should choose nCube?
A typical fit: setting up a five-person ML team in Eastern Europe.
Builds and runs a client-branded R&D team, including HR and office setup. Minimum engagement is not publicly disclosed. Works best with clients in Software & SaaS, Media, Financial services, Manufacturing.
Who should choose STX Next?
A typical fit: adding LLM features to a Django product.
Python specialization applied to data and AI delivery. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Software & SaaS, Media, Healthcare & life sciences.
Decision matrix: nCube vs STX Next
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; nCube rates higher overall |
| You want the supplier to own delivery as well as staffing | STX Next |
| You need one expert part-time | Neither lists part-time experts; ask about reduced hours |
| You want to test an engineer before signing for months | Neither publishes a trial; ask for a short first term |
| Your budget is at the lower end | Compare: nCube (Not published) vs STX Next (Not published) |
| You need overlap with U.S. working hours | Neither is nearshore; agree overlap hours up front |
| You need specialist depth in a specific vertical | nCube |
Use case fit: nCube vs STX Next
| Use case | nCube fit | STX Next fit | Winner |
|---|---|---|---|
| Setting up a five-person ML team in Eastern Europe | Strong | Limited | nCube |
| Building a computer-vision team for a media analytics product | Strong | Strong | Both equally |
| Adding LLM features to a Django product | Limited | Strong | STX Next |
| Building data jobs in Python for analytics | Strong | Strong | Both equally |
Verdict: nCube vs STX Next
nCube (3.9/5) is the stronger overall choice for most AI Staff Augmentation projects. Builds and runs a client-branded R&D team, including HR and office setup.
STX Next (3.9/5) is worth a look if you need building data jobs in Python for analytics. If your situation matches that, STX Next is a competitive option.
Related comparisons
nCube vs STX Next FAQ
Is nCube better than STX Next?
nCube (3.9/5) scores higher overall, but "better" depends on your use case. nCube's strongest advantage: handles the HR, payroll and legal side of a remote team. STX Next's strongest advantage: python depth fits most AI codebases.
How do nCube and STX Next differ in pricing?
nCube uses monthly per-engineer team pricing; free vetting until candidates are chosen; rates on request pricing. STX Next uses time and materials; dedicated teams; rates on request pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: nCube or STX Next?
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 nCube and STX Next?
nCube's primary differentiator is: builds and runs a client-branded R&D team, including HR and office setup. STX Next's primary differentiator is: python specialization applied to data and AI delivery. They also differ in team size (50–249 staff; large external talent pool (per company) vs 250–500), minimum engagement (Not published vs Not published), and primary industries served (Software & SaaS, Media vs Financial services, Software & SaaS).
Verify all details directly with each company before making a decision.