Tensorway vs STX Next: full comparison for 2026
Quick verdict
Tensorway (4.4/5) edges ahead of STX Next (3.9/5) overall. Tensorway is the better choice for product teams adding senior AI specialists without vendor lock-in. 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.
Tensorway vs STX Next: head-to-head summary
| Criterion | Tensorway | STX Next |
|---|---|---|
| Founded | 2019 | 2005 |
| HQ | Alicante, Spain | Poznań, Poland |
| Team size | 50–249 | 250–500 |
| Rating | 4.4 / 5 | 3.9 / 5 |
| Primary differentiator | Senior AI engineers run the technical screening, and every model and line of code stays in the client's repositories | Python specialization applied to data and AI delivery |
| Pricing model | Monthly rate for full-time dedicated engineers; hourly or weekly billing for part-time fractional experts; two-week trial sprint; rate card on request | Time and materials; dedicated teams; rates on request |
| Min. engagement | Not disclosed | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Django, FastAPI |
| Industries served | Financial services, Software & SaaS, Healthcare & life sciences, Logistics, Manufacturing | Financial services, Software & SaaS, Media, Healthcare & life sciences |
Tensorway vs STX Next: overview
Tensorway
Tensorway, founded in 2019 and based in Alicante, Spain, supplies AI engineers who join a client's own team and work inside its Slack, Jira and version control under its coding standards. The firm has more than 20 years of software engineering practice behind its delivery methods. Its central promise concerns ownership: code, documentation and trained models stay in the client's repositories, and knowledge transfer to in-house staff is part of every engagement (per company website; independently unverifiable). Available roles include LLM engineers, RAG specialists, MLOps architects, computer-vision and NLP engineers, with teams usually starting as a squad of two to five. In one published case, a U.S. trading platform serving more than 100,000 investors reports 40% faster market-data processing and 35% lower operating costs (per company website; independently unverifiable).
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: Tensorway vs STX Next
| Capability | Tensorway | 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: Tensorway vs STX Next
| Framework / platform | Tensorway | STX Next |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | ✓ |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Databricks | N/A | ✓ |
| MLflow | ✓ | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Tensorway vs STX Next
| Criterion | Tensorway | STX Next |
|---|---|---|
| Minimum engagement | Not disclosed | Not published |
| Engagement models | Full-time dedicated engineers, Part-time fractional experts, Trial period | Full-time dedicated engineers, Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Tensorway vs STX Next
| Dimension | Tensorway | STX Next |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Software & SaaS, Healthcare & life sciences | Financial services, Software & SaaS, Media |
| Best use cases | Adding RAG and evaluation expertise to a SaaS team shipping its first LLM feature, Bringing GPU inference costs under control for a production model | Adding LLM features to a Django product, Building data jobs in Python for analytics |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
Tensorway vs STX Next: pros and cons
| Tensorway | |
|---|---|
| + | Candidates pass a code review, a practical task in their specialty and a communication check run by senior AI engineers |
| + | Clients keep all code, documentation and trained models in their own repositories |
| + | First engineer typically starts in one to two weeks and a full squad in three to four (per company website; independently unverifiable) |
| + | Engineers bring GPU and inference cost control, fine-tuning and vector-database experience |
| + | Commitment is monthly and can be adjusted between sprints, with no-cost replacement for a poor fit |
| - | No public rate card, so budgeting starts with a sales call |
| - | Its bench is far smaller than EPAM's or Turing's, which limits how many engineers can start at once |
| - | Only AI and ML roles are offered, so general full-stack or QA staffing has to come from elsewhere |
| 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 Tensorway?
A typical fit: adding RAG and evaluation expertise to a SaaS team shipping its first LLM feature.
Senior AI engineers run the technical screening, and every model and line of code stays in the client's repositories. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Software & SaaS, Healthcare & life sciences, Logistics, 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: Tensorway vs STX Next
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | STX Next |
| You want the supplier to own delivery as well as staffing | STX Next |
| You need one expert part-time | Tensorway |
| You want to test an engineer before signing for months | Tensorway |
| Your budget is at the lower end | Compare: Tensorway (Not disclosed) 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 | Tensorway |
Use case fit: Tensorway vs STX Next
| Use case | Tensorway fit | STX Next fit | Winner |
|---|---|---|---|
| Adding RAG and evaluation expertise to a SaaS team shipping its first LLM feature | Strong | Strong | Both equally |
| Bringing GPU inference costs under control for a production model | Strong | Limited | Tensorway |
| Adding LLM features to a Django product | Strong | Strong | Both equally |
| Building data jobs in Python for analytics | Limited | Strong | STX Next |
Verdict: Tensorway vs STX Next
Tensorway (4.4/5) is the stronger overall choice for most AI Staff Augmentation projects. Senior AI engineers run the technical screening, and every model and line of code stays in the client's repositories.
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
Tensorway vs STX Next FAQ
Is Tensorway better than STX Next?
Tensorway (4.4/5) scores higher overall, but "better" depends on your use case. Tensorway's strongest advantage: candidates pass a code review, a practical task in their specialty and a communication check run by senior AI engineers. STX Next's strongest advantage: python depth fits most AI codebases.
How do Tensorway and STX Next differ in pricing?
Tensorway uses monthly rate for full-time dedicated engineers; hourly or weekly billing for part-time fractional experts; two-week trial sprint; rate card 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: Tensorway 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 Tensorway and STX Next?
Tensorway's primary differentiator is: senior AI engineers run the technical screening, and every model and line of code stays in the client's repositories. STX Next's primary differentiator is: python specialization applied to data and AI delivery. They also differ in team size (50–249 vs 250–500), minimum engagement (Not disclosed vs Not published), and primary industries served (Financial services, Software & SaaS vs Financial services, Software & SaaS).
Verify all details directly with each company before making a decision.