Tensorway vs nCube: full comparison for 2026
Quick verdict
Tensorway (4.4/5) edges ahead of nCube (3.9/5) overall. Tensorway is the better choice for product teams adding senior AI specialists without vendor lock-in. nCube is the stronger option for companies building a long-term offshore AI team. The right choice depends on your project size, budget, and required tech stack.
Tensorway vs nCube: head-to-head summary
| Criterion | Tensorway | nCube |
|---|---|---|
| Founded | 2019 | 2008 |
| HQ | Alicante, Spain | London, UK |
| Team size | 50–249 | 50–249 staff; large external talent pool (per company) |
| 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 | Builds and runs a client-branded R&D team, including HR and office setup |
| 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 | Monthly per-engineer team pricing; free vetting until candidates are chosen; rates on request |
| Min. engagement | Not disclosed | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, PyTorch, TensorFlow |
| Industries served | Financial services, Software & SaaS, Healthcare & life sciences, Logistics, Manufacturing | Software & SaaS, Media, Financial services, Manufacturing |
Tensorway vs nCube: 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).
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.
Services and capabilities: Tensorway vs nCube
| Capability | Tensorway | nCube |
|---|---|---|
| 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 nCube
| Framework / platform | Tensorway | nCube |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Databricks | N/A | N/A |
| MLflow | ✓ | N/A |
| Kubernetes | ✓ | ✓ |
Pricing comparison: Tensorway vs nCube
| Criterion | Tensorway | nCube |
|---|---|---|
| Minimum engagement | Not disclosed | Not published |
| Engagement models | Full-time dedicated engineers, Part-time fractional experts, Trial period | Dedicated team, Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Tensorway vs nCube
| Dimension | Tensorway | nCube |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Software & SaaS, Healthcare & life sciences | Software & SaaS, Media, Financial services |
| 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 | Setting up a five-person ML team in Eastern Europe, Building a computer-vision team for a media analytics product |
| Typical project type | Full-time dedicated engineers | Dedicated team |
Tensorway vs nCube: 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 |
| 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 |
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 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.
Decision matrix: Tensorway vs nCube
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | nCube |
| You want the supplier to own delivery as well as staffing | Neither offers managed delivery; you will lead the work |
| 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 nCube (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 nCube
| Use case | Tensorway fit | nCube fit | Winner |
|---|---|---|---|
| Adding RAG and evaluation expertise to a SaaS team shipping its first LLM feature | Strong | Limited | Tensorway |
| Bringing GPU inference costs under control for a production model | Strong | Limited | Tensorway |
| Setting up a five-person ML team in Eastern Europe | Limited | Strong | nCube |
| Building a computer-vision team for a media analytics product | Limited | Strong | nCube |
Verdict: Tensorway vs nCube
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.
nCube (3.9/5) is worth a look if you need building a computer-vision team for a media analytics product. If your situation matches that, nCube is a competitive option.
Related comparisons
Tensorway vs nCube FAQ
Is Tensorway better than nCube?
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. nCube's strongest advantage: handles the HR, payroll and legal side of a remote team.
How do Tensorway and nCube 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. nCube uses monthly per-engineer team pricing; free vetting until candidates are chosen; 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 nCube?
Tensorway 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 nCube?
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. nCube's primary differentiator is: builds and runs a client-branded R&D team, including HR and office setup. They also differ in team size (50–249 vs 50–249 staff; large external talent pool (per company)), minimum engagement (Not disclosed vs Not published), and primary industries served (Financial services, Software & SaaS vs Software & SaaS, Media).
Verify all details directly with each company before making a decision.