nCube vs Xenoss: full comparison for 2026
Quick verdict
nCube (3.9/5) edges ahead of Xenoss (3.8/5) overall. nCube is the better choice for companies building a long-term offshore AI team. Xenoss is the stronger option for AdTech and MarTech firms needing real-time data plus AI. The right choice depends on your project size, budget, and required tech stack.
nCube vs Xenoss: head-to-head summary
| Criterion | nCube | Xenoss |
|---|---|---|
| Founded | 2008 | 2013 |
| HQ | London, UK | New York, New York, USA |
| Team size | 50–249 staff; large external talent pool (per company) | 100–200 |
| Rating | 3.9 / 5 | 3.8 / 5 |
| Primary differentiator | Builds and runs a client-branded R&D team, including HR and office setup | Real-time, high-load data engineering from AdTech roots |
| Pricing model | Monthly per-engineer team pricing; free vetting until candidates are chosen; rates on request | Team extension and project pricing; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, Kafka, Spark |
| Industries served | Software & SaaS, Media, Financial services, Manufacturing | Media, Retail & e-commerce, Software & SaaS |
nCube vs Xenoss: 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.
Xenoss
Xenoss was founded in 2013 by AdTech veterans and lists its headquarters in New York, with CEO Dmitry Sverdlik. Directories put headcount between 100 and 200. It specializes in AI and data engineering, including AI agents, real-time data systems and LLM knowledge bases, and favors small senior teams. Team extension appears in its history, but it does not run a dedicated staff augmentation offer.
Services and capabilities: nCube vs Xenoss
| Capability | nCube | Xenoss |
|---|---|---|
| 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 Xenoss
| Framework / platform | nCube | Xenoss |
|---|---|---|
| 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 | N/A |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: nCube vs Xenoss
| Criterion | nCube | Xenoss |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated team, 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 Xenoss
| Dimension | nCube | Xenoss |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Software & SaaS, Media, Financial services | Media, Retail & e-commerce, Software & SaaS |
| Best use cases | Setting up a five-person ML team in Eastern Europe, Building a computer-vision team for a media analytics product | Adding real-time feature engineering for a bidding model, Building an LLM knowledge base on marketing data |
| Typical project type | Dedicated team | Dedicated team |
nCube vs Xenoss: 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 |
| Xenoss | |
|---|---|
| + | High-load, real-time data experience |
| + | Small senior teams with low management overhead |
| + | Builds agents and knowledge bases on its own data work |
| - | No dedicated staff augmentation page |
| - | Industry focus is narrow outside AdTech and MarTech |
| - | Headcount data varies |
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 Xenoss?
A typical fit: adding real-time feature engineering for a bidding model.
Real-time, high-load data engineering from AdTech roots. Minimum engagement is not publicly disclosed. Works best with clients in Media, Retail & e-commerce, Software & SaaS.
Decision matrix: nCube vs Xenoss
| 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 | Xenoss |
| 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 Xenoss (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 Xenoss
| Use case | nCube fit | Xenoss 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 real-time feature engineering for a bidding model | Limited | Strong | Xenoss |
| Building an LLM knowledge base on marketing data | Strong | Strong | Both equally |
Verdict: nCube vs Xenoss
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.
Xenoss (3.8/5) is worth a look if you need building an LLM knowledge base on marketing data. If your situation matches that, Xenoss is a competitive option.
Related comparisons
nCube vs Xenoss FAQ
Is nCube better than Xenoss?
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. Xenoss's strongest advantage: High-load, real-time data experience.
How do nCube and Xenoss differ in pricing?
nCube uses monthly per-engineer team pricing; free vetting until candidates are chosen; rates on request pricing. Xenoss uses team extension and project pricing; 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 Xenoss?
Xenoss 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 Xenoss?
nCube's primary differentiator is: builds and runs a client-branded R&D team, including HR and office setup. Xenoss's primary differentiator is: Real-time, high-load data engineering from AdTech roots. They also differ in team size (50–249 staff; large external talent pool (per company) vs 100–200), minimum engagement (Not published vs Not published), and primary industries served (Software & SaaS, Media vs Media, Retail & e-commerce).
Verify all details directly with each company before making a decision.