Nearsure vs nCube: full comparison for 2026
Quick verdict
Nearsure (3.9/5) edges ahead of nCube (3.9/5) overall. Nearsure is the better choice for U.S. teams adding Latin American GenAI developers. 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.
Nearsure vs nCube: head-to-head summary
| Criterion | Nearsure | nCube |
|---|---|---|
| Founded | 2018 | 2008 |
| HQ | Montevideo, Uruguay (U.S.-incorporated) | London, UK |
| Team size | 500–850 (sources vary) | 50–249 staff; large external talent pool (per company) |
| Rating | 3.9 / 5 | 3.9 / 5 |
| Primary differentiator | Augmentation-first business model with a growing AI studio | Builds and runs a client-branded R&D team, including HR and office setup |
| Pricing model | Monthly staff augmentation rates; project development; rates on request | Monthly per-engineer team pricing; free vetting until candidates are chosen; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, OpenAI, AWS | Python, PyTorch, TensorFlow |
| Industries served | Software & SaaS, Healthcare & life sciences, Financial services | Software & SaaS, Media, Financial services, Manufacturing |
Nearsure vs nCube: overview
Nearsure
Nearsure started operations in 2018 under co-founder and CEO Giuliana Corbo and is described by Bloomberg as a Uruguayan IT services company, though it is incorporated in the United States. Bloomberg reported a 2024 plan to grow to about 850 staff. Remote staff augmentation for U.S. clients is its core business, and the service list has widened to generative AI, cloud migration and Salesforce work through a Data & AI studio.
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: Nearsure vs nCube
| Capability | Nearsure | 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: Nearsure vs nCube
| Framework / platform | Nearsure | nCube |
|---|---|---|
| PyTorch | N/A | ✓ |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Databricks | N/A | N/A |
| MLflow | N/A | N/A |
| Kubernetes | N/A | ✓ |
Pricing comparison: Nearsure vs nCube
| Criterion | Nearsure | nCube |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team | Dedicated team, Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Nearsure vs nCube
| Dimension | Nearsure | nCube |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Software & SaaS, Healthcare & life sciences, Financial services | Software & SaaS, Media, Financial services |
| Best use cases | Adding a GenAI developer to a U.S. SaaS team, Staffing data engineers for a cloud migration | 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 |
Nearsure vs nCube: pros and cons
| Nearsure | |
|---|---|
| + | Staff augmentation is the main business, so processes are built around it |
| + | Latin American engineers on U.S. hours |
| + | Has been profitable since early in its history, per AméricaEconomía |
| - | AI is a newer studio inside a general staffing company |
| - | Headcount reports vary between 525 and 850 |
| - | HQ location differs between sources |
| 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 Nearsure?
A typical fit: adding a GenAI developer to a U.S. SaaS team.
Augmentation-first business model with a growing AI studio. Minimum engagement is not publicly disclosed. Works best with clients in Software & SaaS, Healthcare & life sciences, Financial services.
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: Nearsure vs nCube
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; Nearsure rates higher overall |
| 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 | 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: Nearsure (Not published) vs nCube (Not published) |
| You need overlap with U.S. working hours | Nearsure |
| You need specialist depth in a specific vertical | nCube |
Use case fit: Nearsure vs nCube
| Use case | Nearsure fit | nCube fit | Winner |
|---|---|---|---|
| Adding a GenAI developer to a U.S. SaaS team | Strong | Limited | Nearsure |
| Staffing data engineers for a cloud migration | Strong | Limited | Nearsure |
| 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: Nearsure vs nCube
Nearsure (3.9/5) is the stronger overall choice for most AI Staff Augmentation projects. Augmentation-first business model with a growing AI studio.
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
Nearsure vs nCube FAQ
Is Nearsure better than nCube?
Nearsure (3.9/5) scores higher overall, but "better" depends on your use case. Nearsure's strongest advantage: staff augmentation is the main business, so processes are built around it. nCube's strongest advantage: handles the HR, payroll and legal side of a remote team.
How do Nearsure and nCube differ in pricing?
Nearsure uses monthly staff augmentation rates; project development; rates 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: Nearsure or nCube?
Nearsure 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 Nearsure and nCube?
Nearsure's primary differentiator is: augmentation-first business model with a growing AI studio. 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 (500–850 (sources vary) vs 50–249 staff; large external talent pool (per company)), minimum engagement (Not published vs Not published), and primary industries served (Software & SaaS, Healthcare & life sciences vs Software & SaaS, Media).
Verify all details directly with each company before making a decision.