nCube vs Revelo: full comparison for 2026
Quick verdict
nCube (3.9/5) edges ahead of Revelo (3.8/5) overall. nCube is the better choice for companies building a long-term offshore AI team. Revelo is the stronger option for hiring Latin American developers through a marketplace. The right choice depends on your project size, budget, and required tech stack.
nCube vs Revelo: head-to-head summary
| Criterion | nCube | Revelo |
|---|---|---|
| Founded | 2008 | 2014 |
| HQ | London, UK | São Paulo, Brazil |
| Team size | 50–249 staff; large external talent pool (per company) | 400,000+ developer network (per company) |
| Rating | 3.9 / 5 | 3.8 / 5 |
| Primary differentiator | Builds and runs a client-branded R&D team, including HR and office setup | A very large Latin American pool with payroll and compliance included |
| Pricing model | Monthly per-engineer team pricing; free vetting until candidates are chosen; rates on request | Marketplace placement with monthly billing; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, OpenAI, Hugging Face |
| Industries served | Software & SaaS, Media, Financial services, Manufacturing | Software & SaaS, AI research labs, Financial services |
nCube vs Revelo: 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.
Revelo
Revelo was founded in late 2014 in Brazil (some sources say 2015) and began as a domestic hiring platform called Contratado. It now runs a network of more than 400,000 Latin American developers and handles hiring and payment for U.S. customers. TechCrunch reported that work on foundation models made up 22% of Revelo's revenue in 2024. Revelo is a marketplace, so engineers are matched through its platform rather than employed in a delivery center.
Services and capabilities: nCube vs Revelo
| Capability | nCube | Revelo |
|---|---|---|
| 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 Revelo
| Framework / platform | nCube | Revelo |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | ✓ |
| OpenAI | 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 Revelo
| Criterion | nCube | Revelo |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated team, Full-time dedicated engineers | Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: nCube vs Revelo
| Dimension | nCube | Revelo |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Software & SaaS, Media, Financial services | Software & SaaS, AI research labs, Financial services |
| Best use cases | Setting up a five-person ML team in Eastern Europe, Building a computer-vision team for a media analytics product | Hiring LLM data specialists for a model-training effort, Adding a Brazilian developer to a U.S. SaaS team |
| Typical project type | Dedicated team | Full-time dedicated engineers |
nCube vs Revelo: 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 |
| Revelo | |
|---|---|
| + | Very large pool across Latin America |
| + | Handles hiring, payroll and compliance |
| + | Foundation-model work gives some engineers LLM training experience |
| - | Marketplace matching means quality varies by candidate |
| - | Founding year is reported as both 2014 and 2015 |
| - | Less hands-on management than employer-based firms |
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 Revelo?
A typical fit: hiring LLM data specialists for a model-training effort.
A very large Latin American pool with payroll and compliance included. Minimum engagement is not publicly disclosed. Works best with clients in Software & SaaS, AI research labs, Financial services.
Decision matrix: nCube vs Revelo
| 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 | 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 Revelo (Not published) |
| You need overlap with U.S. working hours | Revelo |
| You need specialist depth in a specific vertical | nCube |
Use case fit: nCube vs Revelo
| Use case | nCube fit | Revelo fit | Winner |
|---|---|---|---|
| Setting up a five-person ML team in Eastern Europe | Strong | Strong | Both equally |
| Building a computer-vision team for a media analytics product | Strong | Limited | nCube |
| Hiring LLM data specialists for a model-training effort | Limited | Strong | Revelo |
| Adding a Brazilian developer to a U.S. SaaS team | Limited | Strong | Revelo |
Verdict: nCube vs Revelo
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.
Revelo (3.8/5) is worth a look if you need adding a Brazilian developer to a U.S. SaaS team. If your situation matches that, Revelo is a competitive option.
Related comparisons
nCube vs Revelo FAQ
Is nCube better than Revelo?
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. Revelo's strongest advantage: very large pool across Latin America.
How do nCube and Revelo differ in pricing?
nCube uses monthly per-engineer team pricing; free vetting until candidates are chosen; rates on request pricing. Revelo uses marketplace placement with monthly billing; 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 Revelo?
Revelo 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 Revelo?
nCube's primary differentiator is: builds and runs a client-branded R&D team, including HR and office setup. Revelo's primary differentiator is: a very large Latin American pool with payroll and compliance included. They also differ in team size (50–249 staff; large external talent pool (per company) vs 400,000+ developer network (per company)), minimum engagement (Not published vs Not published), and primary industries served (Software & SaaS, Media vs Software & SaaS, AI research labs).
Verify all details directly with each company before making a decision.