InData Labs vs nCube: full comparison for 2026
Quick verdict
InData Labs (4.1/5) edges ahead of nCube (3.9/5) overall. InData Labs is the better choice for mid-sized companies adding data scientists to product teams. 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.
InData Labs vs nCube: head-to-head summary
| Criterion | InData Labs | nCube |
|---|---|---|
| Founded | 2014 | 2008 |
| HQ | Nicosia, Cyprus | London, UK |
| Team size | 50–249 | 50–249 staff; large external talent pool (per company) |
| Rating | 4.1 / 5 | 3.9 / 5 |
| Primary differentiator | A data-science-only firm small enough that senior staff stay involved | Builds and runs a client-branded R&D team, including HR and office setup |
| Pricing model | Time and materials; dedicated engineers; 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, PyTorch, TensorFlow | Python, PyTorch, TensorFlow |
| Industries served | Retail & e-commerce, Healthcare & life sciences, Financial services, Media | Software & SaaS, Media, Financial services, Manufacturing |
InData Labs vs nCube: overview
InData Labs
InData Labs was founded in 2014 and is headquartered in Nicosia, Cyprus, with additional locations including Vilnius and Miami. Most directories put its headcount below 250 people. The company works only on data science and AI, covering predictive analytics, NLP, computer vision and generative AI, and it supplies engineers to client teams as well as delivering projects.
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: InData Labs vs nCube
| Capability | InData Labs | 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: InData Labs vs nCube
| Framework / platform | InData Labs | nCube |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | N/A | 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: InData Labs vs nCube
| Criterion | InData Labs | nCube |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Managed delivery | Dedicated team, Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs nCube
| Dimension | InData Labs | nCube |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Retail & e-commerce, Healthcare & life sciences, Financial services | Software & SaaS, Media, Financial services |
| Best use cases | Adding a computer-vision engineer to a retail analytics team, Building churn and demand models with in-house analysts | 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 |
InData Labs vs nCube: pros and cons
| InData Labs | |
|---|---|
| + | Data science and AI are its only line of work |
| + | Experience across vision, language and predictive models |
| + | Clients deal with a small firm where senior staff stay close to the work |
| - | Headcount estimates vary widely, so confirm bench depth for your role |
| - | Limited capacity for large multi-team programs |
| - | Less visible LLM-agent work than some newer specialists |
| 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 InData Labs?
A typical fit: adding a computer-vision engineer to a retail analytics team.
A data-science-only firm small enough that senior staff stay involved. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Healthcare & life sciences, Financial services, Media.
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: InData Labs 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 | InData Labs |
| 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: InData Labs (Not published) 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 | InData Labs |
Use case fit: InData Labs vs nCube
| Use case | InData Labs fit | nCube fit | Winner |
|---|---|---|---|
| Adding a computer-vision engineer to a retail analytics team | Strong | Limited | InData Labs |
| Building churn and demand models with in-house analysts | Strong | Strong | Both equally |
| Setting up a five-person ML team in Eastern Europe | Limited | Strong | nCube |
| Building a computer-vision team for a media analytics product | Strong | Strong | Both equally |
Verdict: InData Labs vs nCube
InData Labs (4.1/5) is the stronger overall choice for most AI Staff Augmentation projects. A data-science-only firm small enough that senior staff stay involved.
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
InData Labs vs nCube FAQ
Is InData Labs better than nCube?
InData Labs (4.1/5) scores higher overall, but "better" depends on your use case. InData Labs's strongest advantage: data science and AI are its only line of work. nCube's strongest advantage: handles the HR, payroll and legal side of a remote team.
How do InData Labs and nCube differ in pricing?
InData Labs uses time and materials; dedicated engineers; 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: InData Labs or nCube?
InData Labs 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 InData Labs and nCube?
InData Labs's primary differentiator is: a data-science-only firm small enough that senior staff stay involved. 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 published vs Not published), and primary industries served (Retail & e-commerce, Healthcare & life sciences vs Software & SaaS, Media).
Verify all details directly with each company before making a decision.