deepsense.ai vs nCube: full comparison for 2026
Quick verdict
deepsense.ai (4.3/5) edges ahead of nCube (3.9/5) overall. deepsense.ai is the better choice for research-heavy ML problems, computer vision, edge AI. 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.
deepsense.ai vs nCube: head-to-head summary
| Criterion | deepsense.ai | nCube |
|---|---|---|
| Founded | 2014 | 2008 |
| HQ | Warsaw, Poland | London, UK |
| Team size | 100–200 | 50–249 staff; large external talent pool (per company) |
| Rating | 4.3 / 5 | 3.9 / 5 |
| Primary differentiator | A pure applied-AI firm whose augmented engineers come from a research-grade data-science bench | Builds and runs a client-branded R&D team, including HR and office setup |
| Pricing model | Time and materials for augmented engineers; project contracts; 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 | Manufacturing, Retail & e-commerce, Healthcare & life sciences, Financial services, Software & SaaS | Software & SaaS, Media, Financial services, Manufacturing |
deepsense.ai vs nCube: overview
deepsense.ai
deepsense.ai was founded in 2014, grew out of the AI division of CodiLime, and is headquartered in Warsaw with an office in Palo Alto. Third-party directories put its headcount between roughly 100 and 200 people, and the company says it employs more than 120 AI experts, including Kaggle competition winners and PhD holders. Besides project work in generative AI, MLOps, computer vision and edge AI, it runs a dedicated AI staff augmentation service in which its own engineers extend a client's team.
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: deepsense.ai vs nCube
| Capability | deepsense.ai | 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: deepsense.ai vs nCube
| Framework / platform | deepsense.ai | nCube |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Databricks | N/A | N/A |
| MLflow | ✓ | N/A |
| Kubernetes | ✓ | ✓ |
Pricing comparison: deepsense.ai vs nCube
| Criterion | deepsense.ai | nCube |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Dedicated team, Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: deepsense.ai vs nCube
| Dimension | deepsense.ai | nCube |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Retail & e-commerce, Healthcare & life sciences | Software & SaaS, Media, Financial services |
| Best use cases | Adding a computer-vision specialist to a manufacturing quality team, Bringing research depth into a stalled model-accuracy effort | 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 |
deepsense.ai vs nCube: pros and cons
| deepsense.ai | |
|---|---|
| + | Every engineer it places comes from an AI-only company |
| + | Strong record in computer vision and edge deployment |
| + | Clutch reviewers describe team-augmentation work with strong engineering skills |
| - | A bench of roughly 120 AI staff limits how many people can start at once |
| - | Polish rates are higher than Ukrainian or Latin American alternatives |
| - | Better suited to hard modeling work than to routine LLM integration |
| 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 deepsense.ai?
A typical fit: adding a computer-vision specialist to a manufacturing quality team.
A pure applied-AI firm whose augmented engineers come from a research-grade data-science bench. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Retail & e-commerce, Healthcare & life sciences, Financial services, Software & SaaS.
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: deepsense.ai vs nCube
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Both; deepsense.ai rates higher overall |
| You want the supplier to own delivery as well as staffing | deepsense.ai |
| 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: deepsense.ai (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 | deepsense.ai |
Use case fit: deepsense.ai vs nCube
| Use case | deepsense.ai fit | nCube fit | Winner |
|---|---|---|---|
| Adding a computer-vision specialist to a manufacturing quality team | Strong | Limited | deepsense.ai |
| Bringing research depth into a stalled model-accuracy effort | Strong | Limited | deepsense.ai |
| 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: deepsense.ai vs nCube
deepsense.ai (4.3/5) is the stronger overall choice for most AI Staff Augmentation projects. A pure applied-AI firm whose augmented engineers come from a research-grade data-science bench.
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
deepsense.ai vs nCube FAQ
Is deepsense.ai better than nCube?
deepsense.ai (4.3/5) scores higher overall, but "better" depends on your use case. deepsense.ai's strongest advantage: every engineer it places comes from an AI-only company. nCube's strongest advantage: handles the HR, payroll and legal side of a remote team.
How do deepsense.ai and nCube differ in pricing?
deepsense.ai uses time and materials for augmented engineers; project contracts; 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: deepsense.ai or nCube?
deepsense.ai 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 deepsense.ai and nCube?
deepsense.ai's primary differentiator is: a pure applied-AI firm whose augmented engineers come from a research-grade data-science bench. 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 (100–200 vs 50–249 staff; large external talent pool (per company)), minimum engagement (Not published vs Not published), and primary industries served (Manufacturing, Retail & e-commerce vs Software & SaaS, Media).
Verify all details directly with each company before making a decision.