deepsense.ai vs 10Clouds: full comparison for 2026
Quick verdict
deepsense.ai (4.6/5) edges ahead of 10Clouds (4.1/5) overall. deepsense.ai is the better choice for research-heavy ML problems, senior data scientists. 10Clouds is the stronger option for Banks, insurers and fintechs building AI features. The right choice depends on your project size, budget, and required tech stack.
deepsense.ai vs 10Clouds: head-to-head summary
| Criterion | deepsense.ai | 10Clouds |
|---|---|---|
| Founded | 2014 | 2009 |
| HQ | Warsaw, Poland | Warsaw, Poland |
| Team size | 100–200 | 100–200 |
| Rating | 4.6 / 5 | 4.1 / 5 |
| Primary differentiator | Research-grade data scientists available as embedded team members | Financial-services AI focus with Claude partner status |
| Pricing model | Time and materials; dedicated team; rates on request | Time and materials; fixed-term staff augmentation; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | PyTorch, TensorFlow, Hugging Face | Python, Claude, OpenAI |
| Industries served | Retail and e-commerce, Manufacturing, Healthcare, Fintech | Fintech, Banking, Insurance, SaaS |
deepsense.ai vs 10Clouds: overview
deepsense.ai
deepsense.ai was founded in 2014 in Warsaw, Poland, and keeps a second office in Palo Alto. It is an AI-first company whose work spans generative AI, LLMs, retrieval-augmented generation, MLOps, computer vision and edge AI. Its team-augmentation offer draws on a staff of more than 100 data scientists, data engineers and software engineers, a group that includes Kaggle competition winners and PhD holders (per company website; independently unverifiable). Clutch reviewers describe engineers who integrate with in-house teams and add capacity on strategic projects.
10Clouds
10Clouds was founded in 2009 in Warsaw by Maciej Cielecki and others, and employs somewhere between 100 and 200 people depending on the source. It keeps an in-house product team and also supplies developers or designers to clients for fixed periods, a model it has used with U.S. clients such as Rippling. In 2026 it announced a merger with 10Clouds Financial Institutions, creating an AI unit for banks, insurers and fintechs, and it is a Select partner in the Claude Partner Network services track.
Services and capabilities: deepsense.ai vs 10Clouds
| Capability | deepsense.ai | 10Clouds |
|---|---|---|
| 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 10Clouds
| Framework / platform | deepsense.ai | 10Clouds |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | ✓ |
| Hugging Face | ✓ | N/A |
| OpenAI | N/A | ✓ |
| AWS SageMaker | ✓ | N/A |
| Azure ML | N/A | N/A |
| Databricks | N/A | N/A |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: deepsense.ai vs 10Clouds
| Criterion | deepsense.ai | 10Clouds |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Full-time dedicated engineers, Managed delivery | Full-time dedicated engineers, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: deepsense.ai vs 10Clouds
| Dimension | deepsense.ai | 10Clouds |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Retail and e-commerce, Manufacturing, Healthcare | Fintech, Banking, Insurance |
| Best use cases | Embedding a senior data scientist in a product team with a hard modeling problem, Adding computer-vision engineers for an edge-device deployment | Adding an agent developer to a bank's internal automation team, Staffing an LLM engineer for an insurer's claims product |
| Typical project type | Dedicated team | Full-time dedicated engineers |
deepsense.ai vs 10Clouds: pros and cons
| deepsense.ai | |
|---|---|
| + | Every engineer comes from a company that has done nothing but applied AI since 2014 |
| + | Unusually deep bench for computer vision and edge deployment |
| + | Can supply data engineers alongside data scientists, so the people building features also get clean data |
| + | Polish base gives EU data-protection familiarity and a few hours of overlap with the U.S. East Coast |
| - | Bench of roughly 100 people limits how many concurrent placements it can take |
| - | Senior research talent is priced accordingly; rates are not published |
| - | Better suited to multi-month engagements than one-off fractional help |
| 10Clouds | |
|---|---|
| + | Clear industry focus on regulated financial services |
| + | Claude Partner Network status for teams building on Anthropic models |
| + | Has worked as an embedded team for U.S. scale-ups |
| - | The 2026 merger means leadership and structure are still settling |
| - | Small bench for large placements |
| - | Rates not published |
Who should choose deepsense.ai?
A typical fit: embedding a senior data scientist in a product team with a hard modeling problem.
Research-grade data scientists available as embedded team members. Minimum engagement is not publicly disclosed. Works best with clients in Retail and e-commerce, Manufacturing, Healthcare, Fintech.
Who should choose 10Clouds?
A typical fit: adding an agent developer to a bank's internal automation team.
Financial-services AI focus with Claude partner status. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Banking, Insurance, SaaS.
Decision matrix: deepsense.ai vs 10Clouds
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | deepsense.ai |
| Your budget is at the lower end | Compare: deepsense.ai (Not disclosed) vs 10Clouds (Not disclosed) |
| You need specialist depth in a specific vertical | deepsense.ai |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: deepsense.ai vs 10Clouds
| Use case | deepsense.ai fit | 10Clouds fit | Winner |
|---|---|---|---|
| Embedding a senior data scientist in a product team with a hard modeling problem | Strong | Limited | deepsense.ai |
| Adding computer-vision engineers for an edge-device deployment | Strong | Strong | Both equally |
| Adding an agent developer to a bank's internal automation team | Strong | Strong | Both equally |
| Staffing an LLM engineer for an insurer's claims product | Limited | Strong | 10Clouds |
Verdict: deepsense.ai vs 10Clouds
deepsense.ai (4.6/5) is the stronger overall choice for most AI Staffing projects. Research-grade data scientists available as embedded team members.
10Clouds (4.1/5) is worth a look if you need staffing an LLM engineer for an insurer's claims product. If your situation matches that, 10Clouds is a competitive option.
Related comparisons
deepsense.ai vs 10Clouds FAQ
Is deepsense.ai better than 10Clouds?
deepsense.ai (4.6/5) scores higher overall, but "better" depends on your use case. deepsense.ai's strongest advantage: every engineer comes from a company that has done nothing but applied AI since 2014. 10Clouds's strongest advantage: clear industry focus on regulated financial services.
How do deepsense.ai and 10Clouds differ in pricing?
deepsense.ai uses time and materials; dedicated team; rates on request pricing. 10Clouds uses time and materials; fixed-term staff augmentation; 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 10Clouds?
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 agency before shortlisting.
What are the main differences between deepsense.ai and 10Clouds?
deepsense.ai's primary differentiator is: research-grade data scientists available as embedded team members. 10Clouds's primary differentiator is: financial-services AI focus with Claude partner status. They also differ in team size (100–200 vs 100–200), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Retail and e-commerce, Manufacturing vs Fintech, Banking).
Verify all details directly with each agency before making a decision.