10Clouds vs ScienceSoft: full comparison for 2026
Quick verdict
10Clouds (4.1/5) edges ahead of ScienceSoft (4.0/5) overall. 10Clouds is the better choice for Banks, insurers and fintechs building AI features. ScienceSoft is the stronger option for regulated industries hiring experienced data scientists. The right choice depends on your project size, budget, and required tech stack.
10Clouds vs ScienceSoft: head-to-head summary
| Criterion | 10Clouds | ScienceSoft |
|---|---|---|
| Founded | 2009 | 1989 |
| HQ | Warsaw, Poland | McKinney, Texas, USA |
| Team size | 100–200 | 750+ |
| Rating | 4.1 / 5 | 4.0 / 5 |
| Primary differentiator | Financial-services AI focus with Claude partner status | Senior data scientists with a published hiring timeline |
| Pricing model | Time and materials; fixed-term staff augmentation; rates on request | Time and materials; rates sent with CVs |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, Claude, OpenAI | Python, R, Azure ML |
| Industries served | Fintech, Banking, Insurance, SaaS | Healthcare, Manufacturing, Fintech, Retail |
10Clouds vs ScienceSoft: overview
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.
ScienceSoft
ScienceSoft dates its IT work to 1989 and is headquartered in McKinney, Texas, with representative offices in the UAE, Saudi Arabia, Europe and Mexico. Its staff-augmentation pool covers more than 750 professionals, including data scientists with 7–20 years of experience. The company says it sends CVs with rates within 24 hours, arranges interviews in two to four days and has people starting within one to two weeks (per company website; independently unverifiable).
Services and capabilities: 10Clouds vs ScienceSoft
| Capability | 10Clouds | ScienceSoft |
|---|---|---|
| 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: 10Clouds vs ScienceSoft
| Framework / platform | 10Clouds | ScienceSoft |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS SageMaker | N/A | ✓ |
| Azure ML | N/A | ✓ |
| Databricks | N/A | N/A |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: 10Clouds vs ScienceSoft
| Criterion | 10Clouds | ScienceSoft |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Full-time dedicated engineers, Managed delivery | Full-time dedicated engineers, Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: 10Clouds vs ScienceSoft
| Dimension | 10Clouds | ScienceSoft |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Fintech, Banking, Insurance | Healthcare, Manufacturing, Fintech |
| Best use cases | Adding an agent developer to a bank's internal automation team, Staffing an LLM engineer for an insurer's claims product | Adding a senior data scientist to a healthcare analytics team, Staffing a manufacturing predictive-maintenance project |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
10Clouds vs ScienceSoft: pros and cons
| 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 |
| ScienceSoft | |
|---|---|
| + | Rates arrive with the CVs, before any sales calls |
| + | Long history in healthcare and manufacturing IT |
| + | Experienced data scientists rather than junior ML hires |
| - | AI is one of many service lines |
| - | Smaller bench than the large nearshore firms |
| - | Headcount figures differ between the company's own pages |
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.
Who should choose ScienceSoft?
A typical fit: adding a senior data scientist to a healthcare analytics team.
Senior data scientists with a published hiring timeline. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Manufacturing, Fintech, Retail.
Decision matrix: 10Clouds vs ScienceSoft
| 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 | 10Clouds |
| Your budget is at the lower end | Compare: 10Clouds (Not disclosed) vs ScienceSoft (Not disclosed) |
| You need specialist depth in a specific vertical | 10Clouds |
| 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: 10Clouds vs ScienceSoft
| Use case | 10Clouds fit | ScienceSoft fit | Winner |
|---|---|---|---|
| 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 | Strong | Strong | Both equally |
| Adding a senior data scientist to a healthcare analytics team | Strong | Strong | Both equally |
| Staffing a manufacturing predictive-maintenance project | Strong | Strong | Both equally |
Verdict: 10Clouds vs ScienceSoft
10Clouds (4.1/5) is the stronger overall choice for most AI Staffing projects. Financial-services AI focus with Claude partner status.
ScienceSoft (4.0/5) is worth a look if you need staffing a manufacturing predictive-maintenance project. If your situation matches that, ScienceSoft is a competitive option.
Related comparisons
10Clouds vs ScienceSoft FAQ
Is 10Clouds better than ScienceSoft?
10Clouds (4.1/5) scores higher overall, but "better" depends on your use case. 10Clouds's strongest advantage: clear industry focus on regulated financial services. ScienceSoft's strongest advantage: rates arrive with the CVs, before any sales calls.
How do 10Clouds and ScienceSoft differ in pricing?
10Clouds uses time and materials; fixed-term staff augmentation; rates on request pricing. ScienceSoft uses time and materials; rates sent with cvs pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: 10Clouds or ScienceSoft?
10Clouds 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 10Clouds and ScienceSoft?
10Clouds's primary differentiator is: financial-services AI focus with Claude partner status. ScienceSoft's primary differentiator is: senior data scientists with a published hiring timeline. They also differ in team size (100–200 vs 750+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Fintech, Banking vs Healthcare, Manufacturing).
Verify all details directly with each agency before making a decision.