10Clouds vs EPAM Systems: full comparison for 2026
Quick verdict
10Clouds (4.1/5) edges ahead of EPAM Systems (3.9/5) overall. 10Clouds is the better choice for Banks, insurers and fintechs building AI features. EPAM Systems is the stronger option for global enterprises with large, compliance-heavy AI programs. The right choice depends on your project size, budget, and required tech stack.
10Clouds vs EPAM Systems: head-to-head summary
| Criterion | 10Clouds | EPAM Systems |
|---|---|---|
| Founded | 2009 | 1993 |
| HQ | Warsaw, Poland | Newtown, Pennsylvania, USA |
| Team size | 100–200 | 62,850 |
| Rating | 4.1 / 5 | 3.9 / 5 |
| Primary differentiator | Financial-services AI focus with Claude partner status | Scale, compliance maturity and vendor certifications |
| Pricing model | Time and materials; fixed-term staff augmentation; rates on request | Enterprise time and materials; dedicated teams; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, Claude, OpenAI | Claude, OpenAI, Gemini |
| Industries served | Fintech, Banking, Insurance, SaaS | Fintech, Healthcare, Retail, Manufacturing, Travel |
10Clouds vs EPAM Systems: 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.
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with about 62,850 employees as of June 30, 2026, of whom roughly 56,650 work in delivery. It reports more than 5,700 Claude-certified engineers and set a target of 10,000, along with thousands of OpenAI- and Gemini-certified specialists. The company is targeting $600 million in AI-native services revenue for 2026. Its model is enterprise delivery, so individual staff augmentation usually sits inside a larger program.
Services and capabilities: 10Clouds vs EPAM Systems
| Capability | 10Clouds | EPAM Systems |
|---|---|---|
| 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 EPAM Systems
| Framework / platform | 10Clouds | EPAM Systems |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | ✓ |
| AWS SageMaker | N/A | ✓ |
| Azure ML | N/A | ✓ |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: 10Clouds vs EPAM Systems
| Criterion | 10Clouds | EPAM Systems |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Full-time dedicated engineers, Managed delivery | Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: 10Clouds vs EPAM Systems
| Dimension | 10Clouds | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Fintech, Banking, Insurance | Fintech, Healthcare, Retail |
| Best use cases | Adding an agent developer to a bank's internal automation team, Staffing an LLM engineer for an insurer's claims product | Staffing a multi-team GenAI program at a global bank, Adding certified Claude engineers to an enterprise AI platform |
| Typical project type | Full-time dedicated engineers | Dedicated team |
10Clouds vs EPAM Systems: 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 |
| EPAM Systems | |
|---|---|
| + | Largest bench on this list, with security and compliance processes to match |
| + | Thousands of engineers certified on major model platforms |
| + | Can staff any role an AI program needs |
| - | Built for enterprise programs; a single-engineer request is a poor fit |
| - | Highest overhead and slowest procurement on this list |
| - | Rates not published |
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 EPAM Systems?
A typical fit: staffing a multi-team GenAI program at a global bank.
Scale, compliance maturity and vendor certifications. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Healthcare, Retail, Manufacturing, Travel.
Decision matrix: 10Clouds vs EPAM Systems
| 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 EPAM Systems (Not disclosed) |
| You need specialist depth in a specific vertical | EPAM Systems |
| 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 EPAM Systems
| Use case | 10Clouds fit | EPAM Systems 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 |
| Staffing a multi-team GenAI program at a global bank | Strong | Strong | Both equally |
| Adding certified Claude engineers to an enterprise AI platform | Strong | Strong | Both equally |
Verdict: 10Clouds vs EPAM Systems
10Clouds (4.1/5) is the stronger overall choice for most AI Staffing projects. Financial-services AI focus with Claude partner status.
EPAM Systems (3.9/5) is worth a look if you need adding certified Claude engineers to an enterprise AI platform. If your situation matches that, EPAM Systems is a competitive option.
Related comparisons
10Clouds vs EPAM Systems FAQ
Is 10Clouds better than EPAM Systems?
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. EPAM Systems's strongest advantage: largest bench on this list, with security and compliance processes to match.
How do 10Clouds and EPAM Systems differ in pricing?
10Clouds uses time and materials; fixed-term staff augmentation; rates on request pricing. EPAM Systems uses enterprise time and materials; dedicated teams; 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: 10Clouds or EPAM Systems?
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 EPAM Systems?
10Clouds's primary differentiator is: financial-services AI focus with Claude partner status. EPAM Systems's primary differentiator is: Scale, compliance maturity and vendor certifications. They also differ in team size (100–200 vs 62,850), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Fintech, Banking vs Fintech, Healthcare).
Verify all details directly with each agency before making a decision.