Capgemini vs 10Clouds: full comparison for 2026
Quick verdict
Capgemini (3.9/5) edges ahead of 10Clouds (3.8/5) overall. Capgemini is the better choice for european multinationals pairing AI with outsourcing. 10Clouds is the stronger option for smaller banks and lenders adding AI. The right choice depends on how far your pilot has got, your budget, and who will run the system after launch.
Capgemini vs 10Clouds: head-to-head summary
| Criterion | Capgemini | 10Clouds |
|---|---|---|
| Founded | 1967 | 2009 |
| HQ | Paris, France | Warsaw, Poland |
| Team size | 340,000+ | ~200 |
| Rating | 3.9 / 5 | 3.8 / 5 |
| Primary differentiator | Production AI bundled with outsourced business operations after the WNS deal | A dedicated financial-institutions AI unit with its own AIConsole platform |
| Pricing model | Program fees and managed services; rates not published | Project and team pricing; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Azure, AWS, Google Cloud | AWS, Azure, OpenAI |
| Industries served | Financial services, Manufacturing, Public sector, Retail, Energy & utilities | Banking, Fintech, Insurance, Healthcare |
Capgemini vs 10Clouds: overview
Capgemini
Capgemini was founded in Grenoble in 1967, is headquartered in Paris, and employs more than 340,000 people. In October 2025 it completed its acquisition of WNS, adding business process services that it plans to run with AI agents. Production AI here is usually part of a large program that includes process redesign and outsourced operations. It's a strong fit for European multinationals and a poor one for a single pilot.
10Clouds
10Clouds was founded in Warsaw in 2009 and has around 200 people across Warsaw, Wrocław, and Poznań. It set up a separate company, 10Clouds Financial Institutions, to build AI operations for banks and lenders, and it has its own AI platform called AIConsole. Most of its history is custom software and blockchain work, with AI the newest focus.
Services and capabilities: Capgemini vs 10Clouds
| Capability | Capgemini | 10Clouds |
|---|---|---|
| LLM gateway & model routing | ✗ | ✗ |
| Regression tests on prompt and model changes | ✗ | ✗ |
| Canary or staged rollout | ✗ | ✗ |
| Hallucination and drift monitoring | ✗ | ✗ |
| MLOps & model serving | ✗ | ✗ |
| Fixed-scope pilot | ✗ | ✗ |
| Managed run after launch | ✓ | ✗ |
Tech stack comparison: Capgemini vs 10Clouds
| Framework / platform | Capgemini | 10Clouds |
|---|---|---|
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | N/A |
| Kubernetes | N/A | N/A |
| MLflow | N/A | N/A |
| Databricks | N/A | N/A |
| Amazon Bedrock | N/A | N/A |
| Vertex AI | N/A | N/A |
| LangChain | N/A | N/A |
| Snowflake | N/A | N/A |
Pricing comparison: Capgemini vs 10Clouds
| Criterion | Capgemini | 10Clouds |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Time & materials, Managed services | Fixed-scope project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Capgemini vs 10Clouds
| Dimension | Capgemini | 10Clouds |
|---|---|---|
| Best company size | Enterprise | Startup to mid-market |
| Best industries | Financial services, Manufacturing, Public sector | Banking, Fintech, Insurance |
| Best use cases | AI agents inside outsourced finance and customer operations., Production AI on SAP for European manufacturers. | AI-assisted operations at a smaller bank or lender., Customer service assistants in fintech. |
| Typical project type | Time & materials | Fixed-scope project |
Capgemini vs 10Clouds: pros and cons
| Capgemini | |
|---|---|
| + | Can run AI-assisted business operations as a managed service |
| + | Wide European footprint |
| + | Deep SAP and enterprise platform experience |
| - | WNS integration (completed October 2025) is still in progress |
| - | Not set up for small, fast pilots |
| - | Rates not published |
| 10Clouds | |
|---|---|
| + | Dedicated unit for banks and lenders |
| + | Own AI platform to start from |
| + | Nearshore rates |
| - | AI is a recent focus for the firm |
| - | Small team for large programs |
| - | Little public detail on monitoring or rollout practice |
Who should choose Capgemini?
A typical fit: AI agents inside outsourced finance and customer operations.
Production AI bundled with outsourced business operations after the WNS deal. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Manufacturing, Public sector, Retail, Energy & utilities.
Who should choose 10Clouds?
A typical fit: AI-assisted operations at a smaller bank or lender.
A dedicated financial-institutions AI unit with its own AIConsole platform. Minimum engagement is not publicly disclosed. Works best with clients in Banking, Fintech, Insurance, Healthcare.
Decision matrix: Capgemini vs 10Clouds
| Your situation | Recommended choice |
|---|---|
| Your pilot works but nobody trusts it in production yet | Ask each for its release and rollback process |
| Model API spend is growing faster than usage | Neither lists a gateway; ask how cost per feature is tracked |
| You need someone to watch quality after launch | Capgemini |
| You run classic ML models that need serving and retraining | Neither lists MLOps; check each profile |
| You want a priced pilot before a full rollout | Neither advertises one; ask for a scoped quote |
| Your budget is at the lower end | Compare: Capgemini (Not disclosed) vs 10Clouds (Not disclosed) |
| You need a large team for many products at once | Capgemini |
Use case fit: Capgemini vs 10Clouds
| Use case | Capgemini fit | 10Clouds fit | Winner |
|---|---|---|---|
| AI agents inside outsourced finance and customer operations. | Strong | Limited | Capgemini |
| Production AI on SAP for European manufacturers. | Strong | Limited | Capgemini |
| AI-assisted operations at a smaller bank or lender. | Limited | Strong | 10Clouds |
| Customer service assistants in fintech. | Limited | Strong | 10Clouds |
Verdict: Capgemini vs 10Clouds
Capgemini (3.9/5) is the stronger overall choice for most AI Implementation projects. Production AI bundled with outsourced business operations after the WNS deal.
10Clouds (3.8/5) is worth a look if you need customer service assistants in fintech. If your situation matches that, 10Clouds is a competitive option.
Related comparisons
Capgemini vs 10Clouds FAQ
Is Capgemini better than 10Clouds?
Capgemini (3.9/5) scores higher overall, but "better" depends on your use case. Capgemini's strongest advantage: can run AI-assisted business operations as a managed service. 10Clouds's strongest advantage: dedicated unit for banks and lenders.
How do Capgemini and 10Clouds differ in pricing?
Capgemini's pricing: program fees and managed services; rates not published. 10Clouds's pricing: project and team pricing; rates on request. Any hourly bands shown come from Clutch, not a published rate card, so a scoping call is still needed for a project quote.
Which is better for enterprise: Capgemini or 10Clouds?
Capgemini is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each provider before shortlisting.
What are the main differences between Capgemini and 10Clouds?
Capgemini's primary differentiator is: production AI bundled with outsourced business operations after the WNS deal. 10Clouds's primary differentiator is: a dedicated financial-institutions AI unit with its own AIConsole platform. They also differ in team size (340,000+ vs ~200), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Financial services, Manufacturing vs Banking, Fintech).
Verify all details directly with each provider before making a decision.