Comparison for decision-makers evaluating AI initiatives with IBM (Watson), Oracle AI, Capgemini, NTT Data Everis, Indra or Sopra Steria. Real model differences, advantages and when each makes sense.
Each row is a real measurable criterion. We mark with ★ where each option wins. When enterprise tech consultancies wins, we say so.
| Criterion | Datalvar AI | enterprise tech consultancies |
|---|---|---|
| AI models used | ★Frontier (Claude, GPT, Gemini) + open-source (Llama, Mistral) per case | Usually tied to proprietary stack (Watson, Oracle AI) |
| Vendor lock-in | ★None, portable architecture | High — code and data tied to platform |
| Platform license | ★N/A | €40-200k/year typical |
| Time to production | 2-6 months | 12-24 months |
| First pilot cost | €15-80k | €200-800k (project + Y1 license) |
| Specialisation | 100 % applied AI | Broad (ERP, cloud, cybersec, AI, mainframes) |
| Cloud choice | AWS, Azure, GCP, on-premise · per requirements | Usually provider or preferred partner cloud |
| Update to new models | Weeks — architecture ready for change | Annual platform cycles |
| Large-account references | Building | ★Decades |
| 24/7 support with formal SLAs | On demand | ★Standard |
| Other IT area coverage | Only AI | ★Infra, apps, integration, security |
We publish this list so you decide with criteria. If we don\'t fit, we save you and us time.
IBM proposed Watson Assistant + WatsonX ($$$ license + custom integration, 18 months). Us: Claude Opus 5 via Bedrock (Anthropic AWS EU residency) + integration with banking core. 4 months, stable operating cost, portable to another model if a better one appears.
Oracle Consulting proposed Oracle AI integrated in Fusion ERP (complete package with Y1 license). Us: agent over existing Oracle via API, with OpenAI Enterprise. Same operational result with 60% less annual cost.
NTT Data proposed full industry 4.0 platform (18 months, €800k). Us: specific vision model trained with client's parts + integration with SCADA. 12 weeks, €55k.
Watson isn't a single LLM — it's a platform including classic components (NLU, discovery, traditional machine learning) plus access to some generative models (WatsonX incorporates Llama and IBM Granite proprietary models). The key difference is Watson gives you a platform layer with associated license price, and locks you into its ecosystem.
We build directly on frontier models (Claude Opus 5, GPT-5, Gemini 2.5) or open-source (Llama 3.3, Mistral) per specific case. Agent logic, RAG and orchestration is ours or standard open-source (LangChain, n8n). All portable, no platform license.
When a better model appears (Claude Opus 6, GPT-6), we can migrate in weeks. With Watson you depend on IBM's pace incorporating the new model into their platform.
Yes, and it's increasingly frequent. Many large accounts keep their master contract with the big provider for infra and core apps, and hire specialised boutiques for concrete AI initiatives where speed and specific expertise are critical.
The advantage for you: keep existing corporate relationships with IBM/Oracle/NTT and gain speed + expertise in AI execution. Trade-off: manage two providers in parallel, which requires clear sponsor to avoid friction.
We can work as technical partner under the big provider's umbrella when politically necessary, or directly with you when the initiative allows.
Current frontier models (Claude Opus 5, GPT-5, Gemini 2.5) clearly outperform Watson Assistant and IBM Granite proprietary models in generative quality, multi-step reasoning and language comprehension. Public benchmarks and our internal evals confirm it.
Watson has advantages in some specific areas: finely tuned classic NLU, discovery over large document corpora, very polished integration with IBM products (Db2, MQ, mainframes). In those specific scenarios Watson remains competitive.
For most enterprise cases (conversational agents, document extraction, RAG, content generation, internal copilot), frontier models are the best option today.
Our code and architecture is yours from day one. Prompts, agent logic and integrations document and stay in your git repository. Frontier models are interchangeable — switching from Claude to GPT or to a local model requires minimal changes if architecture is well designed.
Components we use are open-source (n8n, LangChain, pgvector, Postgres) or standard cloud services (Azure OpenAI, Bedrock, GCP). Nothing proprietary of ours. If tomorrow we disappear, your team or any other provider can continue operating and evolving the system.
This approach is more expensive short term (we don't leverage accelerated proprietary IP), but it's what serious clients require: guaranteed portability.
Free 30-min diagnosis. No hard sell — if enterprise tech consultancies fits your project better, we\'ll tell you.