أفضل أدوات الذكاء الاصطناعي لـ البيانات / التحليل 2026: مقارنة كاملة

AI for data and analytics covers extracting information from text or tables, generating queries (SQL, formulas), explaining datasets, and creating reports in natural language. Models need to combine reasoning and precision to avoid errors on numbers.

Key points: ability to reason over structured data, code support (Python, SQL) if you automate pipelines, and cost per request or data volume. Models like GPT-4o, Claude, or Gemini are used for exploratory analysis and summary generation; dedicated tools (Browse AI, etc.) add web extraction or automation. Check context limits (table or log size) and export options (CSV, reports). Compare API pricing and plans by request volume.

A comparison of pricing and context limits helps you size a data deployment.