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What is Fleak?

Fleak is an AI-powered data infrastructure platform that sits between your data sources and AI applications, ensuring data is normalized, filtered, governed, and delivered in real time. Built by a team that joined the Databricks startup accelerator, Fleak solves the problem of inconsistent, noisy, and ungoverned data that degrades AI model performance. Its core capabilities include value-aware routing, AI-orchestrated self-healing pipelines, and governed delivery to any destination. The platform connects to any source—cloud, OT, endpoints, APIs, databases—without custom connectors. Users describe their intent in natural language, and Fleak's copilot builds the pipeline automatically. It is designed for data engineering teams, AI/ML practitioners, and organizations in cybersecurity, industrial/IoT, and financial services that need reliable, real-time data for their AI systems. Fleak reduces integration time from months to weeks, cuts storage costs by 50%, and saves up to 40% on LLM token costs through deduplication and normalization.

AI Tool Review Summary

Performance Score

4.5/5

Content/Output Quality

High, consistent, and governed

Interface

Clean and intuitive with natural language copilot

AI Technology
LLMNLP
Purpose of Tool

To normalize, filter, govern, and deliver real-time data from any source to AI applications.

Compatibility

Connects to any data source (cloud, OT, endpoints, APIs, databases) and delivers to AI apps, data lakes, SIEM, or any destination.

Pricing

Freemium with paid tiers

Features

Features with the highest value for users are highlighted here.

AI-powered value-aware data routing

Self-healing pipelines that auto-detect schema drift

Natural language copilot for pipeline orchestration

Real-time normalization to standard schemas (OCSF, UDM, etc.)

Deduplication and filtering at ingestion

Fine-grained access control with full audit trail

Connect any source without custom connectors

Deliver clean data to any destination in real time

Who Is It For?

Data engineering teams

AI/ML practitioners

Cybersecurity teams

Industrial IoT teams

Financial services firms

Startups

Enterprise data platforms

Compliance officers

Data scientists

DevOps teams

Pricing

Free

$0/free
  • Connect up to 2 data sources
  • Basic normalization and filtering
  • Community support
Popular

Pro

$19/monthly
  • Unlimited data sources
  • AI copilot for pipeline creation
  • Self-healing pipelines
  • Email support

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Pros & Cons

Pros

  • Dramatically reduces integration and storage costs while improving data quality.
  • Self-healing pipelines minimize engineering maintenance and downtime.

Cons

  • May require initial setup and tuning to match specific data sources and use cases.
  • Pricing details are not publicly available, potentially limiting transparency for small teams.

FAQs

Just Launched

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Reviews

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Rating

5.0

Pros

Cons

Quick Fleak Comparision

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