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Watsonx.data
✓ verifiedFree trial
IBM's open, hybrid data lakehouse that connects, governs and optimizes enterprise data to make it AI-ready across clouds and on-premises.
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Vespa
✓ verifiedFree trial
Open-source AI search and vector database platform for building large-scale search, RAG, and recommendation systems.
Pricing
No public pricing
No public pricing
Free trial available
DEVELOPER: FREE
STARTER: $119 / month
GROWTH: $599 / month
ENTERPRISE: Starting at $1,800 / month
No public pricing
No public pricing
Free trial available
Core features
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- ✦Open hybrid data lakehouse
- ✦Connects data across clouds and on-prem
- ✦Governance, lineage and access controls
- ✦Business-context enrichment
- ✦AI-ready data for analytics and models
- ✦Developer-first platform for AI-powered integrations
- ✦Secure, isolated sandboxes for running JavaScript/Python code
- ✦Automatic management of npm/PyPI dependencies
- ✦Built-in platform plumbing: secrets, webhooks, scheduling, logs, and audit
- ✦Yep Agent (prompt → runnable processes)
- ✦MCP Server/Tools (convert code into AI agent tools)
- ✦Serverless runtime (YepCode Run) and SDK access
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- ✦Combined vector, text, and structured search
- ✦Distributed machine-learned ranking at query time
- ✦Streaming search mode for cost-efficient personal/private data
- ✦Support for retrieval-augmented generation pipelines
- ✦Continuous deployment and automated scaling
- ✦Open-source core with a managed cloud option
Use cases
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- →Unifying fragmented enterprise data
- →Governing data for AI workloads
- →Moving AI pilots to production
- →Powering analytics with trusted data
- →Building complex API integrations that require custom code and logic beyond what no-code tools offer.
- →Safely running AI-generated scripts in isolated environments with secrets management.
- →Automating workflows that require large datasets, loops, branching, or custom dependencies.
- →Connecting AI agents to external databases, APIs, and services using MCP tools.
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- →Building large-scale enterprise search engines
- →Powering RAG pipelines that need strong retrieval relevance
- →Building recommendation and ad-targeting systems
- →Search over personal/private data at lower indexing cost
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