Search
Vector database and search functionality for Peam
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Search?
Peam's vector database and search functionality provides efficient storage and retrieval of high-dimensional vectors, ideal for applications requiring similarity searches.
Key differentiator
“Peam offers a self-hosted, efficient solution for storing and retrieving high-dimensional vectors, optimized specifically for similarity searches.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Officially supports only a few popular databases and lacks extensive third-party integration support
Requires manual configuration of storage, indexing, and network settings
Fit analysis
Who is it for?
✓ Best for
Developers building self-hosted vector database solutions who need efficient similarity searches.
Data scientists working on projects requiring high-dimensional vector storage and retrieval.
✕ Not a fit for
Teams needing real-time streaming capabilities as Peam is batch-oriented
Projects with strict budget constraints due to the self-hosting requirement
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Integrations
Next step
Get Started with Search
Step-by-step setup guide with code examples and common gotchas.