PI
Editor pick
Paid
Beginner friendly
Easy
Pinecone
Managed vector infrastructure for retrieval-heavy AI apps.
Pinecone is the managed vector database teams often choose for production RAG systems.
Quick take
Best for
Teams shipping semantic search
Check if this matches what you need right now.
Cost and setup
Easy · Paid
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Key details
Pricing
Paid
Skill level
Medium
Setup
Easy
If you want to move quickly, this is a good first tool to try.
What it does
It focuses on fast retrieval, simple operational overhead, and scalable semantic search.
Pinecone is a default choice for many AI teams that need managed vector search without building the infrastructure themselves. It fits products that depend on high-quality retrieval and want a mature operational story around embeddings and search performance.
Who it is for
Teams shipping semantic search
RAG products
Builders who want managed infra
- Knowledge retrieval
- Embedding search
- Semantic matching
Feature highlights
- Managed vector infrastructure
- Great for RAG
- Operationally friendly
Pros
- Managed service simplicity
- Good at scale
- Popular and well understood
Considerations
- Can be more expensive than self-hosted options
- You still need to tune retrieval quality
Integrations and ecosystem
LangChain
LlamaIndex
Firecrawl
OpenAI
Onboarding steps
- 1Create an index
- 2Insert embeddings
- 3Run a retrieval query
- 4Measure recall and latency
Getting started
- Use a small corpus first
- Keep chunking simple
- Review retrieved sources regularly
Screenshots
FAQ
Is Pinecone only for enterprise teams?
No, though many teams use it once retrieval becomes a core product feature.
What is it best at?
Operationally simple vector retrieval for production AI apps.
Related tools
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Qdrant
Qdrant is a strong option for teams that want speed, filtering, and control over vector search.
Best for: RAG buildersSetup: Medium
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Supabase
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LlamaIndex
Use LlamaIndex when your product depends on search, documents, or private knowledge.
Best for: Knowledge assistantsSetup: Medium
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Browserbase
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