Managed vector search for AI applications at scale
Pinecone is a fully managed vector database designed for fast similarity search over large collections of embeddings. It suits AI products where vector search is a core, high-volume workload and you would rather not run the infrastructure yourself.
Pinecone handles indexing, scaling and availability for you, supports metadata filtering and namespaces for multi-tenant data, and performs well under heavy query loads. The trade-offs are an extra service to keep in sync with your main database, data held with a third party and usage-based costs. For smaller or moderate workloads, pgvector inside PostgreSQL is often simpler and cheaper. We design sync pipelines so the source of truth stays in your own database.
What we build with Pinecone
- Semantic search and RAG retrieval
- Embedding ingestion pipelines
- Metadata filtering and namespaces
- Sync with primary databases
- Recall and cost benchmarking
Pinecone questions
Pinecone or pgvector: which should we use?
For most projects already on PostgreSQL, pgvector is a sensible start. Pinecone makes more sense when vector volume or query load is high and you want it fully managed.
What happens to our data in Pinecone?
It stores the vectors and any metadata you send. We keep sensitive content in your own database and store only the minimum needed for retrieval.
What are namespaces in Pinecone?
Namespaces partition an index so queries only search one segment, which is a common way to separate tenants or data sets. It keeps customer data apart without running a separate index for each.
How does metadata filtering work in Pinecone?
Each vector can carry metadata such as document type, date or tenant, and queries can filter on those fields while searching. We keep metadata small and limited to fields that are actually filtered.
How do you load large volumes of data into Pinecone?
We batch upserts, run them in parallel within rate limits and track progress so failed batches can be retried. For very large loads we use bulk import where the index supports it.
How do you keep Pinecone in sync with our main database?
Changes in your system of record trigger queued jobs that upsert or delete the matching vectors, using the same record IDs in both places. A periodic reconciliation checks for anything missed.
Does Pinecone support hybrid search?
Yes. It can combine dense vectors with sparse keyword-style vectors so results reflect both meaning and exact terms. We test whether hybrid ranking improves results for your content before adding the complexity.
What is the difference between serverless and pod-based Pinecone indexes?
Serverless indexes scale automatically and charge by usage and storage, while pod-based indexes run on provisioned capacity you choose. Serverless suits most new projects with variable traffic.
How do you measure retrieval quality in Pinecone?
We build a test set of real questions with known relevant documents and measure how often they appear in the top results. Changes to chunking, models or filters are checked against it.
Can we move away from Pinecone later?
Yes, if your original content and embedding pipeline stay in your own systems. Vectors can then be regenerated or exported into another store such as pgvector without depending on Pinecone.
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Tell us what you are building. We reply within one business day with next steps — and we are happy to sign an NDA first.