Your vector database is the memory of your RAG system. Pinecone, ChromaDB, and Weaviate are the three names we evaluate most often for GenOrbit client work — none is universally best. The right choice depends on team size, query patterns, and whether you need hybrid keyword-plus-vector search from day one.
Under 1M vectors and a small team? Start managed or pgvector on Postgres you already operate.
Pinecone: managed simplicity
Pinecone removes index tuning headaches for teams that would rather not operate vector infrastructure. Namespace isolation and metadata filters suit multi-tenant SaaS RAG. Watch egress, query QPS, and storage pricing on chat-heavy products — costs compound quietly. Model Pinecone bills against cost-per-successful-query, not raw QPS, because chat products often issue three to five retrieval calls per user message. Index dimension and metric choices are set at creation — plan embedding model changes as re-index projects, not config toggles.
We recommend Pinecone when time-to-production beats infra control and your team lacks dedicated platform engineers. Plan index versioning and backup strategy before your first major re-embedding migration. Pinecone's serverless tier suits spiky B2B traffic; provisioned pods make sense when p99 latency SLAs are contractual. Export namespace metadata regularly so tenant migrations between indexes do not lose filter mappings.
ChromaDB: developer velocity
ChromaDB embeds easily in Python projects and local development workflows. Hard to beat for prototypes and single-tenant internal tools. Many teams use Chroma locally and migrate to Pinecone or Weaviate for production scale. Chroma's in-process mode lets engineers run full RAG loops in CI without cloud credentials — valuable for golden-set eval pipelines. Persistent Chroma on a single VM works for internal tools with under one hundred thousand vectors.
Do not treat Chroma's simplicity as permission to skip metadata design. The migration pain you defer shows up as downtime when you finally need hybrid search or multi-region replication. Define metadata schemas — tenant_id, doc_type, access_level — in Chroma even for prototypes so export scripts to production stores are mechanical, not archaeology. Document the Chroma-to-Pinecone migration runbook before your first production traffic spike.
Weaviate: hybrid and schema-rich retrieval
Weaviate combines vector search with BM25 hybrid ranking — useful for SKUs, medical codes, legal citations, and product names that need exact matches plus semantic similarity. Schema-rich objects help when retrieval must join structured fields with embeddings. Weaviate's GraphQL API suits teams that want typed queries over vector plus relational fields in one request. Multi-tenancy via tenant shards maps cleanly to B2B SaaS isolation requirements.
Under one million vectors and a small team, start managed or use pgvector on Postgres you already run. See our RAG pipeline guide for how ingestion metadata maps to store-specific filters. If your corpus is mostly prose without exact-match requirements, pgvector on existing Postgres may beat adding a dedicated vector service — Weaviate earns its ops cost when hybrid search is non-negotiable. Test BM25 weight tuning on product SKU and medical code queries before committing.
What to do next
Start with one measurable outcome — latency, accuracy, adoption, or revenue — and design your stack around that constraint. GenOrbit ships production AI systems across healthcare, astrology, fintech, and SaaS; if you want a second pair of eyes on architecture or delivery, we are happy to talk.
Related: /services/llm-rag-agent-development · /blog/how-to-build-rag-pipeline-langchain-fastapi