If you're building anything with embeddings — semantic search, RAG pipelines, recommendation systems, or similarity matching — you need somewhere to store and query your vectors. The vector database market has exploded over the past two years, and choosing between options has become genuinely confusing.
We've deployed all four of these databases in production for clients across different industries. Here is what we've learned about when to use each one.
What Vector Databases Actually Do
A vector database stores high-dimensional numerical vectors (typically 768-3072 dimensions for modern embedding models) and lets you query them by similarity. When a user asks a question, you convert that question into a vector using the same embedding model, then find the stored vectors that are closest to it — typically using cosine similarity or dot product distance.
This is fundamentally different from traditional databases. You are not searching for exact matches or filtering by columns. You are finding the records whose meaning is most similar to a query. This is what powers semantic search, where "how do I reset my password" matches documentation about "account recovery procedures" even though the words are different.
Every vector database does this core operation. Where they differ is in performance at scale, filtering capabilities, hosting options, pricing, and ecosystem integration.
The Comparison
Here is how the four leading options stack up across the dimensions that matter most.
Pinecone is the most mature managed vector database. It is fully hosted — you never manage infrastructure. Performance is excellent, with single-digit millisecond query latency at scale. Metadata filtering is strong, and the API is clean and well-documented. The trade-off is cost: Pinecone's pricing can escalate quickly as your dataset grows, and you are locked into their cloud. For teams that want to focus entirely on application logic and never think about infrastructure, Pinecone is hard to beat.
Weaviate is an open-source vector database with strong hybrid search capabilities built in. It combines dense (vector) and sparse (BM25) search natively, which is a significant advantage for RAG applications. Weaviate offers both a managed cloud service and self-hosted deployment. Its module system lets you plug in different embedding models and rerankers directly. The learning curve is steeper than Pinecone's, but the flexibility is greater.
Qdrant is our current recommendation for self-hosted deployments. It is written in Rust, which gives it excellent performance characteristics and low memory overhead. The filtering system is particularly powerful, supporting complex boolean queries on metadata alongside vector search. Qdrant offers both a managed cloud and a Docker-based self-hosted option. Documentation is good, the API is intuitive, and the community is active and growing.
ChromaDB is the simplest option and the one we recommend for prototyping and smaller-scale applications. It runs in-process (embedded mode), meaning you can start using it with a single pip install and no external infrastructure. This makes it ideal for development, testing, and applications with fewer than a million vectors. For production workloads beyond that scale, you will likely want to graduate to one of the other options.
Performance and Scaling
Raw query performance varies less than you might expect across these databases at moderate scale (under 10 million vectors). All four can return results in under 50ms for typical workloads. The differences become meaningful at scale.
Pinecone and Qdrant handle large-scale deployments (100M+ vectors) most gracefully. Pinecone manages this transparently through its managed service. Qdrant achieves it through efficient memory-mapped storage and horizontal sharding.
Weaviate scales well but requires more careful configuration of its HNSW index parameters as datasets grow. ChromaDB is not designed for this scale — it is excellent for datasets up to approximately one million vectors but should not be your choice for large-scale production deployments.
One often-overlooked factor is write performance. If you are building a system that needs to ingest and index documents continuously (rather than in batches), test write throughput early. Qdrant and Weaviate handle concurrent writes well. Pinecone can throttle writes under heavy load on lower tiers.
Pricing and Hosting
Cost is where these options diverge most significantly.
Pinecone starts with a free tier (limited to 100K vectors) and moves to usage-based pricing. For production workloads with a few million vectors, expect to pay £200-800/month depending on performance requirements. Costs scale linearly with data volume.
Weaviate Cloud offers a free sandbox and paid tiers starting around £50/month. Self-hosting Weaviate is free (open-source) but you bear the infrastructure and operational costs, typically £100-300/month for a modest production deployment on cloud infrastructure.
Qdrant Cloud is competitively priced with a free tier and production plans starting at approximately £30/month. Self-hosted Qdrant runs comfortably on modest hardware — we've run production workloads on a single 4GB RAM instance for datasets under 5 million vectors.
ChromaDB is free and open-source. Since it runs embedded, the cost is whatever you're already paying for your application server.
When to Use Each
Based on our experience across dozens of deployments, here are our recommendations:
- Choose Pinecone if you want a fully managed experience, your team does not want to manage database infrastructure, and your budget can accommodate the pricing. Ideal for SaaS products and teams focused on shipping quickly.
- Choose Weaviate if hybrid search (vector + keyword) is a core requirement, you want built-in module support for embeddings and reranking, or you need multi-tenancy features.
- Choose Qdrant if you are self-hosting, need powerful metadata filtering, want the best performance per pound spent, or are building systems where low latency is critical.
- Choose ChromaDB for prototyping, proof-of-concept work, smaller-scale applications, or any situation where simplicity and speed of development matter more than production scalability.
For most of our client engagements, we start with ChromaDB during discovery and prototyping, then move to Qdrant or Pinecone for production depending on the client's hosting preferences and operational maturity.
For a more detailed head-to-head, see our Pinecone vs Weaviate comparison.
Choosing a vector database is just one piece of the puzzle. If you're building a RAG system or semantic search application and want guidance on the full architecture, book a free consultation and we'll help you make the right choices for your specific requirements.