Vector Database Development That Powers Semantic Search, RAG, and AI at Scale

The right choice depends on your scale, query patterns, and deployment constraints. Pinecone is the right choice for teams that want a fully managed service with minimal operational overhead. Weaviate suits workloads that combine vector and keyword search. Qdrant delivers the highest raw query performance for on-premises deployments. pgvector is the natural choice if you are already running PostgreSQL and want to add vector search without introducing a new system. Milvus handles billion-scale datasets with high availability requirements. Perimattic runs a structured evaluation during the discovery phase and recommends the database that matches your specific requirements.

Modern vector databases are built for scale. Pinecone and Weaviate handle hundreds of millions of vectors in their managed tiers. Milvus has been benchmarked at over one billion vectors with sub-10ms P99 query latency on appropriately provisioned hardware. pgvector handles tens of millions of vectors reliably on standard PostgreSQL infrastructure. The practical limit is determined by embedding dimensionality, your target query latency, and your infrastructure budget. Perimattic sizes your infrastructure during the architecture phase to meet your specific performance SLA.

A vector embedding is a numerical representation of a piece of content — a sentence, image, or document — as a list of floating-point numbers in a high-dimensional space. Embedding models are trained so that semantically similar content produces vectors that are close together in that space, measured by cosine similarity or dot product. This means a vector database can find documents that mean the same thing even when they use different words, something a traditional SQL query cannot do. Perimattic selects the embedding model that matches your content domain, language requirements, and query latency targets.

A traditional relational database stores structured data in rows and columns and retrieves records using exact-match queries. A vector database stores high-dimensional embeddings and retrieves records using approximate nearest-neighbour search, finding the stored vectors most similar to a query vector. This enables semantic search, meaning-based retrieval, and recommendation systems that are not possible with SQL. Some vector databases (pgvector, Weaviate, Elasticsearch with kNN) also support hybrid search, combining vector similarity with traditional keyword filtering for more precise results.

In a Retrieval-Augmented Generation (RAG) pipeline, the vector database is the knowledge store. When a user submits a query, the system embeds the query and searches the vector database for the most semantically relevant documents or passages. Those documents are then injected into the LLM prompt as context, allowing the model to generate answers grounded in your proprietary content rather than its training data alone. The quality of the RAG system depends directly on the vector database setup: embedding model selection, chunking strategy, indexing configuration, and retrieval parameters. Perimattic builds and optimises the full RAG stack, not just the database layer.

Yes. Perimattic integrates vector databases with existing applications, ERP systems, content management platforms, and data warehouses. We build the embedding pipelines that synchronise your existing content into the vector store as it changes, and expose the vector search capability through APIs that your existing applications can call. We have connected vector databases to ERPNext, Salesforce, custom CMS platforms, and document management systems. Every integration includes authentication, rate limiting, and monitoring as standard.

Hybrid search combines vector similarity search with traditional keyword (BM25) search, then merges the results using a re-ranking step. It is the right approach when you need to balance semantic understanding with exact-term matching — for example, when users search for specific product codes, regulation numbers, or proper nouns that embedding models may not distinguish accurately. Weaviate, Qdrant, and Elasticsearch with kNN all support hybrid search natively. Perimattic configures and tunes hybrid search parameters as part of the query optimisation phase, calibrating the balance between vector and keyword signals for your specific use case.

Performance at scale depends on indexing algorithm selection (HNSW for high recall, IVF for lower memory footprint), dimensionality reduction, quantisation (converting 32-bit floats to 8-bit integers to reduce memory without significant accuracy loss), and shard distribution across nodes. Perimattic benchmarks query performance against your target dataset size and latency requirements during the architecture phase, selecting the configuration that meets your SLA. Load testing and observability are built into every deployment so performance degradation is detected early.

Every vector database deployment Perimattic builds includes authentication and API key management, TLS encryption in transit, encryption at rest, network isolation (VPC and private subnets), audit logging of all queries and mutations, PII data masking for regulated content, and role-based access controls. For healthcare clients, we align to HIPAA. For financial services clients, we align to GDPR and relevant local regulatory frameworks. Security controls are designed into the architecture from day one, not added after deployment.

Cost depends on the scale of the deployment, the number of data sources to ingest, the complexity of the embedding pipeline, and whether you need RAG integration or a standalone semantic search capability. A focused proof of concept for a single data source typically takes two to four weeks. A production deployment with a full embedding pipeline, hybrid search, monitoring, and application integration typically takes eight to fourteen weeks. Perimattic provides a detailed cost estimate after the free discovery session based on your specific requirements and infrastructure constraints.