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Vector Database

Vector Database Setup

Deploy and optimize vector databases for semantic search and RAG applications. We help you choose, configure, and scale the right vector database for your specific requirements and performance goals.

Setup Vector DatabaseView Projects

<50ms

Query Latency

1B+

Vectors Supported

99.9%

Uptime

Databases

Vector databases we deploy

Pinecone

Managed Cloud

Fully managed vector database with enterprise features, automatic scaling, and high availability.

Zero operationsAuto-scalingEnterprise securityGlobal regions

Best for: Teams wanting managed infrastructure

Weaviate

Open Source / Cloud

Feature-rich vector database with built-in ML models, GraphQL API, and hybrid search.

Built-in vectorizationGraphQL queriesHybrid searchSelf-hosted option

Best for: Complex search requirements

Qdrant

Open Source / Cloud

High-performance vector search with advanced filtering and payload management.

Rust performanceRich filteringQuantizationEasy deployment

Best for: Performance-critical applications

Chroma

Open Source

Developer-friendly embedded vector database designed for AI applications and RAG.

Simple APIPython nativeLocal developmentQuick prototyping

Best for: Development and small-scale apps

Milvus

Open Source

Scalable vector database designed for billion-scale similarity search.

Billion-scaleGPU accelerationMultiple indexesKubernetes native

Best for: Large-scale enterprise deployments

PostgreSQL + pgvector

Extension

Vector search extension for PostgreSQL, combining vectors with relational data.

Existing PostgresSQL queriesACID complianceNo new infra

Best for: Teams already using PostgreSQL

Services

What we deliver

Database Selection

Analyze your requirements and recommend the optimal vector database for your use case, scale, and infrastructure.

Infrastructure Setup

Deploy and configure vector database clusters with proper sizing, networking, and security.

Index Optimization

Configure indexing strategies (HNSW, IVF, PQ) optimized for your query patterns and latency requirements.

Security Configuration

Implement authentication, encryption, access controls, and audit logging for enterprise compliance.

Indexing

Index configuration strategies

HNSW

(Hierarchical Navigable Small Worlds)

Graph-based index with excellent query performance and recall

Trade-off: Higher memory, best accuracy

IVF

(Inverted File Index)

Cluster-based index for large-scale datasets

Trade-off: Good balance of speed and accuracy

PQ

(Product Quantization)

Compression technique for memory-efficient storage

Trade-off: Lower memory, slight accuracy loss

Flat

(Brute Force)

Exact search without approximation

Trade-off: Perfect accuracy, slower at scale

Optimization

Performance optimization

Embedding dimension optimization
Batch insertion pipelines
Query caching strategies
Metadata filtering optimization
Hybrid search configuration
Reranking integration
Index partitioning
Quantization tuning

Selection

Choosing the right database

Scale

  • •How many vectors?
  • •Growth rate?
  • •Query volume?

Performance

  • •Latency requirements?
  • •Throughput needs?
  • •Accuracy threshold?

Operations

  • •Self-hosted vs managed?
  • •Team expertise?
  • •Maintenance capacity?

Cost

  • •Budget constraints?
  • •Cost per query?
  • •Storage costs?

Ready to set up your vector database?

Let's deploy the right vector database for your semantic search and RAG needs.

Start Vector Database Project