Evaluation Guide / Data & Context
How to Evaluate Vector Databases for RAG Pipelines
Enterprise guide for evaluating vector databases — embedding storage, similarity search, hybrid search, metadata filtering, and RAG pipeline integration.
Vector Databases: The Foundation of Enterprise RAG
Retrieval-Augmented Generation has become the dominant architecture for grounding LLMs in enterprise knowledge. At the heart of every RAG pipeline is a vector database that stores, indexes, and retrieves embeddings at scale. Choosing the wrong vector database can mean the difference between sub-second, accurate retrieval and a system that degrades under production load.
Evaluation Timeline
Requirements & Data Profiling
1 week
Characterize your embedding dimensions, document volumes, query patterns, and latency requirements.
Benchmark Setup
1–2 weeks
Load representative data into 2–3 candidate databases. Run standardized recall and latency benchmarks.
Integration Testing
2–3 weeks
Connect to your LLM framework (LangChain, LlamaIndex, etc.). Test hybrid search, filtering, and reranking.
Scale & Reliability Testing
1–2 weeks
Test at 2–5× projected volume. Evaluate concurrent query performance, failover, and data durability.
Core Evaluation Criteria
Search Quality (Recall)
Recall@k metrics at k=5, 10, 20. Test with your actual embeddings and queries — synthetic benchmarks like ANN-Benchmarks are a starting point, not the answer.
Query Latency
P50, P95, and P99 latency at your target concurrency. Sub-100ms P95 is the bar for interactive applications.
Hybrid Search
Combined vector + keyword (BM25) search. Critical for enterprise use cases where exact term matching (product codes, names) complements semantic similarity.
Metadata Filtering
Pre-filter by attributes (tenant, date, document type) before vector search. Performance should not degrade significantly with complex filter predicates.
Scalability
Horizontal scaling strategy, data partitioning, and performance characteristics from 1M to 1B+ vectors. Evaluate both ingestion throughput and query performance at scale.
Operational Maturity
Backup/restore, monitoring, access controls, encryption at rest and in transit, and multi-tenancy support.
Database Architecture Comparison
| Aspect | Purpose-Built Vector DB | Vector Extension (pgvector) | Managed Vector Service |
|---|---|---|---|
| Optimized For | ANN search performance | Postgres ecosystem compatibility | Ease of use, zero-ops |
| Hybrid Search | Native support | Combine with pg full-text | Varies by provider |
| Max Vector Scale | 1B+ vectors | 10–100M vectors | 100M–1B vectors |
| Latency (P95, 1M vectors) | 5–20ms | 20–100ms | 10–50ms |
| Operational Overhead | Moderate (self-hosted) / Low (managed) | Low (existing Postgres) | Minimal |
| Cost at Scale | RAM-dependent, moderate | Low (leverages existing infra) | Per-query + storage |
| Index Types | HNSW, IVF, DiskANN, etc. | HNSW, IVFFlat | Provider-managed |
Quantifying Search Quality
RAG Retrieval Effectiveness
Effective Recall = (Relevant Documents Retrieved in Top-K) / (Total Relevant Documents) × Reranker Precision Boost
Integration & RAG Pipeline Fit
The vector database does not exist in isolation. Evaluate how well it integrates into your end-to-end RAG pipeline — from document ingestion and chunking through retrieval, reranking, and LLM prompting.
RAG Integration Checklist
- Native SDK or client library in your primary language (Python, TypeScript, Go)
- LangChain, LlamaIndex, and/or Haystack integrations maintained and up to date
- Streaming ingestion support for real-time document updates
- Batch upsert performance >10K vectors/second for initial loads
- Supports your embedding model output dimensions (768, 1024, 1536, 3072)
- Multi-vector per document support for late-interaction models (ColBERT)
- Namespace or collection isolation for multi-tenant applications
- Backup and point-in-time recovery for production data durability
Cost Drivers and Hidden Expenses
Cost Warning
Vector databases are memory-intensive. The primary cost driver is RAM, not storage. A billion 768-dim float32 vectors requires ~3TB of RAM for HNSW indexing. Evaluate quantization options (binary, scalar, product quantization) that can reduce memory 4–32× with acceptable recall loss. Also watch for per-query pricing models that become expensive at high QPS.
Selection Decision Framework
- Start with your scale trajectory — If you will stay under 10M vectors, pgvector may be sufficient. Above 100M, you need a purpose-built solution.
- Benchmark with your actual embeddings — Generic ANN-Benchmarks use synthetic data. Your recall and latency numbers on real embeddings are what matter.
- Test hybrid search early — If your use case involves exact-match requirements (SKUs, legal citations, medical codes), hybrid search is non-negotiable.
- Evaluate total cost at 12-month projected scale — Not just storage costs, but RAM, compute, query volume pricing, and egress fees.
- Assess operational maturity — A fast database you cannot monitor, back up, or secure is a liability. Production readiness trumps raw benchmark performance.
The best RAG system retrieves the right context in the right amount at the right speed. Your vector database is the gatekeeper of all three.
ANN-Benchmarks
Standardized approximate nearest neighbor benchmarks for comparing vector search algorithms and databases.
MTEB Leaderboard
Massive Text Embedding Benchmark — helps select the right embedding model to pair with your vector database.
Xither Data & Context Reviews
In-depth evaluations of vector databases, embedding models, and RAG infrastructure.
Researched and reviewed under Xither's editorial standards — AI-assisted, adversarially reviewed, and primary-sourced. Spot an error? Tell us.
Procurement
Shortlisted? Take it to RFP.
Enterprise AI RFI & RFP Template — every question ships with what a strong answer looks like and the red flags to watch for, so you score vendors side by side instead of comparing sales decks. One-time purchase, exports to XLSX.