How to choose the best vector database for your AI stack

Every AI project has a turning point. Your RAG system works great in development, retrieval feels instant, and your embeddings pipeline runs smoothly. Then you hit production. Suddenly you're dealing with concurrent users, p95 latency spikes, and queries that need filtering across metadata dimensions you didn't anticipate.

The vector database decision isn't just about dataset size. Real production challenges show up in how your system handles concurrent load, whether your filters perform at scale, how quickly you can update embeddings, and whether your team can actually operate the infrastructure. You'll hit retrieval quality issues like chunking strategy, embedding model selection, and query formulation long before you reach millions of vectors.

This article helps you evaluate vector database infrastructure based on what actually matters in production: performance under load, retrieval quality at your target latency, operational complexity your team can handle, update patterns your workload demands, and whether you need filtering and hybrid ranking capabilities.

What is a vector database?

Vector databases solve similarity search problems by storing high-dimensional vector embeddings and finding semantically close matches using distance metrics like cosine similarity or Euclidean distance. When you need to find similar documents, relevant context for RAG systems, or related products, vector databases search through embeddings to find what's closest to your query.

Traditional B-tree indexes work for exact matches, but they don't scale well for high-dimensional vector comparisons. Vector databases use approximate nearest neighbor (ANN) algorithms that trade perfect accuracy for speed. You tune parameters that trade off recall and latency, and evaluate whether measured recall meets your requirements. For typical RAG workloads with 1536-dimension embeddings and topK=10, well-tuned systems achieve 90-95% recall with p95 latencies under 100ms. Results depend on your dataset size, dimensionality, query patterns, and hardware.

Modern vector databases typically use graph-based indexing algorithms like HNSW (Hierarchical Navigable Small World). HNSW builds a multi-layered graph where each node represents a vector. Searches start at sparse top layers with long-range connections, then drill down through denser layers for precision. This approach balances search speed with recall quality.

Performance varies significantly based on your workload. A system that handles 10 million vectors efficiently might need different tuning at 100 million. Filtering reduces throughput compared to pure vector search. Update frequency affects index maintenance overhead. When evaluating options, test with your actual data dimensions, query patterns, and concurrency requirements to understand real performance characteristics.

Why vector databases matter right now

Vector databases became critical infrastructure as GenAI moved from demos to production systems serving real users. But "critical" doesn't mean universal. Your latency requirements depend on your use case.

Production workloads that need fast vector search

User-facing RAG systems targeting sub-100ms response times need fast vector search. AI agents retrieving conversation context benefit from quick lookups. Recommendation engines finding similar products across catalogs need to handle concurrent queries without degrading. These workloads share common characteristics: high query concurrency, latency-sensitive users, and the need to search across large embedding collections.

The specialized optimization question

Many general-purpose databases now offer vector indexing capabilities. The question isn't whether they can technically store and search vectors. It's whether they deliver the performance characteristics your workload demands. Can they handle your query concurrency? Do filters slow down search to unacceptable levels? How do update patterns affect search performance? Does the index fit your operational model?

Vector databases specialized for similarity search optimize the full stack for these workloads. They use indexing algorithms designed for high-dimensional comparisons, handle concurrent queries efficiently, and provide query execution that combines vector similarity with filtering. When you're searching millions of embeddings under concurrent load with strict latency targets, these optimizations matter.

The decision comes down to workload fit. If vectors are central to your application, you're handling significant query volume, and latency directly affects user experience, specialized vector infrastructure makes sense. If vectors are supplementary to other data operations or your query volume is modest, extending your existing database might work fine.

How to evaluate vector databases

When you're choosing vector database infrastructure, vendor benchmarks and feature lists only tell part of the story. Here's what actually matters.

Performance under your workload conditions

The question isn't "which database is fastest" in the abstract. It's whether a system meets your specific performance requirements under your actual workload. A system optimized for 10 million vectors with pure similarity search will perform differently at 100 million vectors with complex metadata filters.

Test with your actual data and query patterns. Run benchmarks with your vector dimensions, dataset size, filter selectivity, and expected concurrency. Measure p95 and p99 latency, not just averages. Push queries through at production traffic levels to understand tail latency behavior. Vendor benchmarks provide baselines, but your workload determines real performance.

Filtered vector search capabilities

Production workloads rarely need pure vector search alone. You're searching for similar products under $50 in specific categories. You're finding relevant documents from the last 90 days. You're retrieving vectors that match both semantic similarity and business rules.

Some systems require two separate queries: vector search first, then filtering results in your application or operational database. Others support filtered vector search where you combine similarity with metadata predicates in a single query. The architectural difference affects both performance and code complexity.

If you need true hybrid ranking that fuses dense vector similarity with sparse keyword relevance (combining embeddings with BM25 scores, for example), verify that capability explicitly. This differs from filtered vector search and requires specific query execution support.

Operational complexity

Can your team manage another specialized database? Performance matters, but so does operational bandwidth. Tuning index parameters, managing distributed deployments, monitoring query performance, and handling version upgrades all require expertise and time.

Organizations deploying new database systems face challenges with cultural adoption, CI/CD integration, and monitoring infrastructure. Small teams might find that operational simplicity outweighs raw performance advantages.

Total cost of ownership

Infrastructure costs are just one component. Factor in operational overhead, team bandwidth for tuning and maintenance, integration complexity, and monitoring requirements. The time your team spends managing infrastructure is time not spent building features.

Semantic caching can reduce LLM inference costs significantly. This application pattern uses vector similarity to recognize when queries mean the same thing despite different wording. If your infrastructure supports both vector search and caching capabilities, you can implement semantic caching without adding another system to manage.

Evaluating your infrastructure options

The vector database market offers two broad architectural approaches: specialized vector databases and integrated platforms that add vector capabilities to existing data infrastructure. Neither approach is universally better. Your decision depends on workload characteristics and operational constraints.

What specialized vector databases optimize for

Specialized vector databases focus entirely on similarity search. They optimize index structures, query execution, and storage for high-dimensional vector operations.

These systems make sense when:

• Vector search is your core workload with high query volume
• You need to tune index parameters for specific recall/latency targets
• Your team has bandwidth to operate specialized infrastructure
• Your workload justifies the operational overhead

The tradeoff: you're managing additional infrastructure with its own operational requirements, monitoring, and expertise needs.

What integrated platforms optimize for

Integrated platforms add vector search to databases that handle other data types. You get vector similarity search alongside the operational data, caching, or document storage you already use.

These systems make sense when:

• Vectors work alongside other data in your application
• Your team already operates this infrastructure
• Operational simplicity matters more than maximum vector performance
• You want to minimize the number of systems to manage

The tradeoff: vector search performance and features may not match specialized systems, depending on implementation maturity and optimization focus.

Redis as an integrated platform

Redis provides vector search as part of a unified real-time data platform. You get vector similarity search, operational data structures, and caching infrastructure in one system. Redis delivers sub-millisecond operations for key-value lookups and in-memory data structures. For vector search workloads, performance depends on your dataset size, dimensions, and query patterns.

You can implement filtered vector search that combines vector similarity with metadata predicates in a single query. For true hybrid ranking (fusing dense embeddings with sparse keyword scores), Redis Query Engine provides the necessary query execution capabilities.

Redis also enables semantic caching patterns through its combined vector search and caching infrastructure. Your application checks vector similarity before calling LLMs, which can reduce costs substantially in repetition-heavy workloads.

The platform works well when you need multiple capabilities (caching, session storage, vector search, operational data) and want to consolidate infrastructure. Consider capacity planning for mixed workloads and whether your Redis deployment suits vector workload characteristics.

Making your infrastructure decision

Your evaluation should focus on measurable criteria you can validate through testing.

Test these decision criteria

Run these tests with your actual data and expected production load. Vendor benchmarks provide starting points, but your specific workload characteristics determine real performance. Focus on the metrics that directly affect your user experience and operational requirements.

• Latency under load: What's your p95 and p99 latency at expected query concurrency? Measure with your actual vector dimensions, dataset size, and query patterns. Average latency doesn't tell you whether your system handles traffic spikes.
• Filter performance: How does query latency change when you add metadata filters? Test with your actual filter selectivity (how many results match your predicates). Some workloads filter down to 10% of vectors, others to 0.1%. Performance characteristics differ dramatically.
• Recall quality at your target latency: What recall percentage do you need for acceptable retrieval quality? Test whether you can hit that recall within your latency budget. A system delivering 95% recall at 50ms might drop to 85% recall when you need sub-20ms responses.
• Update patterns: How often do you add or modify vectors? Batch updates overnight vs. real-time streaming updates have different performance implications. Test index rebuild times and query performance during updates.
• Operational bandwidth: Can your team operate this infrastructure? Consider monitoring requirements, tuning complexity, troubleshooting workflows, and expertise needed. Small teams shipping features may prioritize operational simplicity over maximum performance.
• Cost structure: Calculate total cost including infrastructure, operational overhead, team time spent on management, and integration complexity. The cheapest per-vector pricing isn't always the lowest total cost.

Specialized vector databases make architectural sense for specific workload profiles. These aren't hard rules, but patterns that emerge from production deployments.

When specialized systems make sense

Your workload might justify specialized vector infrastructure when:

• Vector search is your primary operation with high query volume
• You need to tune index parameters for specific performance targets
• Your team has expertise in distributed systems operations
• Query performance directly affects revenue or user experience at scale

Integrated platforms consolidate infrastructure at the cost of specialized optimization. This tradeoff makes sense when operational simplicity delivers more value than maximum vector performance.

When integrated platforms make sense

You might extend existing infrastructure when:

• Vectors work alongside other data types in your application
• Your team already operates this platform effectively
• Operational simplicity reduces time to production
• Infrastructure consolidation reduces overall system complexity

Neither approach guarantees better performance. Test with your workload to validate which meets your requirements.

Build your vector search infrastructure with Redis

Choosing the right vector database comes down to testable criteria: measure performance under your actual workload, understand your team's operational capacity, and validate that the system meets your latency and recall requirements.

Redis provides vector search as part of a unified real-time data platform. You get vector similarity search alongside caching, session management, and operational data structures. Redis delivers sub-millisecond operations for key-value lookups. For vector workloads, performance depends on your dataset characteristics and query patterns.

You can implement filtered vector search combining similarity with metadata predicates, and Redis enables semantic caching patterns that reduce LLM inference costs by 50-80%.

Try Redis free for managed infrastructure that handles deployment, scaling, and monitoring. Want to discuss your specific AI infrastructure requirements? Meet the team.