If you've built anything on top of an LLM in the past couple of years, you may have hit the wall many builders hit: the model writes fluently but has no view into your data. A vector search database helps close that gap. It stores vector embeddings and retrieves the items closest in meaning to a query. That retrieval step now sits underneath a lot of production AI, from chatbots to AI agents.Vector search databases moved fast in 2025 and 2026, and some of what changed should affect what you buy. This guide covers what a vector search database is and how it works, the news reshaping the category, the workloads that depend on it, and what to evaluate before you commit.What a vector search database is & how it worksA vector search database rests on two ideas: turning meaning into numbers, and finding the nearest of those numbers fast as collections grow.Vector embeddings & vector spaceA dense vector embedding is what makes meaning-based search possible: it represents a piece of content as a list of numbers, placing semantically similar items near one another in vector space. Dimension count matters because it sets how much detail the model can encode and how much memory each vector consumes at query time. Models vary widely on that axis: modern embedding representations commonly span 128 to 4096 dimensions, and production collections often reach hundreds of millions of vectors. At that scale, search becomes a geometry problem: find the stored vectors nearest to the query vector, usually measured by cosine similarity, dot product, or Euclidean distance.Traditional indexes & the curse of dimensionalityTraditional databases without specialized vector indexes don't perform well for nearest-neighbor search across high-dimensional spaces. Tree-based indexes such as k-d trees split space one axis at a time, and they lose their advantage as dimension counts climb. In high-dimensional space, brute-force linear scan often outperforms classic indexing methods because of the "curse of dimensionality." Scanning every vector is typically practical only for smaller collections or workloads with relaxed latency requirements.Approximate nearest neighbor searchApproximate nearest neighbor (ANN) algorithms are the common workaround. They reduce the number of vectors examined and often lower query latency compared with exhaustive search, at the cost of recall. One of the most widely deployed algorithms is Hierarchical Navigable Small World (HNSW), which builds a multi-layer graph: upper layers act as long-range shortcuts and use greedy traversal to localize the search fast, while the base layer uses a broader candidate search for fine-grained traversal. The cost is memory, since the whole graph typically lives in RAM. A flat index checks every vector for perfect recall, making it useful for small datasets and for generating ground truth when you measure ANN accuracy.Redis Iris serves agent context in millisecondsRedis Iris connects memory, live data, and retrieval in one place.The major developments over the past two years were less about new indexing fundamentals and more about where vector search lives in the stack. Adoption kept climbing, fueled by GenAI, retrieval-augmented generation (RAG), and hybrid search. By 2028, 80% of GenAI apps are projected to be built on existing data management platforms rather than new specialized ones.A few signals stand out:Vectors as a data type. Teams are increasingly treating vectors as a data type inside existing multimodel databases.Cheaper RAG storage. Amazon's S3 vector buckets cut RAG storage costs.Purpose-built adoption is softening. Purpose-built vector databases have lost adoption share among enterprise teams building RAG.Hybrid retrieval is the default. Teams increasingly treat hybrid retrieval, which combines dense vector search with keyword or Best Matching 25 (BM25) search, as the consensus enterprise strategy, and they often rerank the combined results.By 2026, vector search is production infrastructure. Evaluations now judge vector databases on production requirements, with a blunt takeaway from a late-2025 review of 17 evaluated systems: storage alone isn't enough. And real deployments run into the billions, with HubSpot handling semantic search across 20 billion vectors.Agents are a big reason why. Anthropic donated the Model Context Protocol (MCP) to open governance in late 2025, when its SDKs had already passed 97 million monthly downloads. Stateful agents need somewhere to keep session state and long-term memory, which creates sustained read and write demand on memory and retrieval systems.RAG, semantic caching & agent memory in productionThose headlines trace back to a handful of workloads that often lean on the same retrieval primitive. RAG systems ground LLM answers in your own data. The original RAG architecture paired a generation model with a dense vector index and reported state-of-the-art results on open-domain question answering. The division of labor matters here: your app chunks documents, generates vector embeddings, and stores them; at query time it embeds the question, asks the vector index for the most similar chunks, and passes those to the model as context. The database stores, indexes, and retrieves. The app orchestrates. Agentic RAG extends this by letting agents decide when and what to retrieve and refine their searches iteratively instead of running one fixed retrieval step.Semantic caching applies the same machinery to cost. The cache layer stores vector embeddings of prompts alongside their LLM responses; when a new query lands, the system compares its vector embedding with cached vector embeddings, and if similarity clears a threshold, it returns the stored response instead of calling the model. Users phrase the same intent many ways, so hit rates can run high: one semantic cache measured hit rates of up to 68.8% across question-and-answer (Q&A) workloads.Agent memory is the newest driver. Stateful agents often need short-term memory for the current session and long-term memory that persists across sessions, and stuffing everything into the context window fails in catalogued ways. Context poisoning, distraction, confusion, and clash together produce the context rot practitioners complain about. LLMs also tend to overlook information in the middle of long contexts. Storing memories as vector embeddings and retrieving only what's relevant to the current turn can keep the window small and the context cleaner.Build agents that remember, not agents that guessRedis Iris gives every agent fresh context and long-term memory.How to evaluate a vector search database in 2026Once you know the workloads, the evaluation criteria follow. These criteria often get less attention than raw feature checklists, but they're what separates a database that demos well from one that holds up in production. Four areas matter most:Hybrid search as a first-class feature. Dense vector search generalizes well but can miss exact identifiers, stock-keeping units (SKUs), and rare terms that keyword scoring catches. In a benchmark covering 23,088 queries, hybrid retrieval fused with Reciprocal Rank Fusion (RRF) reported 0.695 Recall@5 versus 0.587 for dense retrieval alone. For workloads similar to this benchmark, hybrid retrieval is a useful baseline to evaluate for RAG.Filtered query performance. Most production queries carry metadata filters such as tenant ID, document type, or date range, and filtered performance can diverge sharply from unfiltered performance across databases.Tail latency under real load. Median latency on a static dataset hides what 95th-percentile (P95) and 99th-percentile (P99) latency look like under concurrent queries and continuous ingestion.Freshness and operational footprint. Batch-updated indexes can serve stale context to agents, and every separate system you add (vector store, cache, operational store) is another thing to keep in sync and another thing that can page you at 3 a.m.These rarely show up in an initial proof of concept, which is why teams often discover them during a rebuild rather than an evaluation.Redis runs vector search where your data already livesRedis maps to that checklist as an in-memory platform where vector search lives next to your cache, sessions, and operational data. It reports sub-millisecond latency for supported operational and caching workloads, while vector-search latency depends on dataset, index, recall target, and load. On the vector side, Redis supports HNSW and FLAT index types, k-nearest neighbor queries, range queries, and metadata filtering over hashes or JSON documents. For the hybrid retrieval described above, Redis 8.4 introduced the FT.HYBRID command, which fuses full-text and vector results in a single query using RRF or Linear Combination.Benchmarks give a sense of scale. In a billion-vector benchmark, Redis reported 90% precision at approximately 200 ms median latency, running 50 concurrent queries retrieving the top 100 neighbors each. The same benchmark reported approximately 66,000 vector insertions per second at approximately 95% precision. HNSW's usual memory trade-off is addressed through Redis' Intel Scalable Vector Search (SVS)-based vector compression, which shrinks the index footprint and reported up to 144% higher query throughput for FP32 vectors at 0.95 precision in a separate benchmark.Vector search sits inside the broader AI stack that Redis packages as Redis Iris, its real-time context engine for agents. Redis LangCache, the managed semantic caching service, reported up to 15x faster responses on cache hits and up to 73% lower LLM inference costs in high-repetition workloads. Redis Agent Memory, another Iris service, keeps session-scoped working memory and stores long-term memory as vector embeddings retrieved through semantic search. The RedisVL Python client wires vector search and semantic caching into app code.Fresh context, every callRedis Iris keeps agent data current so answers stay accurate.Vector search is becoming a data type, not a database categoryPut the news, the workloads, and the checklist together and the through-line of the past two years is consolidation. Vector search increasingly became a capability inside existing data platforms rather than a reason to stand up a separate one, hybrid retrieval emerged as a useful production baseline for many RAG workloads, and agents pushed hard on freshness and memory. Evaluate accordingly: filtered performance, tail latency under load, and how many separate systems you'll have to keep in sync.Redis approaches this as a fast, in-memory context engine where vector embeddings live next to your cache, sessions, and operational data instead of in yet another system. Redis Iris builds on that foundation, connecting retrieval, semantic caching, and agent memory in one runtime so agents get fresh context without a separate stack. If your app team already runs Redis, your AI team may be closer to a vector search database than they think. Try Redis Iris against your own vector embeddings, or talk to our team about your AI infrastructure.