Determining the best machine learning and AI databases

Data infrastructure underneath a machine learning (ML) system rarely fails due to one slow query. It fails because of multiple issues.  Training datasets that fit comfortably on a laptop last year now sprawl across petabytes. Inference paths that started as one model call have grown into deep execution chains of retrieval, ranking, tool use, and synthesis, where p99 latency at every hop makes response time longer. Vector indexes have to stay fresh while embeddings are rewritten in batches of millions. Agentic workflows fan out across many concurrent reads and writes for what looks externally like one request. Retrieval-augmented generation (RAG) is used with every prompt.

Picking a database for ML and artificial intelligence (AI) is a tough decision. Latency budgets are tight, but overprovisioning to hide unpredictable performance costs money. System load grows unexpectedly; a feature that ships on Monday quintuples traffic by Friday. The more specialized systems stitched together there are, the more work there is for staff. Database choice is no longer between SQL or NoSQL that fits a use case; it’s whether the AI stack stays within its service-level objectives at peak.

Ten databases show up consistently in production ML and AI architectures. Here’s what they actually do well, and where each one falls down. The intent is to give engineering leaders, ML platform engineers, and architects more than a feature checklist.

What ML and AI workloads demand from a database

The first mistake teams make is treating ML data infrastructure as one workload. It is at least three.

Training and offline feature generation

Training reads large historical datasets sequentially, joins across many tables or partitions, and writes derived features back. The bottleneck is throughput per dollar across cold data, scanning billions of rows, performing point-in-time correct joins, and feeding parallel workers such as Spark, Ray, PyTorch, or TensorFlow without saturating the network or the storage tier.

Online feature serving and inference

Inference reads a few features per request, often joining ten or more entities, and must return them in time for the model to work as well. Practitioner-led guidance for real-time feature stores commonly targets sub-50 ms feature retrieval so the rest of the inference budget remains usable¹. The bottleneck here is point-lookup latency at high concurrency under fan-out, not raw throughput speeds. 

Vector retrieval and RAG

RAG, semantic search, and recommendation rerankers have added a third workload. Each query is an approximate nearest neighbor (ANN) search over millions to billions of high-dimensional embeddings, frequently combined with structured filters and keyword signals. Recall, latency, and freshness all matter at once, and the system has to keep reading new embeddings as upstream models retrain or as content updates.

One database rarely excels at all three jobs, which is why AI stacks tend to combine an analytical engine for training, a low-latency operational store for serving, and either a vector-aware extension or a dedicated vector engine for retrieval. Then the question becomes which combinations minimize duplication, fan-out variance, and the cost of running them.

Why tail latency dominates ML serving infrastructure

Enterprises that look only at median latency miss a big user experience issue. ML pipelines fan out across many components, including feature stores, embedding lookups, vector retrieval, ranking models, post-filters, and business logic, and a request is only as fast as its slowest fan-out leaf. With enough fan-out, even rare slow events at p99 or p99.9 dominate user response time, because the probability that some shard is slow grows quickly with the number of components touched.

That gets worse for AI. One user prompt in an agentic system triggers hundreds of internal operations across retrieval, multiple model invocations, tool calls, and follow-up queries. If a database has slow responses 0.01% of the time, across 100 fan-out calls, there is roughly a 1% chance any given user request hits one, and the deeper the fan-out, the more often you fall into the long tail. Engineering teams that focus on p99 response are still under-targeting; for production AI, user experience depends on p99.9 and p99.99.

Database latency depends on cache hit rate, garbage collection cycles, vacuum or compaction behavior, and hot-shard balancing, which results in variance that stacks up across hops. But systems with predictable latency regardless of access pattern, working-set size, or background work absorb fan-out more gracefully. That difference is why the same model and the same prompt feel snappy in staging and unusable under production load.

10 best databases for machine learning and AI

The selection that follows reflects what shows up repeatedly across credible sources covering ML and AI database choices, weighted toward systems with production track records on AI workloads. The list is intentionally diverse: a real-time operational store, a relational system with vector capabilities, a wide-column distributed NoSQL, three specialized vector databases, an AI search platform, a hybrid search engine, a columnar analytics engine, and a graph database.

1. Aerospike

Aerospike is a distributed real-time database designed for workloads that require a consistent user experience even when scale, load, or access patterns change unpredictably. It started supporting high-throughput ad tech and fraud detection systems and has become a common choice for the operational layer of ML and AI stacks at companies such as PayPal, Adobe, Airtel, Criteo, Wayfair, The Trade Desk, Sony Interactive Entertainment, and DBS Bank.

Architecture

Aerospike’s defining feature is its patented Hybrid Memory Architecture. It stores indexes in RAM while data resides on NVMe SSDs, and the storage engine reads from flash. Because random and sequential I/O on SSDs cost roughly the same per operation, Aerospike sustains sub-millisecond reads from persistent storage quickly, with measured single-node throughput in the hundreds of thousands to millions of operations per second. That means the database does not have to fit in DRAM to behave like an in-memory database, which reduces both server count and TCO compared with RAM-resident systems. Independent comparative work has documented sub-millisecond response under tunable consistency, supporting both availability and consistency modes.² 

Beyond storage, Aerospike provides multi-record ACID transactions, Cross-Datacenter Replication (XDR) for active-active geo-distribution, and vector search, which lets teams keep embeddings, structured features, and operational records in the same cluster. The database is written in C and avoids garbage collection, so its tail latency stays tightly bounded even as nodes age and as datasets grow.

Best fit workloads

Aerospike works best for situations where user-visible interactions depend on many parallel reads, such as feature lookups during inference, candidate retrieval and reranking in recommendation engines, transaction-time fraud features, RAG context assembly, and agent memory stores. In a published benchmark on a 20-node AWS cluster, Aerospike sustained between 4 and 5 million transactions per second with sub-millisecond latencies on petabyte-scale datasets. 

At Wayfair, the predictive AI recommendation system handles 1 million transactions per second at sub-millisecond p99.9 latency on just seven nodes. Criteo runs 950 billion ad matches per day at sub-millisecond internal read latency, and consolidated 3,200 servers down to about 800 when migrating off Memcached and Couchbase.

For agentic AI specifically, the integration pattern matters. LangGraph and similar orchestration frameworks generate bursty mixed read/write traffic against a central database. They use Aerospike as a durable low-latency memory layer for those workflows that save their state periodically, where the database performs thousands of concurrent multi-step sessions without becoming the bottleneck.

Tradeoffs to consider

Aerospike's data model is record-oriented with secondary indexes and a query language that is not full SQL. Teams expecting JOIN-heavy ad-hoc analytics will model differently or pair it with an analytical engine. The community is smaller than that of open-source databases. On the other hand, many production clusters run with 20 to 30 nodes serving workloads that would require hundreds of nodes elsewhere. Aerospike is strong when predictable tail latency at scale, fan-out resilience, and infrastructure cost are important.

2. PostgreSQL with pgvector

PostgreSQL has become the default starting point for AI applications that already have a relational backbone. The pgvector extension adds a native vector type, IVFFlat and HNSW indexes, multiple distance metrics, and the ability to combine ANN search with SQL filters in one query.³ That means embeddings, metadata, and transactional data are stored in one system with one set of tools, one backup, and one access control model.

Best fit workloads

For RAG prototypes, semantic search inside an existing app, and recommendation features that need to work with relational data, pgvector is hard to beat on simplicity. Teams routinely run it well into the millions of vectors. It also benefits from PostgreSQL's mature ecosystem, which includes replication, point-in-time recovery, logical decoding, monitoring, and a deep extension catalog including pgvectorscale for higher-throughput ANN search.

Tradeoffs to consider

However, it doesn’t scale well. Performance starts to degrade beyond 10 to 20 million vectors on one node. At 50 million vectors, purpose-built engines such as pgvectorscale and Qdrant are much faster.⁴ Pgvector also uses PostgreSQL's single-leader architecture, which has an 8 KB page limit that constrains indexed vector dimensions to roughly 2,000 unless you reduce them.⁵ It also has the problem of whether to filter before or after the ANN search, where naive filtered ANN queries return fewer results than expected. It’s still usable; it just means that pgvector isn’t the best choice at large scale.

3. Apache Cassandra

Cassandra is a wide-column NoSQL database built for linearly scalable writes and high availability across multiple data centers. Its architecture is a peer-to-peer ring with tunable consistency, multi-datacenter replication, and no one point of failure.

Best fit workloads

For ML, Cassandra is most often used as a real-time feature store and as the system of record behind event-driven model training. Netflix uses Cassandra alongside Elasticsearch and Iceberg in Marken, a scalable annotation service that stores ML model outputs and asset tags.⁶ 

Tradeoffs to consider

The venerable Cassandra's design assumptions show their age in some places. Lightweight transactions are limited, and secondary indexes are unreliable for many workloads, which is why teams that need strong consistency, global secondary indexes, or rich transactions sometimes layer additional systems on top. JVM-based runtimes also introduce garbage collection pauses that add latency. Compared with Aerospike, Cassandra trades some predictability and infrastructure efficiency for ecosystem breadth and proven multi-region tooling.

4. Milvus

Milvus is one of the largest open-source vector databases. It is a distributed system written in Go and C++, designed for scaling ANN search to billions of embeddings with GPU acceleration, multiple index types (HNSW, IVF, DiskANN, and quantized variants), and compute-storage separation that lets ingestion and query scale independently.

Best fit workloads

For pure vector search at high throughput, Milvus tends to lead on raw QPS in independent benchmarks. In comparative SQuAD-dataset benchmarks, Milvus reached around 46 QPS versus 4.7 for Qdrant in throughput-heavy scenarios, due to its batched ANN engine and segment-based indexing.⁷ It is the system most teams use when they expect to pass 100 million vectors and require GPU-backed similarity search.

Tradeoffs to consider

Milvus is a distributed system, typically running on Kubernetes, which requires work to manage components such as the meta store, message queue, and query, index, and data nodes. Smaller teams that do not need its scale will find it more work than self-contained alternatives. It is also less effective when application logic needs to combine vector search with rich relational filters; that use is better served by hybrid systems.

5. Weaviate

Weaviate is an open-source vector database emphasizing hybrid search, modular embedding pipelines, and a GraphQL-first query interface.⁸ It supports BM25 keyword search alongside vector search natively, has integrated modules for many embedding providers, and offers a managed cloud option. 

Best fit workloads

Weaviate fits applications where relevance depends on combining sparse and dense signals, such as enterprise search, product discovery, and RAG over heterogeneous collections of documents or data. Its hybrid search and reranking pipeline is incorporated rather than bolted on, which reduces the amount of integration code teams need to write. 

Tradeoffs to consider

Weaviate's modular architecture is convenient at a small scale but adds overhead at a large scale, and running the system yourself takes more effort than using a simple, all-in-one program.⁹ Hybrid search makes each query more complicated, so teams need to plan time to manage and optimize it. 

6. Qdrant

Qdrant is a Rust-based vector database that’s easy to run and offers low-latency filtered search and rich payload-based filtering. It runs as one simple program, scales horizontally through a distributed mode, and supports Hierarchical Navigable Small World (HSNW), a fast method for similarity search,  with custom segment management, vector compression to save memory and speed search, and more advanced search techniques such as multi-vector and late-interaction retrieval.

Best fit workloads

Qdrant works well for retrieval workloads that depend on metadata filters, such as search-with-filters in e-commerce, multi-tenant RAG with tenant-aware filtering, and agent memory layers where reads dominate. 

Tradeoffs to consider

Qdrant's throughput at large scale lags Milvus in some published tests.¹⁰ Concurrent write-heavy workloads also require careful configuration. The system is at its best between roughly 1 million and 50 million vectors in read-heavy or mixed workloads.

7. Vespa

Vespa is an AI search platform rather than a vector database. Originally built inside Yahoo and open-sourced in 2017, it unifies structured retrieval, full-text search, vector search, tensor operations, and machine-learned ranking in one product.  ML model inference runs on the data nodes. This avoids using separate systems for retrieval, which creates many back-and-forth network calls that make the process slower and more fragmented.  

Best fit workloads

For applications with retrieval, ranking, and inference that require limited latency, such as production RAG, large recommendation systems, and personalization, Vespa works well. Perplexity's RAG pipeline, which reported 22 million active users and 780 million monthly queries by May 2025, uses it.¹¹ Yahoo's deployment runs more than 150 applications serving close to a billion users at over 800,000 queries per second.¹² 

Tradeoffs to consider

However, you have to know how to use it. Schemas, ranking expressions, and tensor operations require thoughtful design, and teams that need only a vector-only RAG store may find it too hard. The trade is whether you want to build retrieval, ranking, and ML inference as separate services and integrate them, or have them incorporated into one platform; Vespa is the strongest answer to the second.

8. Elasticsearch and OpenSearch

Elasticsearch and the OpenSearch fork remain the default search engines in enterprise stacks. Both systems started as traditional keyword search engines built on Apache Lucene, and later developed into more advanced systems that use vector-based search, fast indexing, combine keyword and similarity search, and apply machine learning to rank results. This lets teams that already run search clusters add RAG and semantic search without a parallel database.

Best fit workloads

Hybrid search in document-heavy environments, such as log analytics that need anomaly detection, e-commerce search that needs both lexical and semantic relevance, and support content retrieval for chat assistants, fits these systems well. Their ability to summarize, categorize, and track data over time makes them useful for exploring data, monitoring models, and observing how AI systems perform. 

Tradeoffs to consider

Vector performance is not as good as that of purpose-built systems at large scale, and JVM-based execution introduces garbage collection variability that increases p99.9 latency. Index management for combined keyword and vector workloads requires capacity planning that goes beyond what teams may have done for keyword-only deployments. These systems are best suited when search is already an established workload and adding vector capability matters more than throughput.

9. ClickHouse

ClickHouse is a column-oriented OLAP database that has become a frequent backbone for ML training pipelines, real-time feature analytics, and model monitoring. It reduces data size as much as possible to save space, parallelizes query execution across cores and nodes, and reads data quickly while serving sub-second analytical queries on petabyte-scale datasets.

Best fit workloads

For the offline side of an ML stack, ClickHouse cleans and prepares data, then saves it to train AI models later. Teams use precomputed tables to create features as data arrives, combine data in a time-accurate way for training, and run analysis to explore features and evaluate models. ClickHouse acts as both an offline feature store and an online store for feature serving, using its log-structured merge tree to handle high write throughput while supporting concurrent low-latency queries.¹³ It does vector search within the same system for medium-sized workloads, so teams don’t need to add another tool when experimenting. 

Tradeoffs to consider

Because it’s OLAP, ClickHouse is not a transactional database. Point lookups against large tables are not its strong suit compared with operational stores; updates and deletes are best avoided in favor of insert-and-merge patterns, and concurrency is limited compared with row-oriented OLTP systems. Its place in an ML stack is alongside an operational database, not as a replacement for one.

10. Neo4j

Neo4j is the most widely used graph database. It stores data as nodes, relationships, and properties, traverses connections natively rather than via repeated joins, and queries through Cypher. For ML and AI, it often serves as the backbone of GraphRAG architectures, where structured relationships between entities augment the unstructured retrieval that vector search provides.

Best fit workloads

Neo4j works best for knowledge graphs for enterprise RAG, fraud rings, recommendation systems that exploit multi-hop relationships, and agentic systems that reason over structured ontologies. The system's LLM Knowledge Graph Builder turns unstructured text into queryable graphs and integrates with a RAG agent that combines vector search with graph traversal.¹⁴ In medical and legal domains, hybrid GraphRAG approaches that pair Neo4j with a vector store are more accurate and explainable than vector-only systems.

Tradeoffs to consider

Native graph databases trade horizontal scalability for traversal performance. Very large global graphs require careful sharding strategies, as well as a learning curve for Cypher. The system is stable and well-tested in production, but Neo4j is rarely the only database in a production AI stack. It is most effective when paired with an operational store and a vector engine, with graph traversal reserved for the queries that need it.

The cost of overprovisioning to compensate for unpredictable performance

Often, ML infrastructure has to scale a database more than usual to keep tail latency predictable. It does, up to the point where it doesn’t. 

Server count grows linearly with the chosen safety margin, but the more servers there are, the more work there is. More nodes mean more rebalancing events, more partial failures to handle, more hot-shard mitigation, more failure domains to worry about during a deploy, and more places for a noisy neighbor to surface. Cloud bills grow even faster because processing, network, and managed-service overhead scale together. Teams that doubled their cluster size to keep p99 reliable frequently find that p99.9 did not improve much because the underlying variance source, such as cache miss patterns, garbage collection pauses, or compaction storms when a database tries to merge too much data at once, weren’t helped by adding nodes.

The architectural alternative is to choose systems whose performance is more predictable. Systems that read from persistent storage at constant latency, do not depend on cache hit rate to meet service level objectives, and that hold tail behavior steady even when access patterns change, mean teams don’t have to overprovision. 

This saves money. Migrations off cache-and-database stacks onto Aerospike reduced 3,200 servers to 800 at Criteo, or one application replacing alternatives with infrastructure savings projected at $10 million. Even a small amount of this makes a big difference when it spreads across multiple databases in a machine learning system. 

Choosing between specialized and general-purpose systems

The temptation in AI architecture is to pick the best-in-class system for each workload, such as a specialized vector database, a graph database, an OLAP engine, an operational key-value store, and a search engine. Each looks great by itself, but combined, they create different problems.

Every additional system adds its own problems, its own observability, its own backup method, its own access control model, and its own consistency semantics. Data has to be replicated between them, which means reconciliation logic, change-data-capture pipelines, and potential data inconsistencies. Engineers spend more time connecting the systems, and the mean time to debug an incident grows with the number of hops between systems.

The opposite extreme, forcing one system to serve all workloads, doesn’t work when the workloads are different. 

The compromise is to consolidate when you can. An operational database that handles low-latency feature serving, vector retrieval, and agent memory in one cluster reduces three systems to one. A columnar engine that doubles as an offline feature store and a model monitoring backend eliminates two more. A graph database for the queries that need traversal, and a search engine for the queries that need full-text relevance, fill in the gaps without proliferating.

Reducing staff work

Latency, throughput, and cost show up in benchmarks, but the work staff has to do doesn’t. As ML systems age, a database that was easy to start up becomes the database that is hard to upgrade, resize without downtime, rebalance, back up, and or evacuate when a region degrades.

Properties that make databases easier for staff include zero-downtime cluster expansion and shrinkage, online schema and index changes, predictable recovery from node failure, predictable behavior during compaction or vacuum, and being able to see per-shard tail latency. They also include installation footprint, number of dependencies,  the number of components running per cluster, and the team's ability to figure out what the database is doing under load. Two databases may have similar published latency numbers, but one might be much more work than the other, which adds up over the years. 

Choosing a database for ML and AI workloads

The best database for ML and AI makes the rest of the architecture simpler under stress. That means predictable tail latency that holds as fan-out depth grows, throughput that does not collapse when clients use the system differently, vector and operational data that can be stored together when that helps, and a system that doesn’t add more work as it grows. 

Aerospike fits the profile when those constraints are the most important. Its patented Hybrid Memory Architecture keeps p99 reads in the sub-millisecond range, whether data is hot or cold; its strong-consistency mode offers ACID transactions when correctness matters, and its vector search and XDR replication let teams keep features, embeddings, and operational records in one place across regions. The production track record among organizations such as PayPal, Adobe, Wayfair, Airtel, Criteo, and Sony Interactive Entertainment is the strongest evidence that the architecture holds under production.