Buying snapshot
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| Decision point | What Aerospike states |
|---|---|
Deployment options | Aerospike Cloud, a fully managed, self-service service in hyperscale clouds, peered to your VPC; Managed Service, where Aerospike SREs run the database in your preferred hyperscale cloud within your VPC; or Self Managed, using official container images, Kubernetes, VMs or bare metal. |
Self-managed install | 64-bit GNU/Linux, such as Red Hat Enterprise Linux 8/9, Amazon Linux 2023, Debian 11 or later and Ubuntu 20.04 or later. For production, the README prefers building and installing the .rpm or .deb package for your distribution and running the daemon with systemctl. |
Scale and replication | From a two-node setup to globally distributed replicas using XDR. Develop locally with tools such as Docker. |
Integrations | Spark, Kafka and more, with streaming ingestion and CDC connector options. |
Tooling | Voyager, a visual workspace to query, troubleshoot and visualize data and connect Claude, Cursor and other coding agents to a cluster. Free preview for Mac, Windows and Linux. |
Our take
Choose Aerospike when an application must read and write large datasets with tightly bounded latency under heavy load, as in bidding, fraud checks, payments or AI lookups, and you are ready either to run SSD-backed nodes yourself or to have Aerospike operate the database in your cloud.
Evaluate Aerospike
Comparisons
Where it stands out
Memory-like latency from SSDs. Aerospike says its patented Hybrid Memory Architecture gives memory-like latency with the durability and low cost of SSDs, and that its deterministic design keeps response times tightly bounded under heavy, prolonged load. It cites Criteo, which replaced a database-plus-cache setup with Aerospike and cut its servers by 75%.
Consistency you can choose. Run in high-availability mode with no replica divergence, or in strong-consistency mode with ACID transactions, even with a replication factor of 2.
Logic close to the data. Collection data types and server-side functions run next to the data; the server includes a Lua runtime and geospatial indexing built on Google's S2 Geometry Library.
Product screenshot
Trade-offs to weigh
Running it yourself is Linux operations work. The sample configuration files are meant to be adapted, for example network addresses, namespaces and storage engine settings, and tuned for your system; system limits such as open file descriptors may need raising. Builds from source send telemetry unless AEROSPIKE_TELEMETRY is set to FALSE.
YAML configuration is still experimental. Besides the traditional .conf format, the server accepts YAML with JSON schema validation only as an experimental feature, and output from the asconfig conversion tool must be edited by hand before the server accepts it.
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
1 GitHub commits 90d1.4k GitHub stars0 vulnerabilities across 2 packages
See all signals from 8 sources
npm · aerospike@7.0.1 · PyPI · aerospike@19.3.0
Alternatives to Aerospike
The reviewed substitutes for Aerospike among the vector databases, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Redis Vector Search
- Redis Vector Search and Aerospike bring vector search to fast operational databases used for real-time applications. Redis Vector Search runs similarity queries in memory next to Redis data structures, while Aerospike is a multi-model key-value, document and vector database designed for predictable low latency on very large datasets.Applies to: Choosing a vector store for embedding search in a retrieval or agent application.
- MongoDB Atlas Vector Search
- MongoDB Atlas Vector Search and Aerospike add vector search to an operational database, so embeddings live in the same system as application data. MongoDB Atlas Vector Search is part of the managed MongoDB document service, while Aerospike is a multi-model key-value and document database built for real-time workloads with predictable low latency at large scale.Applies to: Choosing a vector store for embedding search in a retrieval or agent application.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Marqo
- Marqo optimises search conversion using click-stream, purchase and event data, creating a personalised experience that knows what your customers are looking for - better than they do.Applies to: For teams that build their own product search on a vector store rather than buying a hosted ecommerce search platform.
- ChromaDB
- ChromaDB is an alternative to Aerospike for LLM and RAG prototyping workloads centered on embeddings. **ChromaDB** is an open-source embedding database built for LLM applications, with Python-native APIs intended to make RAG prototypes straightforward to build.
- Vald
- Vald is an alternative to Aerospike for Kubernetes-operated approximate-nearest-neighbor vector search workloads. We recommend Vald over Aerospike for organizations already standardized on Kubernetes that want dedicated distributed ANN search and accept the associated platform operations.