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DiskANN Alternatives
Similar projects and alternatives to DiskANN
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InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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Redis
For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.
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Milvus
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
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Stream
Stream - Scalable APIs for Chat, Feeds, Moderation, & Video. Stream helps developers build engaging apps that scale to millions with performant and flexible Chat, Feeds, Moderation, and Video APIs and SDKs powered by a global edge network and enterprise-grade infrastructure.
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TimescaleDB
A time-series database for high-performance real-time analytics packaged as a Postgres extension
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nodejs-pubsub
Node.js client for Google Cloud Pub/Sub: Ingest event streams from anywhere, at any scale, for simple, reliable, real-time stream analytics.
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pgai
A suite of tools to develop RAG, semantic search, and other AI applications more easily with PostgreSQL
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pgvectorscale
A complement to pgvector for high performance, cost efficient vector search on large workloads.
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pgaidocs
Discontinued [GET https://api.github.com/repos/timescale/pgaidocs: 404 - Not Found // See: https://docs.github.com/rest/repos/repos#get-a-repository]
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
DiskANN discussion
DiskANN reviews and mentions
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Unpacking DiskANN: My Technical Journey Through Billion-Scale Vector Search
What happens when vector datasets exceed what RAM can handle? This question drove my investigation into DiskANN – an SSD-optimized approach for massive-scale similarity search. Unlike traditional methods like HNSW that hit scalability ceilings around 100M vectors, DiskANN achieves billion-scale indexing by strategically leveraging disk storage. I’ll share how it balances latency, recall, and cost through architectural innovations.
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Pushing Billion-Scale Vector Search Beyond RAM Limits with DiskANN
The Memory Wall Problem Most vector indexes prioritize RAM for low latency. HNSW, for example, achieves 95% recall at <5ms for 100M vectors but requires ~500GB RAM. At 1B vectors, RAM costs exceed $10k/month on cloud instances—prohibitively expensive for many teams. DiskANN flips this model:
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PostgreSQL Maximalism
Learns from Microsoft's DiskANN: "Graph-structured Indices for Scalable, Fast, Fresh and Filtered Approximate Nearest Neighbor Search"
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Join us for the Open Source AI Challenge with pgai and Ollama: $3,000 in Prizes!
A new index type called StreamingDiskANN, inspired by the DiskANN algorithm, based on research from Microsoft.
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DiskANN Implementation in Rust + Easy NN Search
Hi! I have been noodling away at a re-implementation of the original C++ DiskANN project as well as packaging the latest advances in embedding generation. The rough repo is here and will remain licensed as Apache-2.0!
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Pinecone raises $100M Series B
Spot on. There is zero moat and the self-hosted alternatives are rapidly improving (if not better) than Pinecone. There are good open-source contributions coming from bigcorp beyond Meta too, e.g., DiskANN (https://github.com/microsoft/DiskANN).
- SSD-Based Vector Indices
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A note from our sponsor - Stream
getstream.io | 14 Jul 2025
Stats
microsoft/DiskANN is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of DiskANN is C++.