
Weaviate
Milvus
Vespa.ai
Pinecone
ElasticSearch
Zilliz
Algolia
Qdrant is a high-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Compute various size metrics for a Git repository, flagging those that might cause problems - github/git-sizer

Which is more popular?
Based on our record, Qdrant seems to be a lot more popular than git-sizer. While we know about 64 links to Qdrant, we've tracked only 1 mention of git-sizer.
Website, pricing, platforms and company facts side by side.
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| Website | qdrant.tech | github.com |
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| Company | 2021 | — |
| Listed in |
In their own words, as submitted to SaaSHub.


Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be...
No description of git-sizer yet.
What each product offers, as listed by its team.


Possible disadvantages
An editorial look at what each product does well and who it suits.


Overall verdict
Why this product is good
Recommended for
Overall verdict
Why this product is good
Recommended for
How often each product is chosen within a category, 0–100% relative to the other.


As answered by people managing Qdrant and git-sizer.
Qdrant's answer
Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.
Qdrant's answer
Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.
Qdrant's answer
Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.
Share your experience with using Qdrant and git-sizer. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.


If you build on the JVM and want to use Qdrant, the official client is io.qdrant:client — and it's built for Java. Every call returns a ListenableFuture, requests are assembled with protobuf builders, and it drags a gRPC/Netty stack onto... - Source: dev.to / 2 months ago
The stack runs on Qdrant for vector storage, Ollama for local embeddings, and optional Neo4j for a knowledge graph that I added later. I also set it up to route different operations to the best LLM for each task. It provides eleven tools... - Source: dev.to / 7 months ago
Qdrant: Open-source vector database optimized for hybrid search and easy integration with ML workflows. - Source: dev.to / 10 months ago
Also there’s a cool project from GitHub you can use to help understand the size of git’s objects in your git repo https://github.com/github/git-sizer. This might help you determine what the best cloning strategy could be. Source: almost 5 years ago
When comparing Qdrant and git-sizer, you can also consider the following products.


Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
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Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away.
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Elasticsearch is an open source, distributed, RESTful search engine.
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