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# Qdrant 2024 Roadmap
Hi!
This document is our plan for Qdrant development in 2024.
Previous year roadmap is available here:
* [Roadmap 2023](roadmap-2023.md)
* [Roadmap 2022](roadmap-2022.md)
Goals of the release:
* **Maintain easy upgrades** - we plan to keep backward compatibility for at least one minor version back (this stays the same in 2024).
* That means that you can upgrade Qdrant without any downtime and without any changes in your client code within one minor version.
* Storage should be compatible between any two consequent versions, so you can upgrade Qdrant with automatic data migration between consecutive versions.
* **Make serving easy on multi-billion scale** - Qdrant already can serve billions of vectors cheaply, using techniques as quantization. In the 2024 year, we plan to make it even easier to scale it.
* Faster and more reliable replications
* Out-of-the-box read-write segregation
* Specialized nodes and multi-region deployments
* **Better ecosystem** - in 2023 we introduced [fastembed](https://github.com/qdrant/fastembed) to simplify embedding generation but keep it out of the core.
In 2024 we plan to continue this trend: implement more advanced and specialized tools while keeping the core focused on the main use-case.
* Advanced support for sparse vectors - we plan to make sparse vectors inference as fast and easy as the dense one.
* Hybrid search out of the box with no overhead - something you can build with Qdrant today, but in a more convenient way.
* Practical RAG - battle-tested RAG practices with production-grade implementation.
* **Various similarity search scenarios** - develop vector similarity beyond just kNN search.
## How to contribute
If you are a Qdrant user - Data Scientist, ML Engineer, or MLOps, the best contribution would be the feedback on your experience with Qdrant.
Let us know whenever you have a problem, face an unexpected behavior, or see a lack of documentation.
You can do it in any convenient way - create an [issue](https://github.com/qdrant/qdrant/issues), start a [discussion](https://github.com/qdrant/qdrant/discussions), or drop up a [message](https://discord.gg/tdtYvXjC4h).
If you use Qdrant or Metric Learning in your projects, we'd love to hear your story! Feel free to share articles and demos in our community.
For those familiar with Rust - check out our [contribution guide](../CONTRIBUTING.md).
If you have problems with code or architecture understanding - reach us at any time.
Feeling confident and want to contribute more? - Come to [work with us](https://qdrant.join.com/)!
## Core Milestones
* 📃 Hybrid Search and Sparse Vectors
* [x] Make Sparse Vectors serving as cheap and fast as Dense Vectors
* [x] Introduce Hybrid Search into Qdrant Client
* [x] Dense + Sparse + Fusion in one request
* [ ] Customizable Re-Ranking
---
* 🏗️ Scalability
* [x] Faster shard synchronization
* [x] Non-blocking snapshotting
* [x] Incremental replication
* [ ] Specialized nodes
* [ ] Read-only nodes
* [ ] Indexing nodes
* [ ] Multi-region deployments
* [ ] Automatic replication over availability zones
---
* ⚙️ Performance
* [ ] Specialized vector indexing for edge cases HNSW is not good at
* [ ] Text-index performance and resource consumption improvements
* [ ] IO optimizations for disk-bound workloads
---
* 🏝️ New Data Exploration techniques
* [ ] Improvements in Discovery API to support more use-cases
* [ ] Diversity Sampling
* [ ] Better Aggregations
* [ ] Advanced text filtering
* [ ] Phrase queries
* [ ] Logical operators
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