⚡ World’s Fastest Vector Database for AI & RAG
Casper is a high-performance Vector Search Database, perfectly suited for high-load search systems and AI applications (RAG). It provides a robust and scalable solution to store, search, and manage vectors efficiently.
Casper is built using Rust 🦀 for performance and reliability. Casper clients Python • Go • Rust
Casper is the fastest vector database in our internal benchmarks. It consistently outperforms Qdrant across Top@K workloads and both f32 and i8 quantizations. Notably, Qdrant is widely recognized as the leading open‑source engine and demonstrates state‑of‑the‑art throughput versus other databases (e.g., Weaviate, Milvus), as shown in their published results: Qdrant benchmarks. Surpassing Qdrant therefore places Casper ahead of the current open‑source performance leader.
In practice, Casper delivers up to an order‑of‑magnitude higher RPS compared to Qdrant on our datasets, which translates directly into substantial infrastructure savings: fewer CPU cores and instances to achieve the same SLA, lower memory pressure, and reduced total cost of ownership due to more efficient use of compute resources. Casper is the ideal solution for high-load systems, real-time search, and AI & RAG.
Conclusion: Casper achieves performance unattainable for other databases under comparable conditions, requires fewer compute resources at the same load, and materially reduces infrastructure costs through more efficient CPU and memory utilization.
Hardware:
- CPU: Intel Core i7-13700HX (16 cores / 24 threads)
- Memory: 32 GB RAM
Dataset: deep-image-96-angular.hdf5
- Vectors: 9,990,000
- Dimension: 96
- Metric: Inner Product (vectors are L2-normalized, IP == cosine)
HNSW
- m: 16
- ef construct: 200
Qdrant configured with quantile 0.99 (for int8), always ram enabled.
Search-time parameter. For every measurement in the tables below we explicitly set ef_search = limit on both engines (for Qdrant via params.hnsw_ef, overriding its server-side default of max(limit, 128)). This is the smallest valid HNSW ef and the most apples-to-apples comparison: both engines do the minimum amount of graph exploration the algorithm allows.
Index granularity (why Qdrant recall is higher). Casper builds a single monolithic HNSW index per collection. Qdrant splits the collection across multiple segments (segments_count: 8 in this benchmark) and runs HNSW search independently in every segment, then merges the per-segment top‑K on the coordinator. With per-segment hnsw_ef = limit, Qdrant effectively examines segments_count × limit = 8 × limit candidates per query — eight times more than Casper for the same nominal ef. The higher recall Qdrant shows at every K in the tables therefore reflects this storage organization, not better HNSW graph quality; the same effect is the reason its RPS is correspondingly lower (more work per request).
Requests per second, RPS
| Engine | Top@10 | Top@100 | Top@1k | Top@10k | Top@100k |
|---|---|---|---|---|---|
| Casper | 106.87 k | 30.91 k | 4.363 k | 486 | 53 |
| Qdrant | 17.02 k | 5.488 k | 730 | 85 | 9 |
| Speedup | 6.3x | 5.6x | 6.0x | 5.7x | 6.1x |
Recall
| Engine | Top@10 | Top@100 | Top@1k | Top@10k | Top@100k |
|---|---|---|---|---|---|
| Casper | 0.606 | 0.854 | 0.957 | 0.987 | 0.995 |
| Qdrant | 0.762 | 0.953 | 0.993 | 0.999 | 1.000 |
Requests per second, RPS
| Engine | Top@10 | Top@100 | Top@1k | Top@10k | Top@100k |
|---|---|---|---|---|---|
| Casper | 126.63 k | 45.99 k | 6.756 k | 665 | 65 |
| Qdrant | 12.60 k | 5.698 k | 923 | 100 | 10 |
| Speedup | 10.1x | 8.1x | 7.3x | 6.6x | 6.8x |
Recall
| Engine | Top@10 | Top@100 | Top@1k | Top@10k | Top@100k |
|---|---|---|---|---|---|
| Casper | 0.578 | 0.820 | 0.921 | 0.957 | 0.975 |
| Qdrant | 0.729 | 0.914 | 0.959 | 0.975 | 0.985 |
Casper features a highly efficient HNSW (Hierarchical Navigable Small World) index, providing fast and accurate similarity search.
Casper supports multiple distance metrics:
- Euclidean
- L2SQ
- Cosine
- Inner-Product
Quantizations: f32 (full precision), i8 scalar quantization — reducing memory footprint and improving search performance.
- F32
- I8
Casper is currently completely free. You can use the following free API token to run Casper:
export API_TOKEN=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJleHAiOjE3OTMyOTAzNTMsImZyZWUiOnRydWV9.GxqiVw5kPzmPb25vo2CMOEwnBhjTH_GTAHeDg_nhlIQTo quickly get started with Casper, follow these steps:
1. Download the latest release:
wget https://github.com/casper-vdb/casper/releases/download/v0.0.1/casper-x86_64-unknown-linux-gnu.tar.gz2. Extract the downloaded archive:
tar -xzvf casper-x86_64-unknown-linux-gnu.tar.gz3. Set API token:
export API_TOKEN=<YOUR_API_TOKEN>4. Run Casper:
./casperNow you're ready to use Casper and explore its features!
1. Pull the image:
docker pull alexryzhickov/casper:latest2. Set API token:
export API_TOKEN=<YOUR_API_TOKEN>3. Run the container:
docker run -d --name casper -p 8080:8080 -p 50051:50051 -e API_TOKEN="$API_TOKEN" alexryzhickov/casper:latest4. Verify health:
curl http://localhost:8080/healthCasper provides client libraries for several programming languages:
Casper exposes an HTTP & GRPC API for managing collections, indexing (HNSW), inserts/updates/deletes, and search. For full endpoint descriptions and curl examples, see the documentation:
- Advanced Vector Search: High-speed retrieval for complex AI-driven applications.
- Scalability: Designed to handle large-scale data with ease.
- Robust and Reliable: Built in Rust for high performance even under heavy loads.