As AI-driven search systems and Retrieval-Augmented Generation (RAG) applications grow, vector databases are scaling to store billions of embeddings. While this enables more intelligent retrieval, it also introduces major challenges in latency, throughput, and scalability.
Page Contents
If your vector search pipeline slows down as your dataset grows, you’re not alone. The key to keeping it fast and reliable lies in two main strategies: sharding and latency optimisation.
In this guide, we’ll explore how to scale vector search pipelines, what sharding means for distributed embeddings, and practical fixes for improving query performance.
Understanding the Vector Search Pipeline
Before scaling, let’s recap how a vector search pipeline works:
- Text Input: A user query or document is converted into an embedding (vector).
- Vector Indexing: The embedding is stored in a vector database (like Pinecone, Qdrant, Weaviate, or Milvus).
- Similarity Search: When a new query comes, it’s compared against all stored embeddings using distance metrics (cosine, dot-product, etc.).
- Retrieval & Ranking: The nearest neighbours are retrieved and optionally reranked by a model.
At a small scale, this runs smoothly. But as embeddings grow into the millions or billions, search time, storage, and network costs spike dramatically.
The Scalability Challenges
As your dataset expands, you’ll likely hit these bottlenecks:
- High Latency: Each query must compare against a massive number of vectors.
- Memory Limits: GPUs or CPUs can’t hold all vectors in memory.
- Load Imbalance: Some nodes handle too many requests while others remain idle.
- Update Complexity: Inserting, deleting, or reindexing vectors becomes slower.
That’s where sharding and latency optimisations come into play.
What Is Sharding in Vector Databases?
Sharding is the process of splitting your vector data into smaller partitions (shards) and distributing them across multiple servers or nodes.
Think of it like splitting a huge library into sections — each handled by a separate librarian. Instead of one person searching every book, multiple librarians search their own sections in parallel.
Benefits of Sharding
- Faster Querying: Parallel searches across shards reduce overall latency.
- Scalability: You can add new shards as data grows.
- Fault Tolerance: If one node fails, others can still serve requests.
- Efficient Resource Utilisation: Balances load across servers.
Sharding Strategies for Vector Search
1. Hash-Based Sharding
Each vector (or document ID) is assigned to a shard using a hash function.
✅ Fast, simple, and evenly distributed.
❌ Doesn’t group similar vectors, which may impact recall.
2. Semantic Sharding
Vectors are clustered based on similarity before assigning shards.
✅ Higher recall and localised search performance.
❌ More complex to maintain and rebalance.
3. Hybrid Sharding
Combines hash and semantic logic — hash for even distribution, semantic for query accuracy.
✅ Balanced trade-off between speed and relevance.
Optimising for Latency
Even with sharding, you’ll still face performance issues if your retrieval pipeline isn’t optimised. Let’s explore some fixes:
1. Approximate Nearest Neighbour (ANN) Search
Instead of comparing every vector, ANN algorithms (like HNSW, IVF, PQ) find close enough results quickly — dramatically reducing search time.
Used by Pinecone, FAISS, Milvus, and Weaviate.
2. Caching Popular Queries
Cache embeddings and top results for frequent queries.
Use Redis or in-memory caching to avoid repeated vector comparisons.
3. Batching Queries
Group multiple queries into a single request to minimise I/O overhead.
4. Reduce Dimensionality
High-dimensional embeddings increase computation cost.
Use PCA or model-specific compression (e.g., OpenAI text-embedding-3-small vs 3-large).
5. Async and Parallel Querying
Send parallel requests to shards using async I/O (Python’s asyncio, Node.js’s Promise.all).
6. Proximity Routing
Send queries only to relevant shards instead of all.
Used in Hierarchical Navigable Small World (HNSW) graph-based search.
Infrastructure Considerations
| Factor | Recommendation |
|---|---|
| Hardware | Use SSDs over HDDs for faster read/write |
| Network | Deploy shards close to your API gateway |
| Autoscaling | Add new shards dynamically using cloud auto-scaling |
| Monitoring | Track latency and throughput with Prometheus + Grafana |
Example Architecture
+--------------------------+
| Query API |
+-----------+--------------+
|
v
+--------------------------+
| Shard Router / Load Balancer |
+-----------+-----------+----------+
| | | |
v v v v
Node 1 Node 2 Node 3 Node 4
(Vector DBs / ANN Indexes)
Real-World Example
Let’s say you’re using Pinecone or Qdrant for a growing RAG application.
- You start with a single index (~1M vectors).
- As data grows beyond 10M vectors, queries slow down.
- You introduce semantic sharding based on data topics (e.g., “Tech,” “Finance,” “Health”).
- Each shard is stored in separate nodes and searched in parallel.
- Latency drops from 1.2s to 250ms.
Combine this with query caching and ANN search, and your system becomes near real-time, even at scale.
Best Practices
✅ Use ANN indexing (HNSW, PQ) to improve retrieval times.
✅ Implement query routing to only relevant shards.
✅ Rebalance shards periodically for even data distribution.
✅ Monitor and log latency metrics per shard.
✅ Test scaling strategies in staging before production rollout.
The Future of Vector Search Scalability
With the rapid growth of multimodal embeddings (text, image, audio), scalability challenges will multiply. Future systems will integrate adaptive sharding, vector compression, and GPU-accelerated inference for real-time retrieval across billions of embeddings.
The next wave of vector databases will prioritise elastic scaling, where shards automatically redistribute and scale without manual intervention.
Parvesh Sandila is a passionate web and Mobile app developer from Jalandhar, Punjab, who has over six years of experience. Holding a Master’s degree in Computer Applications (2017), he has also mentored over 100 students in coding. In 2019, Parvesh founded Owlbuddy.com, a platform that provides free, high-quality programming tutorials in languages like Java, Python, Kotlin, PHP, and Android. His mission is to make tech education accessible to all aspiring developers.