Qdrant in Machine Learning

The Gap Between ANN Libraries and Production Databases

By 2021, the ML ecosystem had excellent ANN index libraries -- FAISS from Meta, HNSWlib from Yashunin, ScaNN from Google. But these libraries operated as in-process data structures. They had no persistence layer, no replication, no metadata filtering, and no API server. If you wanted to use FAISS in production, you had to build all of that plumbing yourself: a REST server, a write-ahead log, a snapshot mechanism, a way to handle concurrent reads and writes. Most teams ended up building a fragile, custom vector service on top of FAISS and spending more time maintaining the wrapper than tuning the index.

Qdrant was created to fill this gap -- not by wrapping an existing library but by building a purpose-designed vector database from scratch in Rust. The choice of Rust was deliberate: it provides C/C++ level performance with memory safety guarantees, zero-cost abstractions, and no garbage collector. This last point matters more than people realize. In Java-based or Go-based systems, garbage collection pauses can cause unpredictable latency spikes -- exactly what you do not want in a real-time retrieval system serving user-facing queries.

The Filtering Problem

One of the most persistent pain points with early vector databases was the interaction between vector similarity search and metadata filtering. Consider a multi-tenant RAG system where each query must retrieve documents belonging only to the querying user's workspace. The naive approach -- run ANN search first, then filter results by tenant ID -- can return fewer than $k$ results when the tenant's documents are a small fraction of the total corpus. The alternative -- filter first, then search only within matching documents -- requires building separate indices per filter combination, which is combinatorially explosive.

Qdrant's filterable HNSW implementation addresses this head-on. It extends the HNSW graph with additional edges conditioned on payload values, ensuring that the graph remains navigable even under restrictive filter predicates. This means you get both vector similarity and metadata filtering in a single graph traversal -- no pre-filtering starvation, no post-filtering misses. This was a genuine architectural innovation, not just a marketing feature.

From Niche to Mainstream

Qdrant's trajectory mirrors the broader explosion of vector database adoption driven by the RAG paradigm. When ChatGPT popularized LLM-powered applications in late 2022, every team building an AI product suddenly needed a vector database. Qdrant was well-positioned: it was already open-source, had a clean API, supported Docker deployment in minutes, and offered a managed cloud service. By 2025, Qdrant had surpassed 27,000 GitHub stars and was being used by enterprises including Tripadvisor (1B+ reviews indexed), HubSpot (powering Breeze AI), Sprinklr (enterprise CX platform), and Dailymotion (420M+ videos).

Key Takeaway: Qdrant exists because production vector search requires more than an ANN algorithm -- it requires persistence, filtering, replication, and operational tooling. Qdrant provides all of this in a single, Rust-based binary with predictable performance characteristics.

The Rust Advantage: Predictable Speed

Here is the mental model I use for Qdrant: imagine a library with an extremely fast, never-sleeping librarian who speaks geometry. You hand the librarian a description (your query vector), and within milliseconds, the librarian navigates a network of interconnected bookshelves (the HNSW graph) to find the most similar books. But here is what makes this librarian special -- they can simultaneously check tags on each book (payload filtering) as they navigate, without slowing down. And because this librarian is built in Rust, they never pause to "take a break" (no garbage collection pauses), giving you consistent sub-10ms response times.

The Rust foundation is not just a marketing talking point. In garbage-collected runtimes (Java, Go, Python), periodic GC pauses can cause P99 latency to spike 10-50x above the median. For a vector database serving user-facing queries, a 200ms spike on a 5ms median query is unacceptable. Qdrant's Rust implementation eliminates this entire class of latency variance. When Sprinklr benchmarked Qdrant against Elasticsearch, they found Qdrant delivered P99 latency of 20ms on 1 million vectors -- consistently.

Points, Payloads, and Collections

Qdrant's data model is refreshingly simple. The core abstraction is a point -- a combination of a vector (or multiple named vectors), a JSON payload, and a unique ID. Points live in collections, which are analogous to tables in a relational database. Each collection has a fixed vector configuration (dimensions, distance metric) but accepts arbitrary JSON payloads.

This design means you store your embeddings and their metadata together, query them together, and filter on both simultaneously. No joins, no separate metadata stores, no impedance mismatch. For an Indian e-commerce company like Myntra building a visual search feature, a single point might contain an image embedding, a product category, a price range, and an availability flag -- all queryable in one call.

Why "Approximate" Is Good Enough

Like all vector databases, Qdrant uses approximate nearest neighbor (ANN) search. The word "approximate" can be unsettling, but here is the reality: for most ML applications, a recall@10 of 0.95-0.99 is indistinguishable from exact search in terms of downstream task quality. The speedup, however, is enormous -- from $O(n)$ brute-force to $O(\log n)$ graph traversal. Qdrant gives you knobs (ef parameter in HNSW) to dial the recall-speed tradeoff to exactly the level your application needs.

Mathematical Foundation

Qdrant implements vector search over a collection $C = \{p_1, p_2, \ldots, p_n\}$ where each point $p_i = (\text{id}_i, v_i, m_i)$ consists of a unique identifier, a vector $v_i \in \mathbb{R}^d$, and a JSON metadata payload $m_i$.

Core Search Operation

Given a query vector $q \in \mathbb{R}^d$, an optional filter predicate $\phi(m)$ over payloads, and a result count $k$, Qdrant returns:

$S = \underset{\substack{p_i \in C \\ \phi(m_i) = \text{true}}}{\operatorname{arg\,top-}k} \; \text{sim}(q, v_i)$

where $\text{sim}(\cdot, \cdot)$ is one of the supported distance functions.

Supported Distance Metrics

Qdrant supports four distance metrics:

• Cosine similarity: $\text{sim}(q, v) = \frac{q \cdot v}{\|q\| \cdot \|v\|}$. Normalized to $[0, 1]$ internally (Qdrant stores pre-normalized vectors and uses dot product for speed).

• Dot product (Inner Product): $\text{sim}(q, v) = q \cdot v$. Use when vector magnitudes carry information (e.g., popularity-weighted embeddings).

• Euclidean (L2): $d(q, v) = \sqrt{\sum_{j=1}^{d} (q_j - v_j)^2}$. Lower distance means higher similarity.

• Manhattan (L1): $d(q, v) = \sum_{j=1}^{d} |q_j - v_j|$. Added in recent versions for specific use cases.

HNSW Complexity

The HNSW index provides:

• Search complexity: $O(\log n)$ average case, where $n$ is the number of points
• Insert complexity: $O(\log n)$ per point (amortized)
• Space complexity: $O(n \cdot (d \cdot 4 + M \cdot 8))$ bytes, where $M$ is the number of connections per node and $d$ is the vector dimension

The key tuning parameters are:

• $M$ (m): Number of bidirectional links per node. Higher $M$ increases recall and memory. Default: 16.
• $\text{ef}_{\text{construct}}$: Size of the dynamic candidate list during index construction. Higher values produce a higher-quality graph. Default: 100.
• $\text{ef}$ (ef): Size of the dynamic candidate list during search. Controls the recall-speed tradeoff at query time.

Quantization Memory Savings

Qdrant supports multiple quantization strategies with different compression ratios:

• Scalar quantization: Maps each float32 to uint8, achieving 4x compression. Memory per vector: $d$ bytes.
• Binary quantization: Maps each float32 to 1 bit, achieving 32x compression. Memory per vector: $d/8$ bytes. Works best with high-dimensional embeddings (1536+) from models like OpenAI text-embedding-3-large.
• Product quantization: Divides the vector into $m$ sub-vectors and quantizes each to a codebook index, achieving variable compression (typically 8-64x).

The memory formula for a quantized collection with $n$ vectors of dimension $d$:

$\text{Memory}_{\text{scalar}} \approx n \times d \times 1 \text{ byte}$ $\text{Memory}_{\text{binary}} \approx n \times \frac{d}{8} \text{ bytes}$

For example, 10 million 1536-dimensional vectors with binary quantization: $10^7 \times \frac{1536}{8} \approx 1.92$ GB -- compared to $10^7 \times 1536 \times 4 \approx 61.4$ GB uncompressed. That is a 32x reduction.

Practical Note: Binary quantization with rescoring (where the full-precision vectors are used to re-rank the top candidates from the quantized search) achieves near-lossless recall while retaining most of the speed and memory benefits. Qdrant supports this out of the box with the always_ram: true and rescore: true configuration options.

Bringing It All Together

Qdrant is a Rust-based vector similarity search engine that stands out in the crowded vector database landscape through three key differentiators: filterable HNSW for combined vector + metadata search in a single graph traversal, predictable low-variance latency from Rust's zero-GC runtime, and unique recommendation/discovery APIs that go beyond basic similarity search.

For production ML systems, Qdrant serves as the retrieval backbone in RAG pipelines, recommendation engines, and semantic search applications. Its architecture -- built around segments, write-ahead logging, background optimization, and optional Raft-based distributed consensus -- provides the persistence, durability, and operational features that raw ANN libraries lack. With quantization options ranging from 4x (scalar) to 32x (binary) compression, Qdrant can adapt to memory budgets from startup-scale to enterprise-scale deployments.

The key engineering decisions when deploying Qdrant are: (1) quantization strategy (match to your embedding dimensionality -- scalar for <1024-dim, binary for 1536+), (2) multi-tenancy approach (payload-based with is_tenant flag for most SaaS apps, tiered for heterogeneous tenant sizes), (3) HNSW tuning (m, ef_construct, payload_m based on your recall requirements), and (4) deployment topology (single node for <10M vectors, 3+ node cluster with cross-AZ replication for production HA). Companies like Tripadvisor (1B+ reviews), Dailymotion (420M+ videos), HubSpot (Breeze AI), and Sprinklr have validated these patterns at scale.

Bottom line: If you are building a production ML system that needs vector retrieval with rich metadata filtering, multi-tenancy, or recommendation capabilities, and you want the performance predictability of Rust without the operational complexity of a multi-component distributed system, Qdrant is the strongest choice in the current vector database landscape.