What I Learned Comparing Apache Cassandra and Zilliz Cloud for Vector Search Workloads

As an engineer working on vector-based AI systems, I recently ran a comparative evaluation between Apache Cassandra (v5.0) and Zilliz Cloud for large-scale vector search. What I found sheds light on how infrastructure choices deeply shape performance, operability, and cost—especially under real workloads.

Vector Databases: Beyond Key-Value and Relational

Before diving into comparisons, I want to clarify what makes vector databases fundamentally different. Unlike traditional key-value or relational systems, vector DBs operate on high-dimensional embeddings—numerical arrays representing semantic features extracted from unstructured data like text, images, or audio. These embeddings are used to perform approximate nearest neighbor (ANN) searches.

Typical applications include:

• Semantic product recommendation
• RAG (Retrieval-Augmented Generation) pipelines for LLMs
• Content moderation systems
• Visual search and media deduplication

These workloads demand specialized ANN indexing (e.g., IVF, HNSW) and distance metrics (e.g., cosine, inner product), which aren’t native to most general-purpose DBs.

Cassandra 5.0: Vector Search via Storage-Attached Indexes (SAI)

I was initially skeptical about whether Cassandra—a classic NoSQL key-value store—could perform well in vector search scenarios. But starting in v5.0, Cassandra introduced Storage-Attached Indexes (SAI) for embedding support.

What impressed me:

• Embeddings are stored inline with structured data.
• ANN search is treated like any indexed column—no need for external engines.
• You retain access to Cassandra’s mature ecosystem: tunable consistency, partitioning, and replication.

Example Table with Vector Embedding (Cassandra)

CREATE TABLE images (
    id UUID PRIMARY KEY,
    metadata TEXT,
    embedding VECTOR<FLOAT, 128>
);

SELECT id FROM images
WHERE embedding ANN OF [0.1, 0.5, -0.2, ...]
LIMIT 5;

In my 10M vector benchmark, Cassandra’s ANN performance was surprisingly robust—but heavily dependent on good partitioning and token range tuning. Without careful tuning, hotspotting and uneven latency emerged under distributed load.

Zilliz Cloud: Specialized Vector Workloads with AutoIndex and Hybrid Querying

By contrast, Zilliz Cloud is built on the Milvus engine—a purpose-built vector search platform. What stood out most for me:

• AutoIndexing automatically selects the optimal algorithm (e.g., IVF, HNSW) based on data characteristics.
• Hybrid search is seamless. You can combine dense vector queries with scalar filters in the same operation.
• Cloud-native scaling adjusts resources dynamically.
• Tiered storage offloads cold data to cost-efficient backends like S3 without degrading query speed.

Example Hybrid Query (Zilliz Cloud Python SDK)

results = client.search(
    collection="multimodal_data",
    query_vector=text_embedding,
    metric_type="COSINE",
    top_k=10,
    filter="category == 'shoes'"
)

In practice, this enabled me to deploy cross-modal search (e.g., combining image and text embeddings) with minimal setup.

Microbenchmark Notes: 10M 128-Dim Vector Workload

I ran both systems on a synthetic 10M dataset with 128-dimensional vectors. Here's what I observed:

Metric	Cassandra 5.0	Zilliz Cloud
Index setup time	Moderate (SAI init)	Fast (AutoIndex enabled)
ANN query latency (p99)	~180ms (tuned)	~85ms (default config)
Hybrid search	Manual join logic	Native support
Horizontal scaling	Manual node addition, rebalancing	Automatic resource scaling
Cold data optimization	None	Tiered storage to object backend
Operational complexity	High (cluster tuning required)	Low (managed SaaS)
Cost efficiency under spike	Better (static nodes)	Worse (compute surge pricing)

Insert original benchmark image here, if present in source material.

I noticed Cassandra's latency stability was sensitive to how partitions were structured. In one poorly balanced cluster, p99 latencies exceeded 400ms. Zilliz Cloud avoided these spikes via dynamic scaling but incurred higher costs during load peaks.

Consistency Trade-Offs in Practice

A key architectural difference lies in consistency models. Cassandra supports tunable consistency (e.g., ONE, QUORUM), giving fine-grained control over latency vs correctness. This is beneficial in regulated environments where strong guarantees are needed.

However, for most vector search workloads, such strong guarantees are overkill. These workloads are probabilistic by nature. Zilliz Cloud abstracts this entirely, operating with eventual consistency under the hood—an appropriate tradeoff for AI inference use cases.

Misapplying strong consistency here can unnecessarily throttle performance and inflate costs.

Deployment Implications and System Design

When to Choose Cassandra

Use Cassandra if:

• You already run a Cassandra-based architecture.
• Embeddings are part of broader transactional data.
• You need tight control over replication and consistency.
• You're willing to invest in manual tuning and SAI configuration.

When Zilliz Cloud Makes More Sense

Use Zilliz Cloud if:

• You're building a greenfield AI search application.
• You need support for hybrid, multimodal queries.
• You prefer managed ops with elastic compute and storage.
• You're optimizing for developer velocity rather than cost floor.

Final Thoughts: Benchmark, Don’t Assume

What I learned through this side-by-side exercise is simple but critical: you cannot assume architecture fit from documentation alone. Real-world benchmarks tell a very different story—especially under mixed workloads and production-scale data.

If you’re evaluating vector databases, I recommend running your own suite of tests using something like VectorDBBench. It’s the only way to capture query-specific performance, cost, and operational realities.

What I'm Exploring Next

My next steps will focus on:

• Evaluating hybrid ANN + keyword search across larger multimodal corpora.
• Investigating vector deduplication techniques using MinHash and Jaccard.
• Exploring GPU-accelerated vector query engines (e.g., Faiss-GPU vs Milvus GPU mode).

I’ve come away from this project with a deeper respect for how design assumptions shape performance outcomes. Systems that abstract away tuning can enable rapid iteration—but at the expense of fine-grained control. As always, the right tool depends on your constraints.