What I Learned Comparing Zilliz Cloud and Deep Lake for Scalable Vector Search

Introduction: Why I Benchmarked Two Very Different Tools

As I scaled up a semantic search engine for multi-modal content, I found myself at a fork in the road. Should I lean into a purpose-built vector database like Zilliz Cloud, or embrace a more flexible data lake approach with Deep Lake? These tools promise vector search at scale—but they come from fundamentally different architectural philosophies.

To make a grounded choice, I decided to benchmark them on real workloads: 10M vectors, cosine similarity, concurrent queries, and hybrid search needs. What follows is a synthesis of what I discovered.

Vector Database Architecture: How Design Shapes Reality

Zilliz Cloud: Auto-Optimized, Minimal Tuning

Zilliz Cloud is built on Milvus, a system that abstracts away most of the indexing decisions. Its AutoIndex capability selects between IVF and graph-based indexes depending on the data. That meant I didn’t need to hand-pick parameters like nlist or ef.

import pymilvus
client = pymilvus.Collection("my_vectors")
client.load()
results = client.search(query_vectors, "embedding", metric_type="COSINE", top_k=10)

I tested it on 10 million 768-dimensional vectors using cosine similarity. Query latencies stayed under 100ms even with multiple concurrent users. The tiered storage model—hot vs cold separation—handled scaling well without manual data tiering.

Deployment note: Zilliz Cloud supports AWS, GCP, Azure, and Bring-Your-Own-Cloud (BYOC). I tested in AWS us-west-2 under moderate load.

Deep Lake: Multimedia Native, But More Manual

Deep Lake isn’t a database in the traditional sense—it's a versioned data lake with vector search capabilities. Built-in visualization, versioning, and seamless integration with LangChain and LlamaIndex made it a strong candidate for retrieval-augmented generation (RAG) pipelines.

import deeplake
ds = deeplake.load("hub://username/my_dataset")
results = ds.search(embedding=query_vector, k=10)

It uses HNSW for approximate search. On a 35M vector dataset, queries returned in under 1s. However, hybrid queries (vector + attribute filtering) fell back to linear search—a major bottleneck in structured retrieval tasks.

Consistency Levels: Where Zilliz Gets It Right (and Deep Lake Doesn’t Try)

In distributed systems, consistency guarantees matter—especially under concurrent writes and reads. Zilliz Cloud offers tunable consistency levels (Session, Strong, Eventually). For my use case—read-heavy, low-latency search—Session consistency struck the right balance.

Caution: Misusing Eventually in hybrid pipelines can lead to stale reads during re-indexing. I learned that the hard way when a batch write wasn't visible to a downstream retriever.

Deep Lake, by contrast, avoids the question entirely by relying on file versioning and snapshot immutability. This works for batch workflows, but feels brittle for production search where concurrent writes are expected.

Benchmarking Results: The Good, The Tradeoffs

Microbenchmark Setup

Parameter	Value
Vector Dimensionality	768
Dataset Size	10 million vectors
Query Type	Top-10 Nearest Neighbors
Metric	Cosine Similarity
Concurrency	16 parallel query threads

Performance Snapshot

System	Median Latency	Peak Throughput	Hybrid Query Support
Zilliz Cloud	~100 ms	2,000 QPS	✅ (Indexed)
Deep Lake	~200 ms	700 QPS	❌ (Linear fallback)

Zilliz consistently performed better under high concurrency due to its distributed architecture. Deep Lake lagged behind in query speed but excelled in RAG-friendly tooling and multimedia version control.

Deployment Tradeoffs and Cost Observations

Feature	Zilliz Cloud	Deep Lake
Cold Storage Tiering	Automatic	Manual
Multi-modal Embedding	Limited support	First-class citizen
Hybrid Search (filter+ANN)	Efficient (index-aware)	Linear scan
BYOC Support	Available	Not supported
SDK Integration	Python, REST, gRPC	Python-centric

In my own BYOC setup for Zilliz on AWS with autoscaling enabled, storage cost remained predictable under S3 standard and infrequent access tiers. Deep Lake, with frequent image/audio loading, incurred higher egress and storage costs unless aggressively optimized.

Lessons I Took Away

When to Use Zilliz Cloud

Use it when you care about:

• Fast ANN queries with automated optimization
• Scaling under multi-tenant workloads
• Minimal ops overhead with a clean API

Avoid if your pipeline depends heavily on multimedia types or lineage tracking.

When to Use Deep Lake

Reach for it when you:

• Need RAG pipelines tightly integrated with visual inspection
• Want to co-version models + embeddings + metadata
• Are comfortable sacrificing real-time speed for richer annotation/control

Avoid it if you're building a production-facing hybrid search API with strict latency SLAs.

Final Thoughts: Tooling Must Match the Data Lifecycle

What I’ve learned is this: choosing a vector store isn't about the fastest index—it’s about aligning the system’s strengths with the data’s shape and the query’s demands. Zilliz Cloud is built for speed and ops-free retrieval. Deep Lake is a developer-first, experimentation-rich playground.

In my next round of benchmarks, I plan to:

• Introduce structured filtering with >100M vectors
• Profile memory usage and IOPS under stress
• Compare with pgvector + HNSW hybrid in PostgreSQL