Zilliz Cloud
Fully managed vector lakebase and database for enterprise AI workloads
Zilliz Cloud is a strong choice for scaling vector search to millions or billions of vectors, especially if you're already in the Milvus ecosystem. The Vector Lakebase, Loon engine, and on-demand compute address real pain points around cost and data silos. However, dedicated clusters start at $126/GB/month, and the learning curve is steeper than simpler managed services like Pinecone.
- High-scale recommendation systems requiring sub-300ms latency
- RAG applications with multi-million vector indexes
- Anomaly detection over streaming data
- Enterprise teams needing multi-cloud or BYOC for compliance
- Small projects with <1M vectors where serverless options are cheaper
- Teams preferring pure open-source self-managed Milvus
- Users needing a vector database without a lake-native architecture
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Skip Zilliz Cloud if you need a simple, low-latency vector database for under 1 million vectors — serverless alternatives like Pinecone are cheaper and easier to set up.
Going past 2.5M vCUs/month on the Free tier requires upgrading to a paid plan, which starts at $126/GB/month for dedicated clusters.
Zilliz Cloud's tiered storage offers competitive per-million-vector costs ($63 to $5/month) for dedicated clusters, but the entry price of $126/GB/month is higher than Pinecone's serverless (per-request billing). Free and serverless tiers suit prototyping; enterprise features require dedicated clusters. BYOC adds flexibility but requires sales negotiation.
In short
Zilliz Cloud — Fully managed vector lakebase and database for enterprise AI workloads. Best for High-scale recommendation systems requiring sub-300ms latency, RAG applications with multi-million vector indexes, Anomaly detection over streaming data. Free to start; paid plans from $126/mo.
What's new in Zilliz Cloud
Checked 12 days agoAcross the latest 4 updates: 3 feature updates and 1 news mention.
VDBBench Adds Cost-Aware Benchmarking for Vector Databases
VDBBench now includes cost metrics alongside performance, enabling price-performance comparison across vector databases.
Introducing Loon: A New Storage Engine for Vector Data That Never Stops Changing
Loon storage engine handles continuous vector data updates without performance degradation, enabling real-time data ingestion.
What Is a Vector Lakebase?
Describes Vector Lakebase as a unified, lake-native data foundation for AI workloads, eliminating data silos.
Zilliz Cloud On-Demand Compute: Pay Only for What You Use
Introduces on-demand compute pricing, decoupling compute from storage costs and allowing pay-per-job billing.
Viability Score
How likely is Zilliz Cloud to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- AutoIndex with hybrid IVF and graph indexing
- Cardinal search engine for automated tuning
- Hybrid search across multimodal, sparse-dense, dense-text vectors
- Similarity metrics: Cosine, Euclidean, Inner Product
- Tunable consistency levels
- Horizontal scaling with component-based architecture
- Tiered storage: Performance, Capacity, Tiered-storage
- On-demand compute (pay per job runtime)
- Vector Lakebase unified lake-native storage
- Loon storage engine for continuous updates without performance degradation
- Multi-cloud deployment (AWS, Azure, GCP)
- BYOC (Bring Your Own Cloud) for data sovereignty
- Enterprise-grade security: encryption, RBAC, SSO, audit logs
- VPC peering and private endpoints
- Built-in monitoring and automated upgrades
About Zilliz Cloud
Zilliz Cloud is a fully managed vector lakebase and database built on the open-source Milvus project, targeting data teams and AI engineers building enterprise-scale recommendation systems, retrieval-augmented generation (RAG), and anomaly detection. It offers AutoIndex with hybrid IVF and graph indexing, the Cardinal search engine for automated tuning, hybrid search across multimodal vectors (sparse-dense, dense-text), and tiered storage (Performance, Capacity, Tiered-storage) for cost optimization. The newly introduced Vector Lakebase unifies data lakes and vector databases, while the Loon storage engine (announced June 2026) handles continuous updates without performance degradation. On-demand compute pricing decouples compute from storage costs, allowing pay-per-job billing. Deployable on AWS, Azure, and GCP, with BYOC for data sovereignty. Compared to Pinecone or Weaviate, Zilliz Cloud emphasizes lower TCO (up to 70% reduction) and leverages the mature Milvus ecosystem.
Behind the Verdict
We'd reach for Zilliz Cloud when the dataset is in the tens of millions of vectors or more, and when we want to avoid the operational headache of self-hosting Milvus. The free tier (5 GB storage, 2.5M vCUs per month) is generous enough to prototype serious applications. The Vector Lakebase and Loon engine (announced June 2026) are genuinely innovative—they let you treat a data lake like a vector database without duplicating data, and they solve the painful problem of degrading search performance during continuous ingestion. Where it bites: the cost of dedicated clusters, especially Performance-optimized at $63 per million vectors/month, adds up fast. Serverless starts at $0 but doesn't guarantee latency—useful for dev, risky for production latency-sensitive apps. The Enterprise tier (from $197/month) includes SSO, audit logs, and VPC peering, but you'll still pay storage costs on top. Compared to Pinecone, Zilliz Cloud offers more flexibility (BYOC, multiple cloud providers) and lower TCO at scale, but the setup and tuning require more Milvus-specific knowledge. We'd pass if you're a small team with under a million vectors and need a dead-simple, fully managed experience—Pinecone's free tier is easier to get started with. Also, if you strictly need real-time writes with p99 latency under 10ms, test the Capacity-optimized or Tiered-storage tiers first, as they're designed for throughput over latency.
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Real-world workflow fit
Concrete scenarios for the personas Zilliz Cloud actually fits — and what changes day-one when you adopt it.
You have 10 million document embeddings. You create a Performance-optimized Dedicated cluster, index with AutoIndex, and connect via LangChain.
Outcome: Achieves sub-100ms search latency with >95% recall for real-time Q&A.
You export your Milvus collections and import them into a Zilliz Cloud Dedicated cluster using the migration tool.
Outcome: Eliminates ops overhead, reduces TCO by 70%, and gains managed backups, monitoring, and auto-scaling.
You sign up for the Free tier, create up to 5 collections, and test your recommendation algorithm on 2.5M vCUs/month.
Outcome: Validates the product in days with zero upfront cost, then upgrades to Standard Serverless for production.
Use Cases
- Build a retrieval-augmented generation (RAG) pipeline for enterprise document search.
- Power real-time recommendation systems by vectorizing user and item embeddings.
- Enable semantic search on millions of product descriptions for an e-commerce platform.
- Implement multimodal search across images, text, and audio using hybrid vectors.
- Deploy a HIPAA-compliant medical AI assistant with secure vector storage.
- Run lake-scale discovery queries on external data without importing it into the database.
Models Under the Hood
as of 2026-07-14
Limitations
- Dedicated clusters have a minimum cost of $126/GB/month, which may be prohibitive for small projects.
- Serverless clusters under Standard lack enterprise features like SSO and SLA.
- The free tier is limited to 5 collections and 2.5M vCUs/month.
- On-Demand Compute is new and may not support all workloads yet.
- The platform has a learning curve due to Milvus complexity.
as of 2026-06-29
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Zilliz Cloud tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Solo developers or small teams evaluating vector search with up to 5 collections and 2.5M vCUs/month.
What this tier adds
Starting tier with 5 GB storage, shared environment, and auto-scaling serverless — no payment needed.
Standard - Serverless
From $0/mo
Ideal for
Prototypes and non-critical testing with managed core APIs and basic monitoring.
What this tier adds
Adds fully managed databases, backup/restore, and encryption; starts at $0/mo with a 30-day free trial.
Standard - Dedicated
From $126/GB/month
Ideal for
Production workloads needing isolated resources and predictable performance, starting at $126/GB/month.
What this tier adds
Dedicated single-tenant environment with manual scaling to 32 CUs, configurable auto-scaling, and tiered storage options.
Enterprise - Dedicated
From $197/month
Ideal for
Production applications requiring enterprise SLA, SSO, VPC peering, and priority support.
What this tier adds
Adds 99.95% uptime SLA, audit logs, SSO (SAML 2.0), RBAC, multi-replica, private endpoints, and scaling to 256+ CUs.
Business Critical - Dedicated
Contact sales
On-demand Compute
Pay per job runtime
Ideal for
Teams running intermittent lake-scale search/indexing jobs without maintaining always-on clusters.
What this tier adds
Pay only per job runtime, zero-copy access to external data, no always-on compute required.
BYOC (Bring Your Own Cloud)
Contact sales
Ideal for
Organizations with strict data sovereignty or custom infrastructure requirements.
What this tier adds
Deploy on your own infrastructure with full control and security; same SaaS features in your own cloud account.
Where the pricing makes sense
The company stage and team size where Zilliz Cloud's pricing actually pencils out — and where peers do it cheaper.
Zilliz Cloud's tiered storage offers competitive per-million-vector costs ($63 to $5/month) for dedicated clusters, but the entry price of $126/GB/month is higher than Pinecone's serverless (per-request billing). Free and serverless tiers suit prototyping; enterprise features require dedicated clusters. BYOC adds flexibility but requires sales negotiation.
Setup time & first value
How long it actually takes to get something useful out of Zilliz Cloud — broken out by persona, not the marketing-page minute.
For a prototype: sign up and create a serverless cluster in under 10 minutes. For production: provisioning a dedicated cluster takes 5-15 minutes; data migration from Milvus may take hours depending on volume. On-demand compute jobs can start immediately after configuration.
Switching to or from Zilliz Cloud
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Milvus: Use the documented migration tool to export data from your self-managed Milvus and import into a Zilliz Cloud dedicated cluster.
- →From Pinecone: Export your vectors via Pinecone SDK and bulk-import them into Zilliz Cloud using the Python SDK or REST API.
- →From Weaviate: Use the Weaviate export API to dump data and then batch insert into Zilliz Cloud collections.
- ↗To Milvus: Export collections from Zilliz Cloud using REST API and import into a self-managed Milvus instance.
- ↗To Pinecone: Use the Zilliz Cloud client to read vectors and write to Pinecone index via its API.
Integrations
Resources & Guides
- Conceptsdocs.zilliz.com
Understand Basics | Cloud
Core ideas explained from docs.zilliz.com
- Documentationdocs.zilliz.com
Search
Full product docs from docs.zilliz.com
- Documentationdocs.zilliz.com
Create Cluster | Cloud
Full product docs from docs.zilliz.com
- Documentationdocs.zilliz.com
Manage Cluster | Cloud
Full product docs from docs.zilliz.com
- Documentationdocs.zilliz.com
Backup & Restore | Cloud
Full product docs from docs.zilliz.com
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