Select the Right Cluster Type

Selecting the right Compute Unit (CU) is a crucial step when creating a cluster in Zilliz Cloud. A CU is the basic unit of compute resources used for parallel processing of data, and different cluster types comprise varying combinations of CPU, memory, and storage.

Understand cluster types

Zilliz Cloud offers these cluster types: Performance-optimized, Capacity-optimized, and Tiered-storage.

The following table offers a quick comparison of the three cluster types in different aspects. For a detailed comparison in terms of the capacity and performance among the cluster types, please proceed to Select an optimal cluster type.

Cluster Type	Search QPS	Search Latency	Per Query CU Capacity	Cost per Million Vectors
Performance-optimized	500~1500	sub-10 ms	1.5 million 768-dim vectors	from $65/mo.
Capacity-optimized	100~300	tens-ms	5 million 768-dim vectors	from $20/mo.
Tiered-storage	5~20	hundreds-ms	20 million 768-dim vectors	from $7/mo.

Performance-optimized cluster

• Tailored for scenarios emphasizing low latency and high throughput.

• Ideal for real-time applications like generative AI, recommendation systems, chatbots, and more.

Capacity-optimized cluster

• Crafted for handling vast datasets, boasting five times the data capacity of its Performance-optimized counterpart, albeit with subdued search performance.

• Ideal for large-scale unstructured data search, copyright detection, and identity verification.

Tiered-storage cluster

• Best for ultra-large-scale, cost-sensitive workloads with clear hot and cold data patterns.

• Ideal for applications that need to store massive volumes of data at a low cost. The capacity of a Tiered-storage cluster is 4 times that of a Capacity-optimized cluster.

📘Notes

To select a Tiered-storage cluster, your cluster must have at least 8 query CUs.

Select an optimal cluster type

Factor in data volume, performance expectations, and budgets while selecting the cluster type. Your vector data's magnitude, both in terms of vector count and dimensions, plays a pivotal role in determining cluster resource allocation.

Assess capacity

The number of entities a cluster can accommodate depends on the query CU capacity of a cluster.

The reference table below illustrates the capacity of a performance-optimized and capacity-optimized with 1 query CU, taking into account the vector dimensions and the total vector count. For an estimation of the number of query CU needed for your data volume, please use our calculator.

Vector Dimensions	Performance-optimized (Max. Vectors per query CU)	Capacity-optimized (Max. Vectors per query CU)	Tiered-storage (Max. Vectors per query CU)
128	7.5 million	25 million	100 million
256	4.5 million	15 million	60 million
512	2.25 million	7.5 million	30 million
768	1.5 million	5 million	20 million
1024	1.125 million	3.75 million	15 million

📘Notes

The above metrics are based on tests considering only primary keys and vectors. If your dataset has extra scalar fields (e.g., id, label, keywords), the actual capacity may deviate. It's prudent to conduct personal tests for a precise evaluation.

Evaluate performance

Performance metrics, notably latency and queries per second (QPS), are vital. The Performance-optimized cluster distinctly outperforms Capacity-optimized cluster in latency and throughput, particularly for standard top-k values ranging from 10 to 250.

The following table shows the test result of how each cluster type performs in terms of QPS.

top_k	QPS for Performance-optimized cluster (768-dim 1M vectors)	QPS for Capacity-optimized cluster (768-dim 5M vectors)
10	520	100
100	440	80
250	270	60
1000	150	40

The following table shows the test result of how each cluster type performs in terms of latency.

top_k	Latency of Performance-optimized cluster (768-dim 1M vectors)	Latency of Capacity-optimized cluster (768-dim 5M vectors)
10	< 10 ms	< 50 ms
100	< 10 ms	< 50 ms
250	< 10 ms	< 50 ms
1000	10 - 20 ms	50 - 100 ms

Scenario breakdown

Suppose you are building an image recommendation application with a library of 8 million images. Each image in your library is represented by a 768-dimensional embedding vector. Your goal is to swiftly handle a QPS of 1,000 recommendation requests and deliver the top 100 image recommendations in under 30 milliseconds.

To select the right cluster type and query CU for this requirement, follow these steps:

1. Evaluate Latency: The Performance-optimized cluster is the only type that meets the 30-millisecond latency requirement.

2. Assess Capacity: A single Performance-optimized cluster with 1 query CU accommodates 1.5 million 768-dimensional vectors. To store all 8 million vectors, you would need at least 6 query CUs.

3. Check Throughput: With a top-k setting of 100, the Performance-optimized cluster can achieve a QPS of 440. To sustain a consistent 1,000 QPS, you would need to triple the number of replicas.

In conclusion, for this scenario, the Performance-optimized cluster is your best bet. A configuration of 3 replicas, with each replica consisting of 6 query CUs, should serve you perfectly.