Managing resources (Hot, Warm, Cold)

Weaviate provides flexible resource management features that help you to balance search speeds; search accuracy and recall; and system resource costs.

This guide provides an overview of these topics to help you make allocate resources effectively:

• Storage tiers (hot, warm, cold)
• Vector index types
• Vector compression
• Tenant states
• Tips

Resource management Tips

• Start with the dynamic index type unless you have a reason not to.
• Consider vector compression techniques if some loss of accuracy is acceptable.
  • This will improve query speeds.
  • For HNSW indexes, this will reduce memory usage.
• Avoid overprovisioning storage. Especially hot storage.
  • Can you use tenant states to reduce costs?
  • Can you use vector compression to reduce memory usage?

Storage Tiers - Temperatures

We categorize storage resources using three tiers: hot, warm, and cold. Each tier has different performance characteristics and costs.

Tier	Vector Index Type	Vector Compression	Tenant State	Storage	Performance	Cost
🟥 Hot	HNSW	PQ, SQ, BQ	Active	Memory	Fastest	High
🟨 Warm	Flat	BQ	Active	SSD	Slower	Moderate
🟦 Cold	Any	Any	Inactive	Cloud	Resource not available	Low

🟥 Hot

• Describes memory usage
• Fastest and most expensive
• Primarily driven by HNSW vector indexes
• Always available (active) for use
• Costs increase rapidly with scale

🟨 Warm

• Describes data stored on disk (SSD)
• Slower than hot tier but less expensive
• Driven by flat vector index, object data, and inverted indexes
• Always available (active) for use
• Costs increase more slowly than hot tier as data grows

🟦 Cold

Offloading: AWS S3 only

As of Weaviate v1.26.0, tenants can only be offloaded to cold storage in AWS S3. Additional storage options may be added in future releases.

To offload a tenant, use the offload-s3 module.

• Describes data stored in cloud storage
• Slowest and least expensive tier
• Primarily driven by offloaded tenants
• Resources are not available (inactive) for use
• Requires reactivation to access

Resource Management - Key Factors

Effective resource management in Weaviate involves balancing performance, cost, and data accessibility. The key levers to manage resources are:

• Vector index types: Choose the right index type based on the number of objects and desired performance.
• Vector compression: Use compression techniques to reduce memory usage and improve query performance at the cost of some accuracy.
• Tenant states: Manage tenant states to balance cost and performance.

Vector index types

The choice of vector index type can have a significant impact on performance and resource usage. Weaviate supports the following index types:

Index Type	Resource Usage	Performance	Suitable for	Description
HNSW	🟥 Hot	Fast	Any object count	A memory-based, fast index (read more)
Flat	🟨 Warm	Medium	<~10k objects	A disk-based, brute-force index (read more)
Dynamic	Depends	Depends	Any object count	Transitions from flat to HNSW index at a specified threshold (read more)

The choice of index type depends on the number of objects and the desired performance. As a rule of thumb, use the following guidelines for a multi-tenant collection:

If you are unsure which index type to use, the dynamic index type is a good starting point, as it automatically transitions from a flat to an HNSW index based on the number of objects.

• Starter guide: indexes
• How-to: Set the vector index type

Vector compression

Vector compression techniques reduce the size of vectors by quantizing them into a smaller representation.

This can have the impact of reducing memory usage, or improving performance by reducing the amount of data that needs to be read from disk. The trade-off is that the resulting search quality may be lower.

Weaviate supports the following vector compression methods:

Compression Method	Index Type	Requires Training	Description
Product Quantization (PQ)	HNSW	Yes	Each vector becomes an array of integer-based centroids (read more)
Binary Quantization (BQ)	HNSW, Flat	No	Each vector dimension becomes a bit (read more)
Scalar Quantization (SQ)	HNSW	Yes	Each vector dimension becomes an integer (read more)

As a starting point, use the following guidelines for selecting a compression method:

If you are unsure which index type to use, scalar quantization is a good starting point, provided that you have a representative sample of your likely final dataset.

• Starter guide: Vector compression
• How-to: Configure vector compression

Tenant states

Multi-tenant collections enable you to efficiently manage isolated subsets of data. Each tenant share the same schema and configuration.

Weaviate supports the following tenant states:

Tenant state	CRUD & Queries	Vector Index	Inverted Index	Object Data	Time to Activate	Description
Active (default)	Yes	Hot/Warm	Warm	Warm	None	Tenant is available for use
Inactive	No	Warm	Warm	Warm	Fast	Tenant is locally stored but not available for use
Offloaded	No	Cold	Cold	Cold	Slow	Tenant is stored in cloud storage and not available for use

Hot tenants can be deactivated to warm storage to reduce memory usage, and any tenant can be offloaded to cold storage to reduce memory and disk usage. Conversely, any tenant can be reactivated when needed.

Consider a strategy of deactivating tenants that are not frequently accessed, and offloading tenants that are rarely accessed.

• Starter guide: tenant states
• How-to: Configure tenant offloading
• How-to: Manage tenant states

Tips

Best Practices

• Start with the dynamic index type for new collections. This is particularly useful for multi-tenant collections, as it allows each tenant to use the most appropriate index type.
• Use vector compression techniques to optimize storage and query performance, especially for large collections or tenants.
• Conduct thorough testing when changing index types or compression methods to ensure performance meets your requirements.

Common Pitfalls

• Overprovisioning hot storage: Keeping all data in hot storage can lead to unnecessary costs. Regularly assess what data truly needs the fastest access.
• Neglecting to plan for growth: Not anticipating data growth can lead to performance issues. Always design your resource management strategy with scalability in mind.
• Improper tenant management: In multi-tenant scenarios, forgetting to offload inactive tenants can lead to resource waste. Implement automated processes to manage tenant states based on usage patterns.
• Mismatch between quantization techniques, model and data: When using compression technique, ensure that the quantization technique is compatible with the model (e.g. BQ) and that the data is sufficient and representative for training (e.g. PQ, SQ).

Related pages

• Starter guide: Compression
• Starter guide: Indexing
• Starter guide: Tenant states
• Concepts: Vector Index
• Concepts: Vector Quantization
• Concepts: Multi-Tenancy
• How-to: Set the vector index type
• How-to: Configure vector compression
• How-to: Perform multi-tenancy operations
• How-to: Manage tenant states

Questions and feedback

If you have any questions or feedback, let us know in the user forum.

• Technical questions
• Documentation feedback