# 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_)](#storage-tiers---temperatures)**
- **[Vector index types](#vector-index-types)**
- **[Vector compression](#vector-compression)**
- **[Tenant states](#tenant-states)**
- **[Tips](#tips)**
:::callout{intent="tip" title="Resource management Tips"}
* Start with the dynamic [index type](#vector-index-types) unless you have a reason not to.
* Consider [vector compression](#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](#tenant-states) to reduce costs?
- Can you use [vector compression](#vector-compression) to reduce memory usage?
:::
## Storage Tiers - Temperatures
We categorize storage resources using three tiers: [_hot_](#-hot), [_warm_](#-warm), and [_cold_](#-cold). Each tier has different performance characteristics and costs.
| Tier | Vector Index Type | Vector Compression | Tenant State | Storage | Performance | Cost |
| ------- | ---------------------- | ----------------------- | ------------ | ------- | ---------------------- | -------- |
| π₯ Hot | HNSW | PQ, SQ, BQ | Active | Memory | Fastest | High |
| π¨ Warm | Flat | BQ | Active | SSD | Slower | Moderate |
| π¦ Cold | Any | Any | Inactive | Cloud | Resource not available | Low |
### π₯ Hot
- Describes memory usage
- Fastest and most expensive
- Primarily driven by [`HNSW`](managing-resources-indexing.md#hnsw-indexes) vector indexes
- Always available (active) for use
- Costs increase rapidly with scale
### π¨ Warm
- Describes data stored on disk (SSD)
- Slower than [hot](#-hot) tier but less expensive
- Driven by [flat](managing-resources-indexing.md#flat-indexes) vector index, object data, and [inverted indexes](managing-resources-indexing.md#inverted-indexes)
- Always available (active) for use
- Costs increase more slowly than hot tier as data grows
### π¦ Cold
:::callout{intent="info" title="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](#tenant-states)
- Resources are not available (inactive) for use
- Requires [reactivation](#tenant-states) 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](#vector-index-types)**: Choose the right index type based on the number of objects and desired performance.
- **[Vector compression](#vector-compression)**: Use compression techniques to reduce memory usage and improve query performance at the cost of some accuracy.
- **[Tenant states](#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 | π₯ Hot | Fast | Any object count | A memory-based, fast index ([read more](managing-resources-indexing.md#hnsw-indexes)) |
| Flat | π¨ Warm | Medium | <\~10k objects | A disk-based, brute-force index ([read more](managing-resources-indexing.md#flat-indexes)) |
| Dynamic | Depends | Depends | Any object count | Transitions from flat to HNSW index at a specified threshold ([read more](managing-resources-indexing.md#dynamic-indexes)) |
| HFresh | π¨ Warm | Fast | Large collections | A cluster-based index with memory efficiency for large datasets ([read more](managing-resources-indexing.md#hfresh-indexes)) |
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:
```mermaid
flowchart LR
%% Define nodes and connections
subgraph vectorIndex ["Vector Index Selection"]
direction LR
multiTenant{"Is multi-tenancy enabled?"}
objCount{"Does the collection have more than 100k objects?"}
stayUnder{"Is the collection likely to stay at under 100k objects?"}
multiTenant -->|No| objCount
multiTenant -->|Yes| useDynamic["Use Dynamic"]
objCount -->|Yes| useHNSW["Use HNSW"]
objCount -->|No| stayUnder
stayUnder -->|Yes| useFlat["Use Flat"]
stayUnder -->|"No/Unsure"| useDynamic["Use Dynamic"]
end
%% Style nodes
style multiTenant fill:#ffffff,stroke:#B9C8DF,color:#130C49
style objCount fill:#ffffff,stroke:#B9C8DF,color:#130C49
style stayUnder fill:#ffffff,stroke:#B9C8DF,color:#130C49
%% Terminal nodes
style useHNSW fill:#ffffff,stroke:#61BD73,color:#130C49
style useFlat fill:#ffffff,stroke:#61BD73,color:#130C49
style useDynamic fill:#ffffff,stroke:#61BD73,color:#130C49
%% Style subgraph
style vectorIndex fill:#ffffff,stroke:#130C49,stroke-width:2px,color:#130C49
```
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](managing-resources-indexing.md)
- [How-to: Set the vector index type](../how-to-manage-collections/vector-config.md#set-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](../concepts/vector-quantization.md#product-quantization)) |
| Binary Quantization (BQ) | HNSW, Flat | No | Each vector dimension becomes a bit ([read more](../concepts/vector-quantization.md#binary-quantization)) |
| Scalar Quantization (SQ) | HNSW | Yes | Each vector dimension becomes an integer ([read more](../concepts/vector-quantization.md#scalar-quantization)) |
| Rotational Quantization (RQ) | All index types | No | Each vector is rotated then quantized to an integer ([read more](../concepts/vector-quantization.md#rotational-quantization)) |
As a starting point, use the following guidelines for selecting a compression method:
```mermaid
flowchart LR
%% Define nodes and connections
subgraph compression ["Compression Strategy"]
direction LR
startIndexType{"What is your vector index type?"}
sample{"Do you have a representative sample of your final dataset?"}
tunable{"Do you want tunable compression?"}
bqcompat{"Is your vectorizer model compatible with BQ?"}
startIndexType -->|HNSW| sample
startIndexType -->|Flat| bqcompat
sample -->|Yes| tunable
tunable -->|Yes| usePQ["Use PQ"]
tunable -->|No| useSQ["Use SQ"]
sample -->|No| useBQ["Use BQ"]
bqcompat -->|Yes| useBQ["Use BQ"]
bqcompat -->|No| noCompress["Do not use compression"]
end
%% Style nodes
style startIndexType fill:#ffffff,stroke:#B9C8DF,color:#130C49
style sample fill:#ffffff,stroke:#B9C8DF,color:#130C49
style tunable fill:#ffffff,stroke:#B9C8DF,color:#130C49
style bqcompat fill:#ffffff,stroke:#B9C8DF,color:#130C49
%% Terminal nodes
style usePQ fill:#ffffff,stroke:#61BD73,color:#130C49
style useSQ fill:#ffffff,stroke:#61BD73,color:#130C49
style useBQ fill:#ffffff,stroke:#61BD73,color:#130C49
style noCompress fill:#ffffff,stroke:#61BD73,color:#130C49
%% Style subgraph
style compression fill:#ffffff,stroke:#7AD6EB,stroke-width:2px,color:#130C49
```
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](managing-resources-compression.md)
- [How-to: Configure vector compression](../how-to-configure-weaviate/compression.md)
### 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.
```mermaid
flowchart LR
%% Define nodes and connections
subgraph tenantData ["Tenant Data Availability"]
direction LR
needNow{"Does the tenant data need to be available now?"}
howQuick{"When it is needed, how quickly does it need to be available?"}
needNow -->|Yes| active["Active"]
needNow -->|No| howQuick
howQuick -->|Quickly| inactive["Inactive"]
howQuick -->|"Latency is acceptable"| offloaded["Offloaded"]
end
%% Style nodes
style needNow fill:#ffffff,stroke:#B9C8DF,color:#130C49
style howQuick fill:#ffffff,stroke:#B9C8DF,color:#130C49
%% Terminal nodes
style active fill:#ffffff,stroke:#61BD73,color:#130C49
style inactive fill:#ffffff,stroke:#61BD73,color:#130C49
style offloaded fill:#ffffff,stroke:#61BD73,color:#130C49
%% Style subgraph
style tenantData fill:#ffffff,stroke:#61BD73,stroke-width:2px,color:#130C49
```
Consider a strategy of deactivating tenants that are not frequently accessed, and offloading tenants that are rarely accessed.
- [Starter guide: tenant states](managing-resources-tenant-states.md)
- [How-to: Configure tenant offloading](../storage-and-backups/tenant-offloading.md)
- [How-to: Manage tenant states](../how-to-manage-collections/tenant-states.md)
## Tips
### Best Practices
- Start with the dynamic [index type](#vector-index-types) 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](#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](#tenant-states) 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](managing-resources-compression.md)
- [Starter guide: Indexing](managing-resources-indexing.md)
- [Starter guide: Tenant states](managing-resources-tenant-states.md)
- [Concepts: Vector Index](../indexing/vector-index.md)
- [Concepts: Vector Quantization](../concepts/vector-quantization.md)
- [Concepts: Multi-Tenancy](../concepts/data.md#multi-tenancy)
- [How-to: Set the vector index type](../how-to-manage-collections/vector-config.md#set-vector-index-type)
- [How-to: Configure vector compression](../how-to-configure-weaviate/compression.md)
- [How-to: Perform multi-tenancy operations](../how-to-manage-collections/multi-tenancy.md)
- [How-to: Manage tenant states](../how-to-manage-collections/tenant-states.md)
## Questions and feedback
Have a question or feedback? Here's how to reach us.
::::card-grid
:::card{title="Community Forum" href="https://forum.weaviate.io/c/support" icon="messages-square"}
Ask questions and connect with other developers on our **Community forum**.
:::
:::card{title="Support" href="/guides/support-overview" icon="life-buoy"}
Weaviate Cloud user or customer? Find the right channel on the **Support page**.
:::
::::
## Related pages
- [Agents](./agents-index.md)
- [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md)
- [APIs](./apis-index.md)
- [Authorization and authentication](./authorization-and-authentication-index.md)
- [Benchmarks](./benchmarks-index.md)
- [Best practices](./best-practices-index.md)
- [Client libraries](./clients-index.md)
- [Client Libraries / SDKs](./client-libraries-index.md)
- [Cloud](./cloud-index.md)
- [Cloud account management](./cloud-account-management-index.md)
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