Understanding Azure Search Cost and Pricing

Azure Search pricing is based on the number of documents indexed, with a free tier available for small projects and a pay-as-you-go model for larger ones.

The free tier includes 1 index, 1 data source, and 1,000 documents, which is perfect for small projects or testing the service.

You only pay for what you use, with a cost of $0.000004 per document in the pay-as-you-go model, which can add up quickly for large datasets.

This pricing model allows you to scale your search service up or down as needed, without being locked into a specific pricing plan.

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Azure Search Pricing is a crucial aspect to consider when implementing a search experience in your application. You can choose from various pricing tiers, including Basic, Standard S1, Standard S2, Standard S3, Storage Optimized L1, and Storage Optimized L2.

Each tier has its own storage limits, with the Basic tier offering 50 MB of storage, while the Standard S3 tier offers 200 GB. Storage Optimized L2, on the other hand, offers 2 TB of storage.

The pricing tiers also have different scale out limits, with the Basic tier not having any, while the Standard S2 and Standard S3 tiers allow up to 36 units per service. Storage Optimized L2 also allows up to 36 units per service.

Here's a breakdown of the pricing tiers and their corresponding prices per unit:

To select the right pricing tier for your business, you should consider your ideal search experience and the storage size required for your search index. The storage size of your search index will typically be smaller than the size of your raw data, particularly for files like PDFs and images.

For more insights, see: Azure Index Search

To minimize costs of an Azure AI Search solution, follow these best practices.

Create a search service in a region that has more storage per partition, and group your Azure resources in the same region or as few regions as possible to avoid bandwidth charges.

Scaling up for resource-intensive operations like indexing and then readjusting downwards for regular query workloads can help reduce costs. For example, you can synchronize scale up with predictable patterns in your workload.

Keep in mind that pricing and capacity aren't linear, so doubling capacity more than doubles the cost on the same tier.

If you're looking to take your Azure Search to the next level, you'll want to explore the additional AI features available. These features can be billed separately, and they're designed to help you get more out of your search results.

Custom Entity Lookup Skill is one such feature, which allows you to look for text from a custom, user-defined list of words and phrases. This feature is available for all Basic, Standard, and Storage-Optimized tiers.

The pricing for Custom Entity Lookup Skill varies depending on the number of text records you have. For 0-1M text records, it costs ￥10.176 per 1,000 text records. For 1M-3M text records, it costs ￥7.632 per 1,000 text records.

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Here's a breakdown of the pricing for Custom Entity Lookup Skill:

Document Cracking: Image Extraction is another feature that can be billed separately. This feature extracts content from a file within the enrichment pipeline, and it's billed during the initial document cracking step and when invoking the Document Extraction skill.

The pricing for Document Cracking: Image Extraction varies depending on the number of images you have. For 0-1M images, it costs ￥10.18 per 1,000 transactions. For 1M-5M images, it costs ￥8.14 per 1,000 transactions.

Here's a breakdown of the pricing for Document Cracking: Image Extraction:

Semantic ranker is another feature that can be billed separately. This feature uses AI models to improve the relevance of the search results by finding content that is semantically similar to query terms. The service is only available for accounts on Basic, Standard tiers (S1, S2, and S3), and Storage-Optimized (L1 and L2) and has two pricing plans within those tiers.

The pricing for Semantic ranker is free for the first 1,000 requests per month, and then it costs ￥10 per 1,000 additional requests.

Here's an interesting read: Azure Semantic Search

Azure Search offers robust service and support to ensure a smooth and reliable experience.

You can get help through various channels, including the Azure Search documentation, which provides detailed guides and tutorials.

The Azure Search community is active and engaged, offering a wealth of knowledge and expertise to help you troubleshoot and resolve issues.

Azure Search also provides a 99.9% uptime guarantee, ensuring that your search service is always available and running smoothly.

You can also contact Azure Support directly for personalized assistance and guidance.

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You're charged for Azure AI Search based on capacity, specifically search units (SUs), which are calculated as replicas multiplied by partitions (R x P = SU).

You're also charged an hourly rate based on the pricing tier of your search service, prorated to the hour.

Here's a breakdown of the costs associated with premium features:

Some premium features, like knowledge store, debug sessions, and enrichment cache, have a dependency on Azure Storage, which incurs additional storage costs.

Azure Search cost can be broken down into several components, including pricing options for additional features, pricing details for the service itself, and pricing plans for different tiers.

The pricing options for additional features include a custom entity lookup skill, document cracking: image extraction, and a semantic ranker. The custom entity lookup skill is available for all tiers and looks for text from a custom list of words and phrases, labeling all documents containing matching entities. Pricing for this feature starts at $- per 1,000 text records for 0-1M text records.

Document cracking: image extraction is another feature that can be added to Azure Search, and it's billed during the initial document cracking step and when invoking the Document Extraction skill. Pricing for this feature starts at $- per 1,000 transactions for 0-1M images.

Azure AI Search also allows developers to set up and scale a search experience quickly and cost-effectively. The service can scale out to meet changing data or throughput needs, and then scale back down to reduce costs. Units can be combined to gain more queries per second, or a higher document count, or both.

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High density (HD) mode is an optional setting available for standard S3, which enables customers to pack in a higher number of indices per Azure AI Search service. This is ideal for customers building multi-tenant SaaS apps that have a large number of small tenants, trials, or free accounts.

Here are the pricing details for different tiers: