Open Search charging for over 140 OCU-Hours for a tiny dataset

When building a proof of concept (POC) for a large project with potentially hundreds of thousands of documents, cost-effectiveness is crucial. If you're experiencing substantial costs during your pilot with Bedrock/OpenSearch, there are several factors to consider:

• Indexing Frequency: Ensure that you're not indexing data more frequently than necessary. Frequent indexing can lead to increased costs, especially if you're using a pay-per-use pricing model.
• Indexing Volume: Review the volume of data being indexed. If you're indexing a large number of documents, it can contribute to higher costs. Consider optimizing your indexing process to reduce the number of documents indexed or batch indexing to minimize costs.
• Instance Type: Check the instance type and configuration of your Bedrock/OpenSearch cluster. Using larger or more powerful instances than necessary can result in higher costs. Evaluate your performance requirements and adjust instance types accordingly.
• Storage Costs: Review the storage configuration of your cluster. Storage costs can vary depending on the volume and type of data stored. Consider optimizing data storage by leveraging features like data compression or tiered storage optios.
• Usage Monitoring: Monitor your usage and costs closely using AWS Cost Explorer or similar tools. Identify any unexpected spikes in usage or costs and investigate the underlying causes.
• Cost Optimization Strategies: Implement cost optimization strategies such as resource tagging, reserved instances, or spot instances to reduce costs while maintaining performance.