I'm developing an application that needs to support multiple regions for a global user base and reduce latency. To achieve this, I've considered setting up regional collections where all but one would serve as read replicas. Cross-region replication would occur through OpenSearch Integrations with S3. With at least three regions, that means I'd need a minimum of 9 OpenSearch Capacity Units (OCUs) costing about $1555 monthly. This seems excessive, especially for startups with tight budgets. Are there any cost-effective alternatives out there?
5 Answers
Having a global presence definitely comes at a price, especially with AWS. You're paying for the convenience of a serverless option. You might want to compare these costs to running OpenSearch on EC2 instances by managing it yourself. Just remember, managing those instances can add its own costs and headaches.
Before diving into multi-region setups, have you ensured that OpenSearch is truly necessary for your application? My company serves clients from the US and EU using a single infrastructure in Australia without any issues. Plus, managing serverless effectively often means paying for scaling capabilities you might not use constantly.
Do you really need to worry about latency for such an early-stage product? I’d suggest starting in the region where most of your customers are located and adding more regions later as demand grows.
It’s costly because serverless handles all the operations for you. If you want more control, sure, you can set it up yourself with EC2 instances, but that will eat up a lot of your time in the long run.
Have you considered using a different solution that fits your budget better? For instance, utilizing a Postgres database for full-text search could be a more economical choice if you're not at a scale that justifies OpenSearch.
Absolutely! People often underestimate the effort needed for management. It’s not just about setting it up; you’ve got updating, backups, and recovery to think about. Managed services might seem pricey, but they save a lot of long-term hassle.