1) 𝐑𝐢𝐠𝐡𝐭-𝐬𝐢𝐳𝐞 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞𝐬: Match instance types to your workload needs to avoid over-provisioning and reduce compute costs.
2) 𝐔𝐬𝐞 𝐒𝐩𝐨𝐭 𝐈𝐧𝐬𝐭𝐚𝐧𝐜𝐞𝐬: Leverage AWS Spot Instances for non-critical workloads to save up to 90% on compute costs.
3) 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐞 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞 𝐬𝐡𝐮𝐭𝐝𝐨𝐰𝐧𝐬: Use automation tools to turn off dev/test environments outside business hours. (if it's a non-critical 24*7 Resource)
4) 𝐔𝐬𝐞 𝐬𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬: Opt for AWS Lambda or Fargate to run code without provisioning servers, paying only for actual compute time used.
5) 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐒𝐚𝐯𝐢𝐧𝐠𝐬 𝐏𝐥𝐚𝐧𝐬 𝐨𝐫 𝐑𝐞𝐬𝐞𝐫𝐯𝐞𝐝 𝐈𝐧𝐬𝐭𝐚𝐧𝐜𝐞𝐬: Commit to 1- or 3-year usage plans for predictable workloads to reduce costs.
6) 𝐔𝐬𝐞 𝐒3 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 𝐩𝐨𝐥𝐢𝐜𝐢𝐞𝐬: Move infrequently accessed data to cheaper storage classes like S3 Glacier.
7) 𝐔𝐬𝐞 𝐏𝐫𝐢𝐯𝐚𝐭𝐞 𝐈𝐏𝐬 𝐟𝐨𝐫 𝐢𝐧𝐭𝐞𝐫-𝐬𝐞𝐫𝐯𝐢𝐜𝐞 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Ensure services communicate over private IPs within the same VPC to avoid public data transfer costs.
8) 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐝𝐚𝐭𝐚 𝐭𝐫𝐚𝐧𝐬𝐟𝐞𝐫: Use AWS PrivateLink or Direct Connect for internal data transfers to avoid public data transfer charges.
9) 𝐔𝐬𝐞 𝐜𝐚𝐜𝐡𝐢𝐧𝐠: Implement caching with CloudFront or ElastiCache to reduce backend compute and data transfer costs.
10) 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐮𝐧𝐮𝐬𝐞𝐝 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬: Use cost management tools to find and terminate idle resources like unused EBS volumes or Elastic IPs.
11) 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐚𝐮𝐭𝐨-𝐬𝐜𝐚𝐥𝐢𝐧𝐠: Automatically scale your instances to handle traffic spikes and downscale during off-peak times.
12) 𝐂𝐨𝐧𝐬𝐨𝐥𝐢𝐝𝐚𝐭𝐞 𝐂𝐥𝐨𝐮𝐝𝐖𝐚𝐭𝐜𝐡 𝐥𝐨𝐠𝐬: Use CloudWatch log groups efficiently to avoid unnecessary data retention costs.
13) 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐀𝐑𝐌-𝐛𝐚𝐬𝐞𝐝 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞𝐬: Use AWS Graviton-based instances for workloads that support ARM architecture for better cost-performance.
14) 𝐌𝐢𝐧𝐢𝐦𝐢𝐳𝐞 𝐜𝐫𝐨𝐬𝐬-𝐫𝐞𝐠𝐢𝐨𝐧 𝐝𝐚𝐭𝐚 𝐭𝐫𝐚𝐧𝐬𝐟𝐞𝐫𝐬: Keep services in the same AWS region to avoid inter-region data transfer fees. (to achieve high availability, you can keep minimum resources running in other AZs or run in standby mode)
15) 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬 𝐚𝐧𝐝 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐝𝐚𝐭𝐚: Reduce data transfer sizes by compressing files before sending between services.
Did I miss any important points? please add in the comment section below!
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