You are correct that storage is cheaper in S3, but S3 charges per request to GET, LIST, POST, COPY, etc objects in your bucket. Block storage can be cheaper when you are frequently modifying or querying your data.
It is, but it's not _that_ many. AWS pricing is complicated, but for fairly standard services and assuming bulk discounts at ~100TB level, your break-even points for requests/network vs storage happens at:
1. (modifications) 4200 requests per GB stored per month
2. (bandwidth) Updating each byte more than once every 70 days
You'll hit the break-even sooner, typically, since you incur both bandwidth and request charges.
That might sound like a lot, but updating some byte in each 250KB chunk of your data once a month isn't that hard to imagine. Say each user has 1KB of data, 1% are active each month, and you record login data. You'll have 2.5x the break-even request count and pay 2.5x more for requests than storage, and that's only considering the mutations, not the accesses.
You can reduce request costs (not bandwidth though) if you can batch them, but that's not even slightly tenable till a certain scale because of latency, and even when it is you might find that user satisfaction and retention are more expensive than the extra requests you're trying to avoid. Batching is a tool to reduce costs for offline workloads.
thayne|1 year ago
hansvm|1 year ago
1. (modifications) 4200 requests per GB stored per month
2. (bandwidth) Updating each byte more than once every 70 days
You'll hit the break-even sooner, typically, since you incur both bandwidth and request charges.
That might sound like a lot, but updating some byte in each 250KB chunk of your data once a month isn't that hard to imagine. Say each user has 1KB of data, 1% are active each month, and you record login data. You'll have 2.5x the break-even request count and pay 2.5x more for requests than storage, and that's only considering the mutations, not the accesses.
You can reduce request costs (not bandwidth though) if you can batch them, but that's not even slightly tenable till a certain scale because of latency, and even when it is you might find that user satisfaction and retention are more expensive than the extra requests you're trying to avoid. Batching is a tool to reduce costs for offline workloads.