# Google Cloud Storage

Sedai continuously analyzes Google Cloud Storage access patterns and recommends the lowest-cost class for every object, plus lifecycle-rule insights.

## Stop Paying Standard Rates for Cold Data

Most GCS data lands in the Standard storage class by default, regardless of how often it's actually accessed — an expensive fit for logs, backups, and historical data. Sedai continuously analyzes object-level access patterns and recommends the lowest-cost storage class that still meets your needs.

## Storage Class Shouldn't Be an Accident.

GCS defaults nearly everything to Standard, the class built for frequently accessed data — which makes it the most expensive place for data that isn't.

### Default settings don't account for access frequency.

Default settings don't account for access frequency. Logs, backups, and historical analytics data often sit in Standard long after anyone actually needs fast access to them.

### The right tier depends on a pattern, not a one-time decision.

Nearline, Coldline, and Archive each fit a different access frequency, and that frequency changes as data ages.

### Tracking access patterns object-by-object isn't practical manually.

Without continuous visibility, dormant data stays in an expensive tier well past the point it should have moved.

## How We Help

### Continuous Access Pattern Analysis

Sedai monitors access frequency across every object in your buckets as an automated background process — no manual auditing of storage patterns required.

### Storage Class Transition Recommendations

Based on observed access patterns, Sedai recommends the right tier (Standard, Nearline, Coldline, or Archive) so each object sits in the lowest-cost class that still fits its use.

### Lifecycle Rule & Bucket-Level Insights

Sedai flags existing lifecycle rules that could transition dormant data sooner, and identifies entire buckets where a different default storage class would save money.

## Cold Data Doesn't Belong in a Hot Storage Tier.

## Frequently Asked Questions

### Does Sedai automatically move data between storage classes?

Not currently. Sedai analyzes access patterns and generates storage class recommendations, which are made available in the platform for your review — it doesn't execute transitions autonomously.

### How does Sedai decide which storage class fits an object?

Sedai continuously monitors how often each object is accessed and matches that pattern to the appropriate class — for example, recommending Nearline for data untouched for over 30 days.

### What's the difference between Nearline, Coldline, and Archive recommendations?

Nearline fits data accessed less than once a month, Coldline less than once a quarter, and Archive is for long-term data you rarely, if ever, need to retrieve.

### Can Sedai help with lifecycle rules I've already set up?

Yes. Sedai reviews existing lifecycle rules and flags ones that could be tightened to move long-dormant data to cheaper tiers sooner than they currently do.

### Does this only work at the object level, or can it evaluate entire buckets?

Both. Sedai analyzes individual objects for transition opportunities, and can also flag buckets where the overall access pattern suggests a different default storage class would be more cost-effective.