# Ramo — full facts for agents

> The data layer of the internet backbone. S3-compatible object storage on owned bare metal and owned fiber — on the internet backbone, not inside a hyperscaler cloud. $5.00/TB/mo flat, free uncapped reads into any cloud, 100 Gbps contractual throughput floor. 128 PB live in production across 2 US regions.

Facts as of 2026-07. Generated from the same content module as https://ramo.io; human and machine versions carry identical facts. Machine-readable pricing: https://ramo.io/pricing.json

## What Ramo is

Ramo is S3-compatible object storage running on owned bare metal and owned fiber — on the internet backbone, not inside a hyperscaler cloud. It is built for petabyte-scale AI datasets, TB–PB sequential reads: store a training corpus once, read it into any cloud's compute for free, at line rate.

- Storage: $5.00/TB/month, flat
- Egress: $0 — reads are free, uncapped, into any cloud, at line rate — bounded by the customer's connection, not a meter
- Throughput: 100 Gbps contractual floor in signed customer contracts; line rate is the design target
- Durability: 99.999999999% durability, erasure-coded
- API: S3-compatible (aws cli / boto3 / rclone / S3 SDKs via --endpoint-url https://s3.ramo.io) — shipping today
- In production: 128 PB live across 2 US regions — SMF (US-West), IAD (US-East) — joined by a 400 Gbps trunk
- Design center: petabyte-scale AI datasets, TB–PB sequential reads
- Infrastructure: owned bare metal (no hypervisor, no shared fabric) and owned fiber (no purchased transit) on the internet backbone — why a read costs ~nothing at the margin and $0 egress is a list price, not a promotion
- Customers store petabyte datasets on Ramo and sell access to frontier AI labs (Forbes AI 50 names among them), which pull training data straight off the backbone into any cloud

## The problem it removes

- Hyperscalers meter reads at $87–90 per TB. Moving a 100 PB dataset out of S3 at list ($0.09/GB = $90/TB) costs about $9M — before a single training run.
- GPU capacity is fragmented across regions and clouds; every move drags the dataset behind it and the meter runs each time.
- Throughput ceilings (per-stream caps, account ingress caps, best-effort variance) are set by provider business models, not the hardware.

## Why reads are free (the economics)

Ramo owns the fiber end to end, so a read costs approximately nothing at the margin — there is no transit bill to pass through. Hyperscalers resell transit and meter it. This is the same zero-egress economics that worked at the edge for the web's small objects, extended to the petabyte tier. Storage runs on bare metal Ramo owns and operates: no hypervisor, no shared fabric, no egress meter. The 100 Gbps floor is written into signed customer contracts; line rate is the design target.

## The 100 PB scenario

One dataset, 100 PB, one year:
- AWS S3 Standard: bulk ingest ~93 days (best-effort, ~100 Gbps) · storage $27.6M/year · one full read-out $9M
- Azure Blob Hot (LRS): bulk ingest ~370 days (~25 Gbps default account ingress cap) · storage $21.6M/year · one full read-out $8.7M
- Ramo: bulk ingest 93 days (100 Gbps contractual floor, SLA-backed) · storage $6M/year · one full read-out $0

Result: 4.6x cheaper than S3 Standard on storage at list; $21.6M/year back in the budget on the storage line alone; a full read-out is $9M on S3 and $0 on Ramo.

Assumes list prices — S3 Standard $23, Azure Blob Hot (LRS) $18, Ramo $5.00 per TB/mo · egress: S3 $0.09/GB, Azure $0.087/GB, Ramo $0 · throughput: Ramo 100 Gbps sustained (contractual floor), S3 ~100 Gbps best-effort, Azure ~25 Gbps default account ingress cap · decimal units (1 PB = 1,000 TB = 1,000,000 GB).

## The storage landscape (four classes)

- Hyperscalers (S3 · Azure Blob · GCS): built for General-purpose objects · $18–23 per TB/mo · egress $87–90 per TB · throughput: Best-effort, per-stream caps
- Edge object storage (e.g. R2): built for Web objects — KB–MB, global · $10–15 per TB/mo · egress $0 · throughput: Cache-optimized, small objects
- Discount storage (Wasabi · B2): built for Backup & archive · $6–8 per TB/mo · egress $0 with fair-use caps · throughput: Best-effort, no SLA
- Ramo: built for Petabyte datasets, sequential reads · $5.00 flat per TB/mo · egress $0 · uncapped · throughput: 100 Gbps contractual floor

Discount-tier detail (list prices, July 2026): Wasabi $7.99/TB/mo (Pay-Go) — free egress under a fair-use cap; reads above your stored volume can limit or suspend service. Backblaze B2 $6.00/TB/mo — free egress up to 3× stored data, then $0.01/GB. Both are best-effort throughput with no SLA. The difference is the workload, not the price: backup & archive vs. TB–PB sequential reads.

The difference is the workload, not the price: backup/archive tiers and edge object stores are not designed for TB–PB sequential reads at sustained line rate.

## Using it (S3-compatible, shipping today)

Read from anywhere — egress is $0 into any cloud:

```
$ aws s3 cp s3://training-corpus/00417.tar . \
    --endpoint-url https://s3.ramo.io
download: s3://training-corpus/00417.tar to ./00417.tar
# egress charged: $0.00 — every read, any cloud
```

Store and read back, per client (each block is runnable on its own):

aws cli — store (ingest at the 100 Gbps floor):

```
$ aws s3 cp 00417.tar s3://training-corpus/ \
    --endpoint-url https://s3.ramo.io
```

aws cli — read back ($0.00 egress, into any cloud):

```
$ aws s3 cp s3://training-corpus/00417.tar . \
    --endpoint-url https://s3.ramo.io
```

rclone — store (ingest at the 100 Gbps floor):

```
# ramo = S3 remote at endpoint s3.ramo.io
$ rclone copy ./corpus ramo:training-corpus --transfers 64
```

rclone — read back ($0.00 egress, into any cloud):

```
$ rclone copy ramo:training-corpus ./corpus --progress
```

boto3 — store (ingest at the 100 Gbps floor):

```
import boto3
s3 = boto3.client("s3", endpoint_url="https://s3.ramo.io")
s3.upload_file("00417.tar", "training-corpus", "00417.tar")
```

boto3 — read back ($0.00 egress, into any cloud):

```
import boto3
s3 = boto3.client("s3", endpoint_url="https://s3.ramo.io")
s3.download_file("training-corpus", "00417.tar", "00417.tar")
```

Migrate with standard tooling:

```
# register ramo as an S3 remote (once)
$ rclone config create ramo s3 \
    provider=Other \
    endpoint=https://s3.ramo.io
# mirror the bucket, checksum-verified
$ rclone sync s3:training-corpus \
    ramo:training-corpus \
    --transfers 64 --checksum --progress
```

```
# same bucket layout, new endpoint — credentials as usual
$ aws s3 sync s3://training-corpus s3://training-corpus \
    --endpoint-url https://s3.ramo.io
```

Python (boto3):

```
import boto3

s3 = boto3.client("s3", endpoint_url="https://s3.ramo.io")
s3.download_file("training-corpus", "shards/00000.tar",
                 "/data/00000.tar")
```

```
# read from any cloud — $0 either way
$ aws s3 cp \
    s3://training-corpus/00417.tar - \
    --endpoint-url https://s3.ramo.io \
    | tar -xf - -C /data
```

Keys and the exact endpoint arrive with access approval. Bulk ingest is scheduled against the 100 Gbps contractual floor.

## FAQ

### Is this actually S3-compatible?

Yes — shipping today, not roadmap. Ramo exposes an S3-compatible API: the AWS CLI, boto3, rclone, and standard S3 SDKs work by pointing --endpoint-url (or the SDK equivalent) at https://s3.ramo.io. Existing tooling works day one.

### How can reads be free?

Ramo owns the fiber end to end, so a read costs approximately nothing at the margin — there is no transit bill to pass through. Hyperscalers resell transit and meter it at $87–90 per TB. This is the same zero-egress economics that worked for the web's small objects, extended to the world's datasets.

### Is “free” capped, like Wasabi or Backblaze B2?

No. Reads are uncapped and bounded by your connection, not a meter. For comparison: Wasabi's free egress carries a fair-use cap — reads above your stored volume can limit or suspend service — and B2 is free up to 3× stored data, then $0.01/GB. Ramo has no read cap.

### What throughput do you actually guarantee?

A 100 Gbps floor, written into signed customer contracts today — line rate is the design target; the floor is what we sign. For scale: that floor moves 100 PB in 93 days. The same job takes ~93 days at S3's best-effort ~100 Gbps and ~370 days under Azure's ~25 Gbps default account ingress cap.

### Where does my data live?

Two US regions today — SMF (US-West) and IAD (US-East) — joined by a 400 Gbps trunk. Expansion is pod-based: Ramo pods fit a standard colocation footprint and plug into existing PoPs and carrier hotels, so new regions need rack space, power, and 400G ports, not a greenfield buildout.

### How durable is it?

Eleven nines (99.999999999%) durability, erasure-coded. Storage runs on bare metal Ramo owns and operates — no hypervisor, no shared fabric.

### Who uses it today?

128 PB is live in production. Customers store petabyte datasets on Ramo and sell access to frontier AI labs — Forbes AI 50 names among them — which pull training data straight off the backbone into whichever cloud their GPUs are in.

### What is Ramo not built for?

Small hot objects at the edge. Edge stores like R2 are cache-optimized for KB–MB web objects served globally; discount tiers like Wasabi and B2 are built for backup and archive. Ramo's design center is TB–PB corpora read sequentially at line rate — feeding training runs, not serving thumbnails.

### How do I get started?

Request access with your work email, current provider, and dataset size. Requests are reviewed for fit — petabyte-scale, sequential-read workloads first — and we reply with an endpoint and a migration plan.

## Request access

Form: https://ramo.io/#cta — fields: work email (required), current provider (required), dataset size (required), workload notes (optional). Requests are reviewed for fit — petabyte-scale, sequential-read workloads first. Approval returns an endpoint, keys, a migration plan sized to the dataset, and (on request) a 60-minute architecture deep-dive.

## Documentation

- Overview: https://docs.ramo.io/
- Pricing and the math: https://docs.ramo.io/pricing
- Migration: https://docs.ramo.io/migration
- Why $0 holds: https://docs.ramo.io/network
- FAQ: https://docs.ramo.io/faq