For teams running large-scale scraping pipelines

    Scrape at scale.
    Keep all of it.

    $4.99/TB flat. No per-PUT charges, no egress, S3-compatible. Collection pipelines that bill per write shrink the dataset the budget allows.

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    No credit card required · No per-PUT fees · Connects in minutes

    High-write pipelines pay per write.

    A scraping pipeline writes constantly by design. At per-PUT pricing, the collection operation itself — not the dataset it produces — becomes the largest line item.

    The PUT bill

    1 billion writes. $5,000 in fees.

    AWS S3 charges $0.005 per 1,000 PUTs. A pipeline writing 1 billion objects per month incurs $5,000 in PUT fees alone — before a byte of storage is billed. The collection operation is its own cost centre.

    The coverage decision

    The budget decides what gets collected.

    Teams start pruning scope not because the data has no value, but because the write cost does not discriminate. Lower-priority domains get dropped. Crawl frequency gets reduced. The dataset reflects budget constraints, not coverage goals.

    The egress hit

    Processing your own collection costs extra.

    Deduplicating, enriching, and classifying the scraped corpus reads it back from storage. On AWS, $0.09/GB egress means reading 10 TB of collected content costs $900. Collection pipelines pay twice.

    Same S3 upload call. Zero per-PUT counter.

    Any scraping framework that writes to S3 works without modification. Swap the endpoint; remove the per-write ceiling.

    scraper.py
    import boto3, os
from concurrent.futures import ThreadPoolExecutor

s3 = boto3.client(
    "s3",
    endpoint_url="https://eu-west-1.s3.fil.one",
    aws_access_key_id=os.environ["FIL_ACCESS_KEY"],
    aws_secret_access_key=os.environ["FIL_SECRET_KEY"],
    region_name="eu-west-1",
)

def store_page(page):
    s3.put_object(
        Bucket="scraped-data",
        Key=f"crawl/{page.domain}/{page.url_hash}.html",
        Body=page.content,
        ContentType="text/html",
    )

# High-throughput parallel upload — no PUT charge
with ThreadPoolExecutor(max_workers=64) as pool:
    pool.map(store_page, scraped_pages)
# 1 billion PUTs this month: $0 in request fees
    AWS S3 Standard$0.005 / 1K PUT$5,000
    Google Cloud$0.05 / 10K ops$5,000
    Azure Blob$0.055 / 10K writes$5,500
    Wasabi$0 per request$0
    Backblaze B2$0 per request$0
    Fil One$0 per request$0

    Public US rate cards, Q2 2026. Storage and egress not included — request fees only. AWS: 1,000,000,000 / 1,000 × $0.005 = $5,000. Google: 1,000,000,000 / 10,000 × $0.05 = $5,000. Azure: 1,000,000,000 / 10,000 × $0.055 = $5,500.

    Collection cost that scales with dataset size, not write rate.

    The only change is the endpoint. Write volume stops being a billing event.

    No per-PUT fees

    1 billion PUTs per month on Fil One costs $0 in request charges. The only cost is the storage that results from collection.

    No egress on processing

    Read the scraped corpus back for enrichment, deduplication, or classification without a $0.09/GB charge on every pass.

    Predictable collection cost

    Storage at $4.99/TB. The write rate determines the dataset size; the dataset size determines the cost. No request-rate multiplier.

    S3-compatible, drop-in

    Any framework that writes files — Scrapy, Crawlee, Playwright pipelines — works with standard S3 upload APIs. Swap the endpoint.

    One rate. $4.99/TB/month.

    Storage. That is the whole bill. No per-PUT fees, no egress. Collection pipelines cost what they produce, not what they write.

    Start for freeTalk to an expert

    No credit card required · No per-PUT fees · Connects in minutes

    Collect without the PUT counter.

    Free 1 TB evaluation. Swap the endpoint in your existing scraping framework and watch the request line zero out.

    Start for freeTalk to an expert

    No credit card required · No per-PUT fees · Connects in minutes