The pricing model broke when AI agents arrived

Most scraping APIs were built for predictable scheduled jobs. AI agents created burst traffic, recursive crawling, and unstable billing models built on credits and multipliers

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2.5M owned IPs

195+ countries

USA, Germany, France, etc.

$0.13 / 1,000 requests

flat pricing

Every scraping
API looked the sameEvery scraping API looked the same

Most APIs rented infrastructure
from the same upstream providers

Most APIs rented infrastructure from the same upstream providers

The difference between products mostly came down to:

Branding
Dashboards
Pricing layers
Credit systems

The underlying supply chain stayed the same

Upstream market structure

Circular flow of the scraping API supply chain from the same upstream pool through APIs and brokers to the residential IP network.

Credits made the bill unpredictable

Credits were originally introduced
to simplify pricingCredits were originally introduced to simplify pricing

What starts as a simple request can quickly become several layers of additional cost.

A rendered page costs more than a standard page. Stealth mode increases the multiplier again. Captcha solving adds another pricing layer on top.

As workloads became more complex — especially with recursive crawling and AI agents — the relationship between requests and actual cost became harder to predict.

The harder the workload became, the less predictable the bill became.

Credit multiplier examples

Workload	Multiplier	Effective cost
Standard page	1×	$0.13 / 1k
Rendered page	3×	$0.13 / 1k
Stealth mode	5×	$0.13 / 1k
Captcha solving	10×	$0.13 / 1k
Recursive crawl	∞	Unpredictable

AI agents broke the model

Same agent. Same 30 days. Same ~1M requests. Credit-based bill volatility comes from retry loops, stealth-mode toggles, and deep-crawl multipliers the agent decides at runtime. Geonode charges per request, flat.

Cost over 30 days: volatile spend for an AI agent on credit-based pricing versus steady Geonode flat-rate per-request cost from Day 1 through Day 29. Y-axis dollars on log scale.

The flywheel. Why scale compounds

Most scraping APIs run on rented supply with credit-based pricing. That's why your bill is unpredictable. We chose the opposite path – and the math compounds.

Flat pricing

Same $0.13 per 1,000 for every request. Hard pages or easy ones. Render or no-render. Stealth or wide-open. No multipliers, ever.

Owned network

We own the IP network. Competitors rent theirs and mark it up. We sell direct, so there's no broker margin in your bill.

Volume flywheel

Every customer who joins lowers our per-unit cost. We pass the savings forward — automatically, in the price you already pay.

Data flywheel

Every scrape teaches the network. More volume = smarter routing, better anti-bot evasion, higher reliability. The product gets better as we get bigger.

The unusual part is that scale improves the economics

Most infrastructure companies become more expensive as they grow. More customers usually mean higher support costs, more operational overhead, and rising infrastructure expenses.

Residential pricing

$1.20$0.13

Benchmark efficiency

5.4x cheaper

Network growth

2.5M owned IPs

Routing improvements

+18% success rate

Instead of adding pricing layers as usage grows, the system compounds in the opposite direction.

The incentives stay aligned with the customer.