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ArbitrageOne
ArbitrageOne
ArbitrageOne scores 46/100 on xpay's Agent-Ready Index v2: partial pricing visibility; self-serve checkout is available.
ArbitrageOne pricing
Basic
Contact sales
How ArbitrageOne scores on the agent-ready dimensions
Public pricing visibility
15 / 15
Self-serve checkout
15 / 15
Public API + docs
0 / 20
Usage-based / metered pricing
0 / 20
Free entry path
4 / 7
No sales call required
7 / 7
Card / wallet / x402 payment
5 / 6
Machine-readable bonus (MCP / OpenAPI)
0 / 10
Total
46 / 100
Can an autonomous agent transact here?
Can create account
Can enter payment method
Can upgrade plan
Can buy more usage
Can cancel online
Payment methods
Human approval recommended
Pricing transparency
full
Purchase motion
self_serve
Demo required
Quote required
Annual commit required
Auto-renewal mentioned
Cancellation policy visible
Refund policy visible
Unknown
Invoice payment available
Unknown
Purchase orders supported
Unknown
MSA required
Unknown
DPA available
Unknown
Security review required
Unknown
Trial
14 days (no card)
Positioning & ecosystem
“Alles, was dein Amazon-Business braucht, in einer Plattform.”
Buyer persona
Six-step check: can an agent actually buy from ArbitrageOne?
Discover price
Select a plan
Pay per task
Avoid a sales call
API docs without auth
Estimate cost upfront
Pros and cons for AI agents
Observational summary written by xpay from the signals captured on 2026-05-06. Not a review of the product — only of its current pricing posture for agent buyers.- Pricing is publicly visible on an indexable page — agents can read tiers without scraping past auth.
- No per-unit price was advertised, so an agent has no way to estimate the cost of a single task.
- All advertised tiers route to a sales contact form; an agent cannot complete a purchase autonomously.
- API documentation is gated or absent; an agent cannot inspect the integration surface without authentication.
- No /.well-known/ai-pricing.json or equivalent machine-readable pricing manifest is published — agents must rely on HTML scraping.
- The pricing URL did not render numeric tier data on the scoring date — possibly a router or sales-led landing page.
How ArbitrageOne could lift its score
Add a per-unit price (e.g. $X / 1K calls) to the pricing page so an agent can compute its own cost before committing.
| pricing_visible | false |
| headline_phrasing | Stelle dein Amazon Business auf Autopilot mit ArbitrageOne — ⦿ Schluss mit Google Sheets |
| tier_count | 0 |
| lowest_paid_entry_usd | null |
| free_tier | false |
| free_tier_terms | null |
| per_unit_price | null |
| annual_required | false |
| self_serve_paid_tiers | 0 |
| sales_only_tiers | 0 |
| public_api_docs_url | null |
| api_docs_auth_walled | null |
| ai_pricing_json_present | false |
| agents_txt_present | false |
| anonymous_purchase_path | false |
| per_unit_classification | null |
| usage_headline_present | false |
| custom_tier_present | false |
| agent_friendly | {"ai_pricing_json":false,"agents_txt":false,"llms_txt":false,"sitemap_xml":true,"mcp_server_card":false,"agent_skills_index":false,"x402_supported":false} |
View raw extracted page text →
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See agent-ready picksArbitrageOne
46
/ 100 (rubric v2.0)E Commerce Tools
Stelle dein Amazon Business auf Autopilot mit ArbitrageOne — ⦿ Schluss mit Google Sheets
0
No
Sales-led
0 / 0
Auth-walled
No
Not published
2026-05-09
Tier-based subscription
Free trial
Discovery files and protocols
Side-channel signals — informational, not part of the score. Each protocol is independent; adoption signals the publisher is thinking about agent buyers.ai-pricing.json
agents.txt
llms.txt
sitemap.xml
MCP Server Card
Agent Skills
x402 / MPP
Compare ArbitrageOne with peers
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Explore the Agent-Ready SaaS Index
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