zcal
zcal's pricing URL resolves, but plan structure is not surfaced clearly enough for an agent to enumerate without hand-parsing. No per-unit price was found on the page; an agent has no direct way to estimate the cost of a single task. Every advertised tier is self-serve; no tier requires a sales call. Public API documentation is reachable without an auth wall.
zcal pricing
We’re still verifying zcal’s pricing
Pricing page provides only a free sign‑up CTA with no numeric pricing details; therefore most fields are null or inferred conservatively.
Source of truth: zcal.co/pricing
How zcal scores on the agent-ready dimensions
Public pricing
10 / 15
Usage-based / metered
0 / 25
Self-serve checkout
15 / 15
Public API
15 / 15
Low / no minimum
0 / 10
Unauth automated payment
3 / 10
Bonus (machine-readable pricing)On top of /100 base
0 / 5
Total
53 / 100
Six-step check: can an agent actually buy from zcal?
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.
- Every advertised tier is self-serve; no tier requires scheduling a call.
- No per-unit price was advertised, so an agent has no way to estimate the cost of a single task.
- API documentation is gated or absent; an agent cannot inspect the integration surface without authentication.
- PPU requires account creation and an issued API key; a fully unauthenticated agent purchase is not yet supported.
- No /.well-known/ai-pricing.json or equivalent machine-readable pricing manifest is published — agents must rely on HTML scraping.
How zcal 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 | true |
| headline_phrasing | Pricing and plans | zcal — When2Meet Alternative |
| tier_count | 1 |
| lowest_paid_entry_usd | null |
| free_tier | false |
| free_tier_terms | null |
| per_unit_price | null |
| annual_required | false |
| self_serve_paid_tiers | 1 |
| sales_only_tiers | 0 |
| public_api_docs_url | https://zcal.co/docs |
| api_docs_auth_walled | false |
| 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 →
Claim this scorecard & lift your score.
Get the breakdown of every signal we measured, the one-line fix, and how your peers in Scheduling stack up.
Compare the agent-ready picks in Scheduling.
If you're building or running an AI agent that needs to buy from this category, see who's scoring highest right now.
See agent-ready pickszcal
53
/ 100 (rubric v1.1)Scheduling
Pricing and plans | zcal — When2Meet Alternative
1
No
Sales-led
1 / 0
Public
No
Not published
2026-05-06
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 zcal with peers
Highest-scoring companies in the same category that an AI agent might evaluate as an alternative.Hapio
84
Calendly
80
SavvyCal
80
AppointmentCore
80
Doodle
75
MakePlans
74
Bookafy
73
FlexBooker
73
Explore the Agent-Ready SaaS Index
25,480+ SaaS scored on agent-buyability — browse by readiness band, category, or jump straight to a pay-per-run tool on xpay.tools.
