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Hypertune logo

Hypertune

Approaching Agent-Ready
Agent-Ready SaaS Index · hypertune.com · scored 2026-05-09 · rubric v2.0
Score 71 / 100
Ab Testing
Self-serve checkout

Hypertune scores 71/100 on xpay's Agent-Ready Index v2: partial pricing visibility; self-serve checkout is available; API access is partial; usage-based / per-token pricing is published; a free entry path exists.

See Hypertune pricing summaryLive pricing page
Pricing
Hypertune pricing
As of 2026-05-09 · www.hypertune.com/pricing
Free
Free
Unlimited CDN bandwidth and service connections
Unlimited MAUs / MTUs
Unlimited environments and projects
Unlimited feature flags and experiments
Git‑based version control
Starter
$10/user/mo
All Free features
Branches to test flag changes
Pull requests to review and merge
Schema migrations
Flag labels and webhooks
Pro
$20/user/mo
All Starter features
Team roles and approval workflows
Granular flag permissions
Advanced experiment analysis
Dedicated Slack channel
Enterprise
Sales
Contact sales
All Pro features
AI loops for automated experimentation
Bulk user management
Self‑hosting / air‑gapped environments
SAML 2.0 SSO, HIPAA compliance, enterprise SLAs
Score breakdown · rubric v2.0
How Hypertune scores on the agent-ready dimensions

Public pricing visibility

15 / 15

Self-serve checkout

15 / 15

Public API + docs

2 / 20

Usage-based / metered pricing

20 / 20

Free entry path

7 / 7

No sales call required

7 / 7

Card / wallet / x402 payment

5 / 6

Machine-readable bonus (MCP / OpenAPI)

0 / 10

Total
71 / 100
Effective pricing
What it actually costs at typical scale
5 team members on Starter plan, no extra usage
$50/mo
· Each team member billed at $10 per month· Usage stays within the included 2 M requests and 2 M events
Confidence: high
Agent purchase verdict
Can an autonomous agent transact here?
Agent-ready
LLM score: 90/100

Can create account

Yes

Can enter payment method

Yes

Can upgrade plan

Yes

Can buy more usage

Yes

Can cancel online

Yes

Payment methods

card

Human approval recommended

No

Pricing transparency

full

Purchase motion

self_serve

Demo required

No

Quote required

No

Annual commit required

No

Auto-renewal mentioned

No

Cancellation policy visible

No

Refund policy visible

No

Invoice payment available

Unknown

Purchase orders supported

Unknown

MSA required

Unknown

DPA available

Yes

Security review required

Unknown

Trial

Available, no card

API readiness
API & developer surface

API-first product

Unknown

Sandbox present

No

Webhooks

Yes

MCP mentioned

No

Enterprise signals
Trust & compliance

Certifications

SOC2
ISO27001
GDPR
HIPAA

SSO / SAML

Yes

RBAC

Yes

Audit logs

Yes

Data residency

US
EU

Competitive intel
Positioning & ecosystem
Headline

“Predictable, seat-based pricing that scales with you.”

Buyer persona

smb
mid market
enterprise
developer

Integrations mentioned
Vercel
Google Analytics
Mixpanel
Amplitude
Datadog
Agent purchase simulation
Six-step check: can an agent actually buy from Hypertune?
✓

Discover price

✓

Select a plan

✗

Pay per task

✓

Avoid a sales call

◐

API docs without auth

✗

Estimate cost upfront

Analysis
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.
✓ For an AI agent
  • 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.
◐ Opportunities for the provider
  • 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.
One-line fix
How Hypertune 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 — Predictable, seat-based pricing that scales with you. Never worry about unpredictable volume-based pricing.

tier_count

6

lowest_paid_entry_usd

0

free_tier

false

free_tier_terms

null

per_unit_price

null

annual_required

false

self_serve_paid_tiers

5

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":false,"mcp_server_card":false,"agent_skills_index":false,"x402_supported":false}

View raw extracted page text →
Page heading: Pricing
CTAs (31): Why Retool chose HypertuneLearn why Retool chose Hypertune for feature flags and · Docs · Pricing · Blog · Sign in · Start for free · Get started · Contact us · Get started for free · Loading system status... · Feature flags · Experimentation
Price tokens (13): $0 / month · $10 / team member · $10 / million · $30 / millionEverything in · $20 / team member · $10 / million · $30 / millionEverything in · $10 / team member · $20 / team member · $10 / million · $10 / millionCustomCDN band · $30 / million
Why Retool chose HypertuneLearn why Retool chose Hypertune for feature flags and experimentationRead moreDocsPricingBlogSign inStart for freePricingPredictable, seat-based pricing that scales with you. Never worry about unpredictable volume-based pricing.FreeFor testing and evaluation, or small-scale deployments.$0 / month1m CDN requests1m analytics eventsUp to 3 team membersUnlimited CDN bandwidth and service connectionsUnlimited MAUs / MTUsUnlimited environments and projectsUnlimited feature flags and experimentsType-safe, Rust-powered SDKs in all major languagesGit-based version controlHypertune Toolbar to override flags from your frontendIntegrations with Vercel Edge Config, the Vercel Toolbar, and the Flags SDKFlag debuggerSSO via GoogleGet startedStarterFor small teams getting started with feature flags.$10 / team member / month2m requests then $10 / million2m events then $30 / millionEverything in Free, plus...Branches to test flag changes and support old app versionsPull requests to review, discuss, and merge branchesSchema migrationsFlag labelsWebhooksGet startedProFor teams looking to start scaling with flags and experimentation.$20 / team member / month2m requests then $10 / million2m events then $30 / millionEverything in Starter, plus...Team roles and approval workflowsGranular flag permissionsAdvanced experiment analysisSequential testingDedicated Slack channelGet startedEnterpriseFor organizations with high volume, security, control, or support needs.Contact us…
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Are you Hypertune? Something off in this listing?
Agent-Ready Spec
Hypertune
Approaching Agent-Ready

71

/ 100 (rubric v2.0)
Category

Ab Testing

Pricing model

Pricing — Predictable, seat-based pricing that scales with you. Never worry about unpredictable volume-based pricing.

Tier count

6

Free tier

No

Lowest paid entry

Free

Self-serve / sales-only tiers

5 / 0

Public API docs

Auth-walled

Anonymous purchase path

No

Machine-readable pricing

Not published

Last refreshed

2026-05-09

Pricing intelligenceInsights extracted from www.hypertune.com/pricing
Pricing model

Tier-based subscription

Agent-purchase verdict
Agent-ready
90/100 · v2.1
Canonical answer

Hypertune pricing starts at $0/mo for the Free plan; paid plans range from $10 to $20 per team member per month. Billed monthly.

Cheapest entry

Free plan (Free)

Actions
Open live pricing page
Verdict reflects Approaching Agent-Ready status under rubric v2.0. Source observed at https://www.hypertune.com/pricing.
Agent-friendly features0 of 7 present
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

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