Proxon
Summary
AI tools are already inside your company — approved or not — and nobody has a complete list of what's running, who owns it, or what it costs. Proxon is the management layer that builds that inventory and keeps it current.
Proxon surfaces AI activity across teams into a single operating record: which tools and agents exist, who owns them, what they cost, and whether they're producing results. The attribution layer connects token spend to specific teams and workflows rather than burying it in an undifferentiated API bill. Adoption tracking lets leadership identify high-performing workflows and push them to teams still running ad hoc. The ceiling appears when you need deep integrations with proprietary internal tooling — the vendor page describes discovery and governance but does not detail connector coverage, so teams with niche or self-built stacks will need to validate fit before committing.
Bottom line: Proxon earns its place when a VP asks 'what AI are we actually running and what is it costing us?' and nobody can answer — but if your governance need is enforcing fine-grained model-level access controls or integrating with a homegrown orchestration layer, you will hit gaps the dashboard cannot fill.
Pricing Plans
Subscription- Free Tier
- <10 employees, up to 25 AI systems, up to 10 workflows
FREE
Get AI under control for <10 employees
- Basic adoption snapshot
- Up to 25 AI systems tracked
- Up to 10 workflows tracked
- Email support
STARTUP
Manage AI as you grow for 10–99 employees
- Full org, team, and employee analytics
- Up to 250 AI systems tracked
- Up to 100 workflows tracked
- Team and workflow cost attribution
- Policy templates and audit trails
- Priority support
SCALING
Scale AI company-wide for 100–499 employees
- Full org, team, and employee analytics
- Up to 1,000 AI systems tracked
- Up to 500 workflows tracked
- Team and workflow cost attribution
- Policy templates and audit trails
- Priority support
ENTERPRISE
Operate AI at scale for 500+ employees
- Unlimited systems and workflows
- Custom integrations and reporting
- SSO, SAML, and advanced controls
- Custom policies and approval flows
- Dedicated success and security review
- Procurement and legal support
View full pricing on proxon.ai →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Single inventory of all AI tools, agents, and workflows across the organization, so leadership stops discovering production AI systems by accident during an incident or audit.
- Spend attribution down to the team, workflow, and model level, which means the monthly API bill becomes an actionable breakdown instead of a number nobody can explain.
- Built-in optimization signals that flag redundant model work and suggest caching or routing changes, so engineering doesn't need a separate cost analysis pass to find waste.
- Adoption trend tracking by team, which means high-performing workflows can be identified and pushed to underperforming teams rather than staying siloed with whoever built them first.
- API access for exporting governance and cost data, so Proxon can feed into existing dashboards or alerting pipelines rather than requiring a separate login for every stakeholder.
Cons
Sign in to edit- The vendor page does not enumerate which AI platforms, model providers, or agent frameworks Proxon connects to natively. Teams running self-built or niche tooling will find gaps in the discovery layer — activity that runs outside supported connectors stays invisible, which defeats the core premise of a complete inventory.
- No self-hosted deployment option is described. Organizations in regulated sectors where activity metadata cannot leave their own infrastructure hit a hard architectural wall here, and at that point the evaluation moves to purpose-built on-premises governance tools regardless of what Proxon offers.
- Policy enforcement and approval flows are listed as capabilities but the vendor page does not show the depth of the rules engine. Teams that need granular, conditional access controls — for example, blocking specific models for specific data classifications — will need to pressure-test whether Proxon's governance layer goes deep enough or whether they need a dedicated AI access control product alongside it.
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About
- Platforms
- Web
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-07-14T09:01:45.737Z
Best For
Who it's for
- Companies scaling AI usage beyond experiments
- Teams needing centralized AI governance and visibility
- Organizations requiring cost tracking and policy compliance for AI
What it does well
- Track AI system and workflow adoption across teams
- Attribute costs to specific teams and workflows
- Enforce policies with audit trails and approval flows
- Identify and propagate high-performing AI patterns
Integrations
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Frequently Asked Questions
- Is Proxon free?
- Proxon has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is Proxon open source?
- No — Proxon is a closed-source tool. Source code is not publicly available.
- Does Proxon have an API?
- Yes. Proxon exposes a developer API. See the official documentation at https://proxon.ai for details.
- What platforms does Proxon support?
- Proxon is available on: Web.
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Curated lists that include this category
Most organizations reach a point where AI usage has grown faster than oversight: tools procured without approval, agents with no clear owner, and a monthly model API bill that nobody can break down by team or outcome. Proxon addresses this by creating one operating record for AI activity across the company. The workflow runs in five stages the vendor describes as discover, govern, attribute, measure, and propagate — building an inventory first, then attaching ownership and policy, then connecting spend to the work behind it, then tracking adoption and outcomes, and finally helping effective workflows reach more teams.
The cost intelligence layer is the feature that differentiates Proxon from generic SaaS spend trackers. Rather than reporting total API spend, it attributes costs to vendors, models, teams, workflows, and individual owners. The vendor page shows an optimization signal that flags repeated model work and suggests routing or caching changes — which means leadership can act on waste, not just observe it.
Proxon fits organizations that have moved past isolated AI experiments and need a shared control point across functions — engineering, sales, finance, customer success, and operations are all shown in the vendor’s demo. It does not self-host, which is a hard stop for teams in regulated industries whose data governance policies prohibit sending activity metadata to a third-party SaaS. The vendor page also does not detail which AI platforms and API providers Proxon connects to natively, so teams running less common tooling need to verify integration coverage before assuming discovery will be complete.
An API is available, which means teams can pipe Proxon data into existing BI or incident workflows rather than treating it as a standalone dashboard. The vendor page does not describe the API’s scope in detail, so the degree to which it exposes raw adoption, cost, and compliance data programmatically requires direct confirmation.