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Manny Medina, the co-founder and former CEO of Outreach, launched Paid in London in March 2025 to help software companies price, meter and bill AI-agent work. The startup’s premise is straightforward: when software performs variable amounts of work at variable cost, a per-seat subscription may no longer explain either the customer’s value or the vendor’s margin.
Paid initially raised roughly $10 million to $11 million, with coverage also describing the amount as €10 million. In September 2025, the company announced a $21 million seed round led by Lightspeed Venture Partners, with FUSE and EQT Ventures participating. Paid said the financing brought its total funding to $33.3 million.
Who is Manny Medina?
Medina co-founded Outreach in 2014 with Wes Hather, Gordon Hempton and Andrew Kinzer. He stepped down as Outreach’s chief executive in September 2024, becoming executive chairman and remaining on the board, according to launch coverage. Outreach raised nearly $500 million and reached a reported $4.4 billion valuation after a $200 million financing in 2021. GeekWire’s launch report is the source for those historical figures.
That background matters because Medina experienced the commercial problem inside a large SaaS company: software can gain autonomous or agent-like capabilities while the company’s pricing, cost accounting and billing systems remain designed for human users and predictable subscriptions.
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What Paid is building
Paid describes itself as a monetization and “business engine” for AI companies, not just an invoice generator. Its public product combines several functions:
- Pricing design: Configure subscriptions, per-seat charges, credits, usage fees, outcome fees or combinations of them.
- Metering: Record agent actions and results as billable “Signals.”
- Cost accounting: Attribute model, tool, infrastructure and agent costs to products, customers and actions.
- Margin analysis: Compare customer revenue with the cost of serving that customer.
- Value communication: Produce receipts or dashboards showing results such as hours saved, tickets resolved or revenue generated.
- Billing: Turn recorded events into invoices.
Medina has also discussed adjacent areas such as collections, revenue recognition and vendor management. Those are longer-term ambitions or adjacent capabilities; Paid’s publicly documented product today centers on pricing, Signals, cost and margin visibility, value proof and invoicing. See Paid’s product overview and getting-started guide.
Why AI agents strain seat-based SaaS pricing
Traditional SaaS pricing generally assumes a defined product, human users, relatively predictable account costs and value delivered through access to features. An AI agent breaks those assumptions.
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- An agent can perform research, outreach, document processing or support work that previously required multiple people.
- Each task can trigger model inference, retrieval, browser actions, tool calls and external API charges.
- The customer may care about a completed result, not the number of prompts or tokens consumed.
- The cost and duration of a task can vary widely, making a fixed seat price either unprofitable for the vendor or hard to justify to the buyer.
In a Sequoia Capital podcast, Medina said conventional software infrastructure did not answer basic questions such as what an agent cost to run, what value it created and what margin the vendor earned. Paid’s later funding announcement makes the same case while positioning the company for SaaS businesses moving toward agent-based products: Paid’s September 2025 announcement.
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Which pricing models does Paid support?
| Model | What the customer pays for | Main consideration |
|---|---|---|
| One-time fee | A setup, delivery or other single charge | Simple, but does not track continuing cost. |
| Recurring platform fee | Ongoing access to the product | Provides a predictable base for the vendor. |
| Per-seat | Named or permitted users | May not fit autonomous work performed by agents. |
| Usage | Recorded activity Signals, such as emails or documents | Easier to meter, but activity does not always equal value. |
| Outcome | A defined successful result, such as a booked meeting | Requires precise definitions and dispute rules. |
| Credits | A prepaid or bundled balance consumed by activity | Flexible for the vendor, but potentially less transparent. |
| Hybrid | A fixed fee plus usage or performance charges | Can balance predictability and upside, but needs clear limits and overage terms. |
Paid’s documentation illustrates an AI sales-development agent with a monthly platform fee, a per-email charge and a fee for each meeting booked. That is a vendor example, not a public recommendation or a standard Paid price list. The documentation example should be checked for current syntax and terminology.
Input, activity, outcome and value pricing are different
Input-based pricing charges for tokens, API calls or compute time. Activity-based pricing charges for actions such as emails sent, documents processed or tickets handled. Outcome-based pricing charges for a successful result. Value-based pricing attempts to tie the fee to economic value, such as savings or incremental revenue. A hybrid model combines these approaches with a base subscription.
Outcome pricing is not automatically better. It moves measurement and attribution into the commercial relationship. A customer can dispute whether an agent caused a meeting, whether a ticket was truly resolved or whether a human had to do enough work that the result should be billed at all. Delayed outcomes, partial success, retries and refunds also need explicit treatment in the contract.
How the product works
- Create a Paid account.
- Create a Product representing the agent or AI service.
- Configure pricing for the chosen subscription, usage, outcome, credit or hybrid model.
- Create a Customer.
- Create an Order connecting that customer to the product.
- Add an API key.
- Send Signals when the agent performs a billable action or achieves a billable result.
- Generate an invoice from the recorded events.
Paid says its onboarding can send an initial Signal in under five minutes and offers SDKs for Node.js, Python, Go and Ruby. It also advertises instrumentation or wrappers for major model providers; the supported list can change. Its public JavaScript example uses PaidClient and a PaidOpenAI wrapper:
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import { PaidClient, PaidOpenAI } from '@paid-ai/paid-node'
import OpenAI from 'openai'
const client = new PaidClient({ token: '<your_paid_api_key>' })
await client.initializeTracing()
const openaiClient = new OpenAI({ apiKey: '<your_openai_api_key>' })
const paidOpenAiWrapper = new PaidOpenAI(openaiClient)
await client.trace('<your_external_customer_id>', async () =>
paidOpenAiWrapper.images.generate({ /* ... */ })
)This is vendor-provided example code, not independently tested code. Teams should verify the current package names, authentication requirements, supported languages and production-readiness in the live onboarding material and cost-tracking documentation.
The hard part is proving an outcome
A credible AI-agent bill needs an event trail that a customer can understand and a finance team can reproduce. Before choosing outcome pricing, define:
- What counts as success and who decides.
- Whether a human-assisted result is billable.
- How partial completion, failed tool calls, retries and duplicate events are handled.
- When a delayed result becomes billable.
- How refunds, reversals and credits change the invoice.
- What evidence is retained for a dispute.
There are also economic risks. A model provider can change prices, autonomous usage can spike, and support or human review can erase an apparently healthy gross margin. Signals may contain sensitive information, so privacy, access controls, retention and regulatory obligations need review. Revenue recognition, credits, refunds and performance fees should be reviewed by finance and accounting teams rather than inferred from a billing configuration.
Funding and the company’s change in emphasis
Launch coverage in March 2025 reported more than $10 million, approximately $11 million or €10 million in initial funding, depending on the publication and currency framing. Reported investors included EQT Ventures, Sequoia Capital, GTMfund, Exceptional Capital, Alt Capital, SV Angel and Founders’ Co-op. TechCrunch and GeekWire used different descriptions of that round.
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Paid’s later announcement reported a $21 million seed round, led by Lightspeed Venture Partners with FUSE and EQT Ventures participating, and said total funding reached $33.3 million. The updated positioning is broader than the launch slogan “help AI agents get paid”: it targets SaaS companies adding agents, AI application companies and businesses selling software based on outcomes rather than seats.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who may benefit from Paid?
Potentially strong fit
- An AI-agent or AI-application company with materially variable model and tool costs.
- A SaaS company moving from pilots to commercial agent workflows.
- A team testing several pricing models without rebuilding metering and invoicing each time.
- A vendor that needs customer-level gross-margin visibility and a defensible value receipt.
Potentially weak fit
- A simple subscription business with predictable costs.
- A company that already has mature metering, billing, revenue-recognition and finance systems.
- A product without a reliable, contractually clear definition of an outcome.
- A business whose customers reject variable or performance-linked charges.
- A buyer needing a complete ERP, tax, collections or specialized accounting suite rather than a monetization layer.
Paid’s pricing page currently advertises a free entry plan for up to $100,000 in annual billings, a 14-day free trial with no credit card requirement and an Enterprise tier with unlimited billings and custom pricing. Plan limits and prices can change; see the live pricing page before making a purchasing decision.
How Paid compares with adjacent billing infrastructure
| Option | Likely strength | How it differs from Paid |
|---|---|---|
| Stripe Billing | Payments, subscriptions, invoices and a broad billing ecosystem | General-purpose billing; AI cost and outcome instrumentation may require additional work. |
| Orb | Flexible usage-based billing and pricing experimentation | More focused on usage-billing infrastructure than AI-specific value receipts and margin concepts. |
| Metronome | Enterprise-scale usage metering and billing | Broader usage-based billing positioning rather than Paid’s explicit agent focus. |
| Lago | Open-source and developer-oriented usage billing | Appeals to teams wanting control over the billing layer. |
| m3ter | Complex enterprise metering and rating | Specialist metering rather than a bundled AI monetization workflow. |
These are not interchangeable products, and current prices, integrations, tax handling, data retention and accounting capabilities should be checked directly with each vendor.
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What Paid’s launch means for SaaS economics
Paid is addressing a genuine infrastructure gap: vendors need to know not only what an AI system did, but what it cost, what value it produced and whether the resulting commercial model is profitable. The company’s success will therefore depend less on generating invoices than on making definitions, attribution and margins credible to both sides of the contract.
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Frequently Asked Questions
When did Manny Medina launch Paid?
Paid launched in London in March 2025, after Medina stepped down as Outreach CEO in September 2024.
How much funding has Paid raised?
Paid says its September 2025 $21 million seed round brought total funding to $33.3 million. Earlier launch coverage described the initial financing as more than $10 million, approximately $11 million or €10 million, depending on the source and currency framing.
Is outcome-based pricing always better than subscription pricing?
No. It can align payment with customer results, but it also creates measurement, attribution, dispute, refund and accounting obligations. Many products will need a carefully defined hybrid model instead.
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