Conto

Micropayments

x402/MPP and service-spend budgets for machine payments

When agents are paying for APIs, inference, or compute by the call, Conto applies per-request caps and session budgets so service spend stays bounded even when the agent is making many decisions per minute.

Industries

Compute & AI infra · Agentic commerce

Best for

Inference, APIs, service spend

Controls

Per-call caps, session budgets, velocity

Outcome

Machine-speed buying with hard limits

How per-call spend stays inside budget

Budget at request time

$4.12/ $5.00 session

82% spent · $0.88 left

Per-call cap$0.50
Velocity18 / 60 per min

Per-call decisions

inference.helia.dev$0.012
search.serp.api$0.040
gpu-rent.xyz$1.20
embeddings.io$0.008
render.farm$0.95
over per-call cap session budget spent

Every paid request is checked before the provider is paid. Low-cost calls keep flowing, while an oversized call or an exhausted session both stop without a human in the loop.

Service-spend activity with budget context attached

Machine-speed payments show up with the same context as enterprise approvals and budgets, which makes service-spend automation easier to govern alongside the rest of the payment estate.

Evaluationpol_4f9c · 142ms

Payment request

procurement-agent → Quill Data$8,500.00
quilldata.io · datanew counterparty

Rules evaluated

Per-transaction limit
$8,500 ≤ $10,000pass
Monthly budget
$24.1k / $40kpass
Counterparty trust ≥ 70
new · no score yetreview
Category allowlist
datapass

Routed to review

1 of 4 rules needs a human before settlement.

policy: procurement-spend-v4

Controls that bound automated service spend

Per-call, session, provider, and velocity policies stop runaway spend without forcing every low-cost request into manual review.

Per-call ceilings

Prevent a single API or inference request from blowing past the allowed unit economics for the task.

Session envelopes

Give each task or agent run a hard budget so many small calls cannot add up to an unlimited bill.

Provider-level policy

Restrict autonomous spend to the providers or service types you actually trust for the workflow.

Velocity monitoring

Catch broken loops or runaway retry behavior before the agent can hammer a paid endpoint continuously.

How per-call payments stay inside budget

Agents can buy APIs, inference, and compute on demand while Conto checks unit economics and task budgets before each paid request.

Step 1

The agent chooses a paid service

A compute or API workflow selects the service endpoint and proposes the call based on price, latency, or task fit.

Step 2

Conto checks call-level economics

Conto checks each request against your per-call limits, session budgets, and provider rules before the service is paid.

Step 3

Normal usage continues until budget is spent

Low-cost calls keep flowing. Expensive routes or exhausted budgets are blocked before the provider is paid.

Demo

Compute & AI infra in action

Compute & AI infra applies this solution to a realistic agent payment workflow, with approved payments, review paths, and blocked requests visible from request to settlement.

Pay-per-call inference with hard session budgets.
An AI shopper that can only spend where you let it.

Machine payments that stay in budget

Engineering teams can let agents buy services dynamically without accepting unbounded cost exposure.

The same control plane works for per-call infrastructure spend and more traditional payouts.

Every paid request comes with a budget story that finance can understand.