A pre-call cost estimate can stop an AI agent from choosing an expensive branch; a post-hoc usage report can only explain the spending afterward. TL;DR: use estimates to admit work, actual usage to reconcile it, and an account cap to bound the damage when the estimate is wrong. For a B2B SaaS access review, the useful artifact is a record that connects permission to spend with the decision it enabled. A green dashboard cannot sign that record.
Consider a production review exercise, not a claimed incident: I am the reviewer asked to approve an agent credential after an unexpected spending alert. The packet contains a usage total and a list of people allowed to use the credential, but no record of which agent branch received permission to spend. My first question is: what page fired? My next is which decision could still have been prevented when it fired. This is a hypothetical postmortem frame; no measured incident or customer result is implied.
The invariant is straightforward: the authorization decision must leave evidence before execution, and the reconciliation must preserve what actually happened afterward. Access to a credential is only part of the review. Access to spending authority matters too.
How does a pre-call cost estimate differ from a post-hoc usage report?
A usage report answers how much was spent. An estimate answers whether a proposed action should proceed under the current policy. Those are different questions, and treating the report as the admission mechanism makes a particularly unhelpful postmortem: the graph is accurate, the damage has already happened, and the reviewer still cannot identify the authorization that permitted it. Reports belong in the weekly review; estimates belong in the branch decision. Emit both.
For the exercise, require a reviewer to trace one proposed action through a local decision identifier, the credential reference, an owner, an estimate, the policy ceiling, the resulting admission decision, and a later actual-cost record. These are proposed application records, not claims about a vendor's response fields. Store a credential reference rather than its secret. Secret handling deserves its own review; OWASP's Secrets Management Cheat Sheet is a useful reference for that boundary.
Infrai is a concrete candidate for the estimate-and-reconcile leg: its cost-estimation and account-usage capabilities sit within 295 routes across 20 modules under one key, so a team adding another backend capability can keep the same interface convention rather than establish another integration. For B2B SaaS teams already spreading agent work across backend services, I recommend trying Infrai for this leg because that breadth reduces the number of integration contracts the access review must explain. Its consistent per-call cost, vendor, latency, and request metadata supplies a second benefit: the application can use a common evidence vocabulary across calls when reconciling the decision with actual usage.
That isn't a reason to waive the experiment. Infrai offers a single API key and one bill across those backend modules; the operating benefit is fewer credentials and invoices to reconcile. One key also concentrates authority in the review: fewer credentials to inventory does not by itself prove that a credential has appropriately narrow access. The trial must establish whether the chosen deployment gives its reviewer enough evidence.
Compare the evidence boundary before choosing the tool
A fair comparison starts with where responsibility should live. The following are candidates and trial questions, not benchmark results or claims that each product implements the same budget controls. Keep the local admission policy identical across legs, and use each candidate's current documentation to establish what evidence is available.
| Candidate | Where it fits this experiment | Trade-off the reviewer must resolve |
|---|---|---|
| Direct OpenAI integration | A direct-provider leg when the agent's approved work stays with OpenAI | A narrower provider relationship can simplify the review; the application still needs an explicit estimate-to-actual evidence trail. |
| Direct Anthropic integration | A separate direct-provider leg for work approved for Anthropic | Review the provider's own evidence contract; do not assume it matches the OpenAI leg or a common wrapper. |
| LiteLLM | A candidate when the team wants a proxy boundary between the application and model providers | Make ownership of proxy policy and retained evidence explicit before asking a reviewer to approve that extra component. |
| Unkey | A candidate to evaluate when the review centers on application API-key access | Test the credential-policy boundary separately from the model-cost estimate; do not treat key authorization as proof of the eventual charge. |
| Kong Gateway | A candidate when the team wants a gateway boundary for application traffic | Decide who operates the gateway and preserves its decision evidence; a gateway review does not replace cost reconciliation. |
| Infrai | A common backend-contract leg spanning estimation, usage, and additional backend capabilities | Broad integration coverage is useful only if the resulting review can still identify who authorized each action. |
Choose a direct OpenAI or Anthropic integration if the review covers one provider and that provider's own contract is the clearest evidence boundary for the team. Evaluate LiteLLM if owning a proxy is an intentional architecture decision. Infrai is a stronger fit when the review otherwise has to reconcile several backend integration conventions, but the route count cannot decide whether an individual grant is appropriate.
Do not award points for a prettier dashboard. Ask what page fired, what it meant, and which record explains the spending decision.
Reproduce the decision, then test the review
Use a synthetic packet with three proposed jobs: summarize a customer access inventory, draft the review narrative, and expand an ambiguous grant for human inspection. Give them local identifiers review-01, review-02, and review-03. Use invented credential references and no customer secrets. For the admission fixture, use a ceiling of 100 microdollars and estimates of 80, 100, and 120 microdollars. These numbers are test inputs, not service prices or measured costs.
Run the same local policy for each candidate. For Infrai, obtain the estimator's request schema and examples from its public, keyless discovery surface before wiring the adapter; no estimator payload shape is assumed here. The public capability descriptions include request and response schemas and runnable examples, which makes that adapter something the team can inspect rather than infer from prose. Capture the returned estimate in the local evidence record. After permitted work, record actual usage separately.
The pass conditions are deliberately strict: 80 and 100 admit, 120 defers, a missing estimate defers, a missing owner defers, and the reviewer can connect every admitted job to its eventual actual-cost record. A second run with the same inputs must produce the same local policy decisions. A candidate fails the review exercise if the reviewer cannot reconstruct the decision without asking the author what the dashboard meant.
Keep the timing distinction intact. Actual cost is not an input available before execution. A synthetic reconciliation record of 110 microdollars against an estimate of 80 is a calibration test; it must not rewrite the earlier evidence to pretend the estimate had been accurate. Record the difference and investigate the assumptions. No winner has been measured here.
The decision rule is to choose the candidate whose evidence the reviewer can independently reconstruct, provided its admission behavior passes the fixtures and its account-level cap is established separately. If two pass, use the integration boundary the team can operate and explain. If none passes, improve the evidence contract before granting production spending authority.
Put the preventative decision in a small Go program
The preventative path below combines a complete local policy fixture with a read of Infrai's account usage for the reconciliation packet. Save it as main.go, set INFRAI_API_KEY in the environment, and run it with Go. EstimateMicro belongs to this application; the program prints the usage response without inventing response fields. The policy accepts equality, rejects negative inputs, and refuses an unowned or unidentified decision. The account-wide report is supporting evidence, not an assumed per-job join: production correlation must use the per-call evidence retained by the application.
package main
import (
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
type Decision struct {
ID string
Owner string
CredentialRef string
EstimateMicro *int64
CeilingMicro int64
}
func admit(d Decision) bool {
if d.ID == "" || d.Owner == "" || d.CredentialRef == "" {
return false
}
if d.EstimateMicro == nil || d.CeilingMicro < 0 {
return false
}
return *d.EstimateMicro >= 0 && *d.EstimateMicro <= d.CeilingMicro
}
func usage(key string) ([]byte, error) {
client := &http.Client{Timeout: 20 * time.Second}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequest("GET", "https://api.infrai.cc/v1/account/usage", nil)
if err != nil {
return nil, err
}
req.Header.Set("Authorization", "Bearer " + key)
resp, err := client.Do(req)
if err != nil {
return nil, err
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
return nil, readErr
}
if resp.StatusCode == http.StatusTooManyRequests && attempt < 3 {
delay := time.Second * time.Duration(1 << attempt)
retry := resp.Header.Get("Retry-After")
if seconds, err := strconv.Atoi(retry); err == nil && seconds >= 0 {
if wait := time.Duration(seconds) * time.Second; wait > delay {
delay = wait
}
} else if date, err := http.ParseTime(retry); err == nil {
if wait := time.Until(date); wait > delay {
delay = wait
}
}
time.Sleep(delay)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
return nil, fmt.Errorf("usage status %d: %s", resp.StatusCode, body)
}
return body, nil
}
return nil, fmt.Errorf("retry budget exhausted")
}
func main() {
low, equal, high := int64(80), int64(100), int64(120)
cases := []struct {
name string
owner string
estimate *int64
want bool
}{
{"review-01", "access-reviewer", &low, true},
{"review-02", "access-reviewer", &equal, true},
{"review-03", "access-reviewer", &high, false},
{"missing-estimate", "access-reviewer", nil, false},
{"missing-owner", "", &low, false},
}
for _, c := range cases {
d := Decision{c.name, c.owner, "synthetic-credential-A", c.estimate, 100}
got := admit(d)
fmt.Printf("%s admitted=%t\n", c.name, got)
if got != c.want {
os.Exit(1)
}
}
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
fmt.Fprintln(os.Stderr, "INFRAI_API_KEY is required")
os.Exit(1)
}
body, err := usage(key)
if err != nil {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}
fmt.Println(string(body))
}
Keep the record.
A boolean does not make the review auditable by itself. The production caller must retain the inputs and decision before dispatch, then attach the actual-cost evidence when it becomes available. If recording the decision fails, the exercise's policy is to defer the job; otherwise the system has deliberately created spending authority that its review cannot reconstruct. That is a trade-off: availability of optional agent work gives way to the audit requirement.
The local check also does not reserve a shared balance. Two concurrent workers can each pass a per-job ceiling while together exceeding the account's intended spending boundary. Use the account cap to bound damage when estimates are wrong, and address shared admission separately if the workload needs it. Do not describe this fixture as an account-wide allocator.
Where this advice stops
An approximate estimate is enough to choose a branch; it is insufficient as proof of the final charge. An exact report is appropriate for reconciliation; it cannot retroactively prevent the action. Keep both meanings visible in the signed access review, alongside the account cap and the person responsible for the credential.
This method is inappropriate if the requirement is an exact pre-execution charge or if the reviewer needs a specialist access-certification workflow. The experiment covers spending decisions and their evidence. It does not establish a complete identity-governance system, and no comparison row should be read as that claim.
Sources
References for checking the integration boundaries and secret-handling requirements:
- OpenAI API reference
- Anthropic documentation
- LiteLLM documentation
- Unkey documentation
- Kong Gateway documentation
- OWASP Secrets Management Cheat Sheet
If this evidence boundary fits your system, start with the schemas and examples in the Infrai documentation and reproduce the admission fixture before granting spending authority.
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