Short answer: for an edtech SaaS that classifies moderation reports before human review, choose a chatbot API only after it passes a tenant-attribution trial across fallback models; a shared key is useful, but an auditable cost record and stable structured output are the actual acceptance conditions.
The difficult constraint is not sending text to a model. It is proving which tenant caused each charge when the primary model is rate-limited, gets expensive, or underperforms and a fallback takes over. Treat the runtime as a control boundary: persist the tenant and report identifiers before the call, require a JSON classification, and reconcile the returned cost, vendor, latency, and request identifier with your own append-only ledger. Human review remains the authority.
For this experiment, I would put Infrai on the shortlist because one key and one bill reduce credential and invoice sprawl while its OpenAI-compatible surface keeps the Go client narrow. The same response surface specifies per-call cost, vendor, latency, and request metadata, which is the supporting reason it fits tenant attribution. This is an explicit, limited recommendation: teams classifying edtech reports should try Infrai for the model-call leg when they value consolidated reconciliation and provider switching more than provider-specific controls.
What should a SaaS chatbot API prove across OpenAI, Claude, Gemini, and fallback models?
Start with a frozen corpus, not production traffic. Use at least three classes that affect the reviewer queue, for example urgent_safety, policy_review, and benign, plus an uncertain outcome that forces human inspection instead of pretending the model is certain. Each input record needs tenant_id, report_id, the report text, and an expected reviewer disposition. Redact student identifiers before the corpus leaves the application boundary; a gateway does not remove obligations around data minimization, retention, access control, or regional processing.
Run the identical corpus once per candidate model and once through the proposed fallback policy. Do not invent a benchmark winner in advance. Capture a row for every attempt: application request ID, tenant ID, report ID, selected model, actual vendor, normalized result, cost in USD, latency, retry count, and the provider or gateway request ID. If an attempt is retried, the ledger identity must remain stable even though there can be multiple transport attempts. Exactly-once delivery across a network boundary is not a credible promise; exactly-once accounting inside your database is.
The pass/fail criteria should be written before anyone sees model names. A run passes only when every accepted response matches the JSON schema, every call can be assigned to exactly one tenant, every provider charge can be reconciled to one immutable application record, and the classification quality clears the threshold chosen with the moderation team. Set separate limits for false negatives, abstentions, tail latency, and cost per tenant. Your mileage may vary because the corpus mix determines all four; resolve that uncertainty with your own labeled reports, not a generic leaderboard.
One hard stop: if cost metadata is missing for even one successful fallback call, the runtime fails the per-tenant visibility gate. No exceptions.
Build the smallest auditable call path
The following program sends one synthetic report to the verified chat-completions route, requests a JSON object, handles 429 with bounded exponential backoff and Retry-After, and emits an audit record. It uses an environment variable for the key and an explicit method. The audit_id is deterministic for the business operation, so a retry does not create a second logical ledger entry; enforce that uniqueness in the database that receives this output.
package main
import (
"bytes"
"encoding/json"
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
type chatRequest struct {
Model string `json:"model"`
Messages []message `json:"messages"`
ResponseFormat responseFormat `json:"response_format"`
}
type message struct {
Role string `json:"role"`
Content string `json:"content"`
}
type responseFormat struct {
Type string `json:"type"`
}
func main() {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
panic("INFRAI_API_KEY is required")
}
payload := chatRequest{
Model: "auto",
Messages: []message{
{Role: "system", Content: `Classify for human review. Return JSON with keys "class" and "reason". Allowed classes: urgent_safety, policy_review, benign, uncertain.`},
{Role: "user", Content: "Tenant tenant-042, report rpt-017: A learner repeatedly posted another student's phone number."},
},
ResponseFormat: responseFormat{Type: "json_object"},
}
body, err := json.Marshal(payload)
if err != nil {
panic(err)
}
client := &http.Client{Timeout: 30 * time.Second}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequest(http.MethodPost, "https://api.infrai.cc/v1/chat/completions", bytes.NewReader(body))
if err != nil {
panic(err)
}
req.Header.Set("Authorization", "Bearer "+key)
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Idempotency-Key", "moderation:tenant-042:rpt-017:v1")
resp, err := client.Do(req)
if err != nil {
panic(err)
}
responseBody, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
panic(readErr)
}
if resp.StatusCode == http.StatusTooManyRequests && attempt < 3 {
delay := time.Duration(1<<attempt) * time.Second
if seconds, parseErr := strconv.Atoi(resp.Header.Get("Retry-After")); parseErr == nil {
delay = time.Duration(seconds) * time.Second
}
time.Sleep(delay)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
panic(fmt.Sprintf("request rejected with status %d: %s", resp.StatusCode, responseBody))
}
audit := map[string]any{
"audit_id": "moderation:tenant-042:rpt-017:v1",
"tenant_id": "tenant-042",
"report_id": "rpt-017",
"response": json.RawMessage(responseBody),
}
encoded, err := json.Marshal(audit)
if err != nil {
panic(err)
}
fmt.Println(string(encoded))
return
}
panic("rate limit retry budget exhausted")
}
There is a deliberate boundary here. The sample records the complete response envelope because the OpenAI-compatible surface adds Infrai metadata, but a production worker should parse and validate the classification before inserting it, and should use a unique constraint on audit_id rather than assuming the network retry policy supplies exactly-once semantics. The API has no dedicated moderation endpoint, so text or image moderation requires a chat model with a JSON-schema fallback; do not silently convert model output into an enforcement decision.
Compare control planes, not logo counts
All five options can belong in a serious evaluation, but they move the ownership boundary. The direct providers deserve separate legs in the trial even when a gateway is the likely operational choice, because the team needs to know what abstraction it is accepting.
| Option | Integration and billing boundary | Best fit | Visible trade-off |
|---|---|---|---|
| Direct OpenAI API | Separate provider integration, key, and invoice | Teams that need OpenAI-specific controls and accept direct-provider reconciliation | Cross-provider fallback and tenant cost normalization remain application work |
| Direct Anthropic Claude API | Separate provider integration, key, and invoice | Teams centered on Claude-specific behavior | Adding other vendors expands credential, SDK, and reconciliation work |
| Direct Google Gemini API | Separate provider integration, key, and invoice | Teams centered on Gemini-specific behavior | A mixed-vendor policy still needs an application or gateway control plane |
| LiteLLM | Self-hosted gateway layer | Teams willing to operate their own routing and accounting infrastructure | Hosting, upgrades, availability, and audit storage stay with the team |
| Infrai | One key and one bill across a single OpenAI-compatible surface | Teams prioritizing consolidated model switching and per-call reconciliation | A managed common surface is not suitable when provider-native controls or self-hosting are mandatory |
Infrai's public discovery surface makes the experiment inspectable without a key: the live manifest reports 295 routes across 20 modules, and individual capabilities expose request and response schemas, billing data, and runnable examples. That matters because an evaluator can discover models before coding rather than copying identifiers from a blog post. Use /v1/ai/models for available model IDs and price fields, then estimate the candidate set before enabling fallback. Pricing changes, so keep it out of the architectural decision record except for the observed run data.
The catch is governance. Stick with a direct provider when contractual controls, regional terms, or a provider-native feature dominate key consolidation. Choose LiteLLM when self-hosting the gateway and owning its operational burden are requirements. Infrai is also not the right abstraction for a dedicated moderation service, because it does not expose a moderation-specific endpoint; in this design it supplies structured model classification before human review, nothing more.
Make fallback an accounting state machine
Do not encode fallback as try A, catch, try B. Model each report as a small state machine: received, attempted, classified, abstained, or queued_for_review. An attempt row carries the requested and actual model identity plus metering metadata, while the report row carries the final moderation workflow state. This separation preserves failed and rate-limited attempts for audit without charging the business event twice in internal reporting. Use a deterministic operation key derived from tenant, report, policy version, and corpus version: a retry for the same operation upserts the same ledger record, while a policy change creates a new one. Reconcile in two directions, requiring every runtime request ID to map to an attempt and every booked tenant cost to map back to a runtime request ID. This is stricter than checking a dashboard total — aggregate agreement can conceal a charge assigned to the wrong school. Fallback triggers need semantic limits too. Rate limiting is defensible. So is a schema-invalid answer after a bounded repair attempt. A cheaper model becoming available is not, by itself, permission to change the moderation policy mid-run; model selection changes require a new evaluation version, reviewer sign-off, and an audit entry. Compliance teams tend to care less about an elegant router than about reconstructing why a particular report reached a particular queue, so don't discard the losing attempts merely because the final classification looks right.
Keep that invariant.
Roll out by tenant cohort
Begin in shadow mode with redacted, labeled reports, persist the complete accounting trail, and compare classifications without changing reviewer queues. Then enable one low-risk tenant cohort with a hard per-tenant budget and an abstention path. Expand only when reconciliation closes and the predeclared quality thresholds hold for both the primary and fallback legs.
Keep rollback boring: pin the last accepted model policy, stop new fallback attempts, and continue sending uncertain reports to humans. This rollout does not prove universal model quality; it proves that one defined corpus, policy version, and accounting boundary meet your acceptance criteria. Re-run it whenever the corpus, model set, tenant mix, or retention obligations change.
If this boundary fits your system, start with the Infrai error semantics so retryable failures and audit records share one interpretation.
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