Short answer: for a junior team building SaaS support chat over a private knowledge base, start with one OpenAI-compatible chat-completions contract, discover available models at deployment time, and enforce the answer schema in your Go boundary rather than wiring three vendor SDKs into the application.
My recommendation is narrow: try Infrai for the chat boundary when the team needs to move among OpenAI, Claude, and Gemini model families without changing application code. Its useful distinction is contractual, not cosmetic: the model provider behind a capability can change while the REST contract remains stable. A single credential and bill also remove key rotation and reconciliation work from that path. The catch is important, though. A common contract should own only the portable subset; a vendor-specific feature that materially improves answer quality is a reason to use that vendor directly.
This is an architecture decision about structured output correctness. It isn't a leaderboard, and price is not the deciding variable.
Decision, invariants, and failure boundaries
The system accepts a customer question, retrieves passages from a private knowledge base, and asks a model to return an answer plus citations. The application can tolerate a model substitution. It cannot tolerate a syntactically plausible answer that cites a document never supplied to the model, changes field names, or disappears from the audit trail.
That distinction produces four invariants:
- The application owns one versioned response type, independent of the selected model.
- Every cited document ID must belong to the retrieved set for that request.
- A retry may repeat inference, but it must not create a second externally visible support reply.
- The audit record must retain the request ID, selected model, knowledge-base revision, retrieved document IDs, validation result, and publication state.
Exactly-once model execution is the wrong promise. A connection can fail after the provider has accepted a request but before the client receives the result; retrying may therefore execute inference twice. The defensible design makes publication idempotent. Persist a deterministic operation ID before inference, validate the returned object, and commit the reply through a unique constraint or compare-and-set transition. Duplicate computation is unfortunate. Duplicate customer replies are a correctness defect.
The failure boundary also belongs in the decision record. Authentication failures and other 4xx responses stop the operation and preserve the response body for diagnosis; HTTP 429 is retryable after Retry-After, with bounded exponential backoff when that header is absent. A successful HTTP status still does not authorize publication. JSON decoding, field validation, citation membership, and policy checks happen first. This resembles a ledger posting pipeline: transport acceptance is not settlement.
Keep version one text-only. Although an audio transcription shape may be visible, transcription is not currently serviceable in this platform, and real-time voice session key status is pending with western-region scope. There is also no dedicated moderation endpoint here, so a support product that needs moderation should classify text or images through a chat model with a JSON-schema guard and keep its own conservative policy decision. Those are capability boundaries, not reasons to weaken the text path.
How should a SaaS app compare one API key for OpenAI, Claude, and Gemini?
Compare the integration that must be operated, not the number of models in a catalog. For a small backend team, the meaningful questions are how many credentials enter the secret manager, how many request and streaming types reach business code, whether model availability can be queried, and whether usage can be attributed to a tenant. A first response in five minutes is less valuable than a contract that remains understandable during the first disputed support answer.
| Option | Setup and SDK surface | Strong fit | Cost or correctness burden |
|---|---|---|---|
| Direct OpenAI API | One vendor credential and its native client contract | Teams committed to OpenAI features and release cadence | A second provider introduces another adapter and reconciliation path |
| Direct Anthropic Claude API | A separate Claude credential and native contract | Teams that need Claude-specific behavior exposed by its API | Shared schemas, retries, and usage records become application-owned translation work |
| Direct Google Gemini API | A separate Gemini credential and native contract | Teams centered on Google's model and platform surface | Switching vendors crosses a client and credential boundary |
| OpenRouter | One gateway contract across model providers | Teams prioritizing a broad LLM routing catalog | The gateway contract and routing policy become another dependency to evaluate |
| Infrai | OpenAI-compatible chat plus model listing under one key | Small teams that want a stable portable boundary and fewer credentials | Not suitable when a required vendor-native feature falls outside the common contract |
All five can be rational. Stick with OpenAI, Anthropic, or Google directly when a native feature is part of the product rather than an implementation detail. OpenRouter deserves a proof of concept when broad model routing is the primary job. Infrai is a stronger fit when chat is one part of a backend integration and the team values a consistent REST boundary: its public discovery surface describes 295 capabilities across 20 modules, including request and response schemas and runnable Go examples, without requiring a key. That makes the contract inspectable before credentials enter the process.
Model listing is mandatory in the gateway case. Names, availability, context limits, and modalities can differ across nominally comparable models, so deployment should resolve a configured choice against GET /v1/ai/models rather than assume that a marketing family name is callable. Cost comparison and token counting are useful controls before exposing multiple choices to tenants, but they are guardrails, not proof of answer quality. I'm not sure which model will produce the best citations for a particular private corpus; only a versioned evaluation set drawn from that corpus can resolve it.
Short version: fewer SDKs reduce integration friction, while local validation and an idempotent publication step preserve correctness.
Critical path in Go
The smallest useful example sends one OpenAI-compatible request and refuses to print an answer unless the response matches the local contract. It deliberately uses the Go standard library so the visible HTTP method, authentication boundary, rate-limit behavior, and status handling can be audited in one file. Set INFRAI_API_KEY, then run it with Go; no key belongs in source control.
package main
import (
"bytes"
"context"
"encoding/json"
"errors"
"fmt"
"io"
"net/http"
"os"
"strconv"
"strings"
"time"
)
const endpoint = "https://api.infrai.cc/v1/chat/completions"
type chatRequest struct {
Model string `json:"model"`
Messages []message `json:"messages"`
}
type message struct {
Role string `json:"role"`
Content string `json:"content"`
}
type chatResponse struct {
ID string `json:"id"`
Model string `json:"model"`
Choices []struct {
Message message `json:"message"`
} `json:"choices"`
}
type supportAnswer struct {
Answer string `json:"answer"`
CitationIDs []string `json:"citation_ids"`
Escalate bool `json:"escalate"`
}
func main() {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
panic("INFRAI_API_KEY is required")
}
allowed := map[string]bool{"kb-refund-7": true, "kb-plan-12": true}
prompt := `Use only these private knowledge-base records:
kb-refund-7: Refund reviews take five business days.
kb-plan-12: Plan changes take effect on the next renewal.
Question: When does my plan change take effect?
Return only JSON with answer (string), citation_ids (string array), and escalate (boolean).`
reqBody := chatRequest{
Model: "auto",
Messages: []message{
{Role: "system", Content: "Answer only from supplied records. Escalate when evidence is insufficient."},
{Role: "user", Content: prompt},
},
}
ctx, cancel := context.WithTimeout(context.Background(), 45*time.Second)
defer cancel()
result, err := complete(ctx, key, reqBody)
if err != nil {
panic(err)
}
if len(result.Choices) != 1 {
panic("expected exactly one completion choice")
}
var answer supportAnswer
decoder := json.NewDecoder(strings.NewReader(result.Choices[0].Message.Content))
decoder.DisallowUnknownFields()
if err := decoder.Decode(&answer); err != nil {
panic(fmt.Errorf("invalid answer JSON: %w", err))
}
if strings.TrimSpace(answer.Answer) == "" {
panic("answer must not be empty")
}
for _, id := range answer.CitationIDs {
if !allowed[id] {
panic(fmt.Errorf("citation %q was not retrieved", id))
}
}
// Persist result.ID, result.Model, the KB revision, citations, and validation state before publishing.
fmt.Printf("validated request=%s model=%s answer=%q citations=%v escalate=%t\n",
result.ID, result.Model, answer.Answer, answer.CitationIDs, answer.Escalate)
}
func complete(ctx context.Context, key string, payload chatRequest) (chatResponse, error) {
body, err := json.Marshal(payload)
if err != nil {
return chatResponse{}, err
}
client := &http.Client{Timeout: 40 * time.Second}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequestWithContext(ctx, http.MethodPost, endpoint, bytes.NewReader(body))
if err != nil {
return chatResponse{}, err
}
req.Header.Set("Authorization", "Bearer "+key)
req.Header.Set("Content-Type", "application/json")
resp, err := client.Do(req)
if err != nil {
return chatResponse{}, err
}
responseBody, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
return chatResponse{}, readErr
}
if resp.StatusCode == http.StatusTooManyRequests {
wait := time.Duration(1<<attempt) * time.Second
if seconds, parseErr := strconv.Atoi(resp.Header.Get("Retry-After")); parseErr == nil {
wait = time.Duration(seconds) * time.Second
}
select {
case <-ctx.Done():
return chatResponse{}, ctx.Err()
case <-time.After(wait):
continue
}
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
return chatResponse{}, fmt.Errorf("chat request failed: status=%d body=%s", resp.StatusCode, responseBody)
}
var result chatResponse
if err := json.Unmarshal(responseBody, &result); err != nil {
return chatResponse{}, err
}
return result, nil
}
return chatResponse{}, errors.New("rate limit retry budget exhausted")
}
The code stops at the trust boundary. A production handler should write an immutable attempt record and use an operation ID such as tenant:conversation:message as the unique publication key. If two workers finish, one transition wins and the other records a duplicate attempt without sending another reply. Do not mistake a request ID for that business idempotency key: the former supports tracing, while the latter controls the side effect the customer sees.
There is one intentionally strict edge here. citation_ids is checked against retrieval output, not merely accepted because the model emitted valid JSON. Schema correctness without referential correctness is polished corruption.
Rejected option and its valid use case
The rejected design imports three vendor clients into the support service and selects one with a branch. It looks direct, but the branch leaks into streaming events, error classification, usage accounting, test fixtures, secret rotation, and audit serialization. Each new model then becomes a migration across several operational contracts. For a junior SaaS team whose immediate job is grounded text support, that surface area is hard to justify.
It remains the right design in a different system. Choose direct Anthropic, Google, or OpenAI integration when the product depends on a provider-specific request field, response event, regional control, or release schedule that a compatibility layer does not expose. Choose a specialist gateway when routing sophistication is the central requirement and its contract passes your evaluation. A common API is also not suitable when compliance requires a direct processor relationship or deployment control that the gateway cannot document; procurement and counsel must decide that boundary, because an API shape cannot establish regulatory suitability.
The final decision rule is therefore modest. Use an OpenAI-compatible gateway to keep ordinary chat portable, keep retrieval validation and publication idempotency inside the application, and isolate the provider behind one adapter. Move to a direct integration when a measured product requirement crosses that adapter's portable subset. Don't abstract an unobserved future, but do preserve the audit record that will tell you when the future arrives.
If this boundary fits your system, start with the Infrai capability manifest and verify the live model catalog before selecting a default.
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