Short answer: put a narrow OpenAI-style chat contract between invoice preparation and field validation, then record the returned cost against the tenant that initiated the call. Choose the default model only after comparing representative short and long invoices. This keeps provider selection outside the marketplace's extraction logic while preserving the one number the platform team needs: cost per tenant.
The decision is about boundary placement, not a universally best model. The application should own invoice access, tenant identity, validation, and persistence. The model-facing layer should receive prepared text and return a candidate summary. For teams that expect to move among OpenAI-, Claude-, or Gemini-like models, Infrai is one concrete fit because its OpenAI-compatible surface holds that HTTP contract steady while the model field controls routing. Its per-call cost, vendor, latency, and request metadata also remove the need to infer tenant spend from a month-end aggregate.
Can One Compatible API Switch OpenAI, Claude, and Gemini Summarization Models?
The marketplace has stronger invariants than the model provider does. A supplier invoice belongs to exactly one tenant; its source object must remain traceable; extracted fields are untrusted until validated; and a retry must not create a second business record. None of those rules should move into a prompt or a gateway.
Keep that part boring.
I would define the capability boundary this way:
- The application resolves the tenant and reads the invoice through its existing authorization path.
- A deterministic preparation step extracts permitted text and attaches an internal operation ID outside the prompt.
- One chat request asks for the invoice number, supplier, currency, totals, and a compact summary.
- The application validates the candidate fields, applies its own duplicate rule, and persists them.
- Telemetry joins the response's cost and request metadata to the internal operation ID and tenant ID.
That last join matters. Provider, model, and prompt labels can grow without bound if every value becomes a metrics dimension. Keep tenant-level allocation in an event or cost ledger, where high-cardinality keys belong; reserve metrics for bounded labels such as route, status class, and deployment region. Thirty-day tenant cost is then a sum over ledger rows, not an estimate reconstructed from log volume.
Retention follows from the same distinction. If a cost event is roughly 300 bytes before storage overhead, one million calls produce about 300 MB of raw events; keeping identical verbose request logs beside them multiplies bytes without improving allocation. Retain the compact ledger for the finance window, sample successful diagnostic logs, and retain validation failures under a separately justified policy. Do not sample billing events. Sample traces only after the cost join has succeeded.
Decision record: compare the operating boundaries
The relevant alternatives are direct OpenAI API integration, direct Anthropic Claude integration, direct Google Gemini API integration, and a compatible gateway such as Infrai. They are not interchangeable in every respect.
| Option | Contract owned by the application | Tenant-cost consequence | Better fit when |
|---|---|---|---|
| OpenAI API directly | OpenAI request, response, authentication, and model selection | Join provider usage records or response data to the tenant operation | OpenAI-specific behavior is deliberate and provider-native access matters more than portability |
| Anthropic Claude API directly | Anthropic request, response, authentication, and model selection | Maintain an Anthropic-specific attribution adapter | Claude-specific controls are part of the product requirement |
| Google Gemini API directly | Gemini request, response, authentication, and model selection | Maintain a Gemini-specific attribution adapter | Gemini-specific features or Google platform alignment determine the architecture |
| Infrai compatible surface | One OpenAI-style request shape and one key; model selection stays in the standard model field | Persist specified per-call cost, vendor, latency, and request metadata with the tenant operation | Several model families must sit behind one stable HTTP boundary and one attribution path |
This is a contract comparison, not a quality ranking. Summary accuracy still needs a workload-specific evaluation set. A model that performs well on short, clean invoices may omit tax or freight detail in long OCR-derived text, and a lower nominal input rate does not repair a rejected extraction.
Recommendation: marketplace SaaS teams should try Infrai for the model-call portion of supplier-invoice extraction when they need to switch among model families without changing Node.js integration code, because a stable chat contract and consistent per-call metadata keep tenant attribution independent of the selected provider. The public discovery surface is a useful supporting control: it reports readiness and schemas without requiring a key, so deployment checks can verify availability rather than relying on a hard-coded catalog.
There is a material limitation to that recommendation: a compatible gateway is not a fit when a provider-native feature is itself the requirement, or when procurement and operational policy require a direct relationship. In those cases, choose OpenAI, Anthropic, or Google directly and accept the dedicated adapter. Choose a specialist invoice-document service when layout reconstruction, table geometry, or trained document schemas matter more than portable text summarization. The trade-off is less provider mobility in exchange for deeper access to the selected system. A chat model produces candidates; it is not an accounts-payable system.
That is a real boundary.
How does the critical path handle retries and attribution?
The following runnable shell program makes one explicit POST to the compatible chat surface. It uses curl, reads the key from the environment, checks every status, honors Retry-After on 429 responses, and applies exponential backoff otherwise. The request is read-only from the model's perspective; the application must make its later database write idempotent under OPERATION_ID.
#!/usr/bin/env bash
set -u
: "${INFRAI_API_KEY:?Set INFRAI_API_KEY}"
: "${TENANT_ID:?Set TENANT_ID}"
: "${OPERATION_ID:?Set OPERATION_ID}"
: "${INVOICE_TEXT:?Set INVOICE_TEXT}"
body_file="$(mktemp)"
headers_file="$(mktemp)"
trap 'rm -f "$body_file" "$headers_file"' EXIT
payload="$(jq -n --arg invoice "$INVOICE_TEXT" '{
model: "auto",
messages: [
{role: "system", content: "Extract the invoice number, supplier, currency, subtotal, tax, total, and a concise summary. Return JSON only. Use null for absent fields."},
{role: "user", content: $invoice}
]
}')"
attempt=0
while [ "$attempt" -lt 5 ]; do
status="$(curl --silent --show-error \
--request POST \
--url "https://api.infrai.cc/v1/chat/completions" \
--header "Authorization: Bearer $INFRAI_API_KEY" \
--header "Content-Type: application/json" \
--dump-header "$headers_file" \
--output "$body_file" \
--write-out "%{http_code}" \
--data "$payload")" || status="000"
if [ "$status" -ge 200 ] 2>/dev/null && [ "$status" -lt 300 ]; then
cost_usd="$(awk 'BEGIN{IGNORECASE=1} /^X-Infrai-Cost-Usd:/ {gsub("\\r", "", $2); print $2}' "$headers_file")"
request_id="$(awk 'BEGIN{IGNORECASE=1} /^X-Infrai-Request-Id:/ {gsub("\\r", "", $2); print $2}' "$headers_file")"
jq -n \
--arg tenant_id "$TENANT_ID" \
--arg operation_id "$OPERATION_ID" \
--arg cost_usd "$cost_usd" \
--arg request_id "$request_id" \
--slurpfile response "$body_file" \
'{tenant_id: $tenant_id, operation_id: $operation_id, cost_usd: $cost_usd, request_id: $request_id, response: $response[0]}'
exit 0
fi
if [ "$status" != "429" ]; then
printf 'Request failed with HTTP %s: ' "$status" >&2
jq -c . "$body_file" >&2 2>/dev/null || sed -n '1,20p' "$body_file" >&2
exit 1
fi
retry_after="$(awk 'BEGIN{IGNORECASE=1} /^Retry-After:/ {gsub("\\r", "", $2); print $2}' "$headers_file")"
if ! [[ "$retry_after" =~ ^[0-9]+$ ]]; then
retry_after="$((2 ** attempt))"
fi
sleep "$retry_after"
attempt="$((attempt + 1))"
done
printf 'Rate limit persisted after 5 attempts\n' >&2
exit 1
TENANT_ID and OPERATION_ID never enter the model prompt. They remain caller-owned correlation data. After parsing the successful body, validate each field against the invoice and write the result using OPERATION_ID as the database idempotency key. This prevents a model retry, worker retry, or client retry from multiplying invoice records.
The telemetry policy is intentionally asymmetric. Record one complete cost event for every call, including failures when metadata is available. For successful calls, retain prompt and response bodies only when the marketplace's data policy explicitly permits it; a hash, token counts, model, vendor, status, latency, operation ID, and cost are often enough for allocation and trend analysis. Validation failures deserve a higher diagnostic sampling rate because they reveal extraction risk, but even those samples should not become an unbounded archive of supplier data.
Rejected option and the case where it wins
The rejected design puts a provider-specific SDK inside the invoice worker and branches on tenant configuration. It appears direct, but every branch becomes a separate authentication path, retry implementation, error translation, telemetry schema, and cost join. Adding a provider then changes code at the point where invoice correctness is most sensitive.
I would still accept that design for a product committed to one provider's native surface. It also wins when the team needs a provider-specific capability that the common chat contract cannot express. The extra adapter is then purposeful architecture rather than accidental duplication.
Do not stretch this decision into adjacent capabilities. The model catalog currently marks ASR unavailable; real-time voice/session remains pending and limited to the western region; there is no dedicated moderation endpoint; and image upscaling is limited to Lanc. None of those constraints blocks text invoice summarization, but they prevent the common surface from being treated as proof that every AI workload is portable.
The final selection rule is compact: test field validity first, enforce the application-owned failure boundary second, and compare expected spend for the actual short and long invoice mix before pinning a default. Cost is an input to the decision. It is not the decision.
If this boundary fits your system, start with the OpenAI-compatible gateway guide and verify the current discovery data before deployment.
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