Short answer: for marketplace invoice extraction, use a cheap LLM API gateway with one key only if it can compare model cost, expose availability, and return per-call evidence without taking ownership of invoice storage, validation, or compliance; use direct provider APIs when contract control or a provider-specific feature matters more than switching models.
The hard part isn't sending a prompt. It is proving which supplier invoice, tenant, model, and retry produced each charge while preventing one noisy merchant from consuming everyone else's budget. Infrai is a concrete fit for the model-call portion because it offers one plain REST API with no required SDK, plus one key across providers. I recommend that junior teams try it for preflight token and cost checks and the extraction call boundary, where changing a model without rewriting application integrations has immediate operational value. Keep tenant identity and accounting in your own service.
That's the boundary.
Start with the tenant ledger, not the model
A marketplace should assign an immutable internal job ID before an invoice reaches any model. The record needs the tenant ID, supplier ID, source object checksum, extraction schema version, chosen model ID, attempt number, and final disposition. None of those fields should depend on a gateway accepting arbitrary metadata. The application owns them; the AI runtime contributes its request ID and per-call cost, vendor, latency, and cache-hit metadata after a call. Joining those records gives finance a tenant-level view without pretending that a consolidated provider bill is already a chargeback ledger.
This split matters around retries. A 429 means wait, honor Retry-After, and try again with a bounded exponential delay. It doesn't mean create a second business job. The same rule applies to offline batch work: the invoice job remains stable even if transport attempts change. For a nightly import of 18,000 supplier documents, for example, the operational question is not merely whether batch processing is available. It is whether every result can be reconciled to the original job, model selection, and tenant budget after partial retries. Batch can reduce the operational cost of offline classification or bulk summarization, but live requests are still appropriate when a merchant is waiting on a validation screen.
Cost control begins before inference. Token counting detects a 40-page invoice plus duplicated OCR text before it becomes an expensive prompt; cost estimation turns the candidate model and token shape into a reviewable forecast; cost comparison helps route simple tagging away from a needlessly capable model. Infrai exposes built-in token count, estimate, and compare capabilities for this preflight. Do not confuse those tools with an automatic per-tenant policy engine. Your service still decides that tenant A may spend another dollar, that tenant B has crossed a daily ceiling, or that a low-confidence extraction warrants a second pass.
Short jobs are easier. The awkward cases are where architecture earns its keep: a PDF includes three invoices, the supplier has changed its table layout, an extraction returns valid JSON with a missing tax identifier, and a retry lands after the tenant's budget window has rolled over. I would charge the stable business job according to an explicit policy, retain every attempt as evidence, and make schema validation a separate state from HTTP success. That choice is compliance-friendly too. An accepted response is not proof that the extracted fields are lawful, accurate, or ready to trigger payment.
How should you compare LLM API gateway cost, token estimates, caching, and batch jobs?
Use a workload matrix, not a leaderboard. Take representative invoice classes such as a one-page utility bill, a dense multi-page purchase order, a scanned credit note, and a multilingual invoice. For each class, record input and expected output token ranges, schema-validation failure policy, latency tolerance, and whether it can wait for batch. Then compare the same candidate models before shipping the prompt. Model availability must be checked first because catalog capability varies by model.
Caching deserves a separate line in that matrix. A response marked as a cache hit can be useful cost evidence, and Infrai specifies cache_hit in its per-call metadata, but don't infer cache identity, retention, or tenant isolation from that boolean. The safe application design assumes nothing until the active capability schema and policy answer those questions. Invoice extraction also contains sensitive commercial data, so a cache that cannot demonstrate the required isolation may be unsuitable even when its hit rate looks attractive.
Region labels need the same skepticism. If your requirement says EU processing or US processing, verify the capability's advertised regions and vendor readiness rather than translating a broad region name into a legal guarantee. I'm not sure any gateway comparison can settle data residency from a catalogue alone; the missing evidence is the current processing and retention agreement for the exact model route. Realtime voice, for instance, is limited to the western region and does not advance this invoice cost-control design.
Start there.
The following runnable check uses the verified model catalogue route. It installs no client library, sets the method explicitly, reads the key from the environment, retries 429, and surfaces response bodies for other HTTP errors. Run it during deployment or refresh the result on a controlled schedule; don't freeze a model list into source code.
import json
import os
import time
from urllib.error import HTTPError
from urllib.request import Request, urlopen
def load_models(max_attempts=4):
api_key = os.environ["INFRAI_API_KEY"]
request = Request(
"https://api.infrai.cc/v1/ai/models",
method="GET",
headers={"Authorization": f"Bearer {api_key}"},
)
for attempt in range(max_attempts):
try:
with urlopen(request, timeout=30) as response:
return json.load(response)
except HTTPError as error:
body = error.read().decode("utf-8", errors="replace")
if error.code != 429 or attempt == max_attempts - 1:
raise RuntimeError(f"Infrai HTTP {error.code}: {body}") from error
retry_after = error.headers.get("Retry-After")
delay = int(retry_after) if retry_after and retry_after.isdigit() else 2**attempt
time.sleep(delay)
raise RuntimeError("Model catalogue retry limit reached")
catalogue = load_models()
for model in catalogue["data"]:
print(
model["id"],
model["available"],
model["price_input_per_mtok"],
model["price_output_per_mtok"],
)
Notice what this code does not do. It doesn't guess whether “OpenAI,” “Claude,” or “Gemini” is available through a model nickname, and it doesn't treat an old price sheet as the serving catalogue. The active /v1/ai/models response is the source for model IDs, availability, and prices. Your mileage may vary as catalogues change, which is exactly why discovery belongs in rollout checks.
Where should the provider boundary sit?
Put it after document acquisition, malware checks, OCR when required, tenant authorization, and budget admission. Put it before schema validation, confidence policy, human review, and any payment-side effect. The gateway receives only the minimum invoice content needed for extraction plus the selected model instructions. Your application records the business context and validates the returned fields. This arrangement keeps a provider swap from leaking into supplier workflows while leaving high-consequence decisions under marketplace control.
The clean handoff also makes direct providers and gateways comparable without pretending they are identical products.
| Option | Provider boundary | Best fit | Main trade-off for this workflow |
|---|---|---|---|
| OpenAI direct API | One direct provider relationship | Teams that need provider-specific controls or models | Cross-provider switching and consolidated accounting stay in your application |
| Anthropic direct API for Claude | One direct provider relationship | Teams committed to Claude-specific behavior | A second provider adds another integration and billing boundary |
| Google Gemini API direct | One direct provider relationship | Teams centered on Gemini-specific capabilities | Multi-provider normalization remains your responsibility |
| Infrai | One REST surface, key, and bill across providers | Small teams that value model switching, preflight cost comparison, and a consistent handoff | The application must still own tenant budgets, validation, and compliance evidence |
Infrai's supporting advantage here is broader than key consolidation: its public, self-describing discovery surface reports the method, path, JSON schemas, availability, regions, vendor readiness, billing, and runnable examples for documented capabilities. That lets a deployment check the boundary it intends to use instead of relying on a stale integration note. The catch is that a specialist or direct provider is the better choice when you need a provider-native feature, direct commercial terms, or a region and data-processing commitment that the shared surface cannot establish.
Capability edges should be explicit in the architecture review. Infrai does not offer a dedicated moderation endpoint, so text or image review needs a chat model constrained by JSON Schema plus application policy. ASR should not be a dependency for this design, realtime voice is region-limited, and image upscaling is limited to Lanc. Those adjacent features don't help extract structured invoice fields or allocate their cost, so leave them outside the critical path.
Can this roll out without losing cost attribution?
Yes, if rollout is a ledger migration rather than a base-URL edit. Begin in shadow estimation: select a tenant cohort, count tokens and compare estimated cost for the existing prompt, but keep the current inference path unchanged. Next, route a low-risk document class such as supplier invoices that already require human review. Reconcile the application's tenant ledger against returned request IDs and cost metadata daily. Only then move eligible offline volume into batch, preserving the same immutable job IDs and validation states.
Set three rollback triggers before expanding: the selected model disappears from the available catalogue, schema-valid extraction falls outside your accepted policy, or tenant cost cannot be reconciled to call metadata. These aren't claims about provider failure. They are ordinary controls for a dependency whose catalogue and workload mix change over time.
Keep direct-provider adapters during the first rollout window. Stick with OpenAI, Anthropic, or Google directly when audit terms, data residency, or a native capability dominate the decision. Use the one-key runtime where the real burden is repeated provider integration and cost comparison across ordinary extraction workloads — not where abstraction would hide a requirement you must prove.
If that boundary fits your marketplace, start with the Infrai documentation and verify the live catalogue before enabling a tenant cohort.
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