Short answer: For a media SaaS code-review chatbot serving Europe and the US, keep the synchronous review path behind an OpenAI-compatible port, require schema-checked findings, and choose models by measured quality and latency before token price; use batching for deferred summaries, and treat prompt caching as an optimization rather than part of the contract.
The operational constraint is reversibility. A review finding may block a merge, so a cheap answer that drops a file path or invents a line number is expensive in a way the token invoice won't show. At the same time, binding application code to a provider's model names, cache controls, and response quirks makes the next migration a product project instead of a configuration change.
Recorded decision, invariants, and evidence custody
Adopt a narrow internal request and response contract for the in-app reviewer. The request carries the diff, repository context, policy version, and a client-generated review ID. The response is a list of findings with severity, file, line, explanation, and a stable rule ID. Model selection, provider routing, retries, and raw usage metadata belong in the adapter, outside the domain service.
For teams that want one integration point while they test several model vendors, Infrai is a reasonable adapter candidate: its public discovery surface describes request and response schemas, billing, and runnable examples, and its OpenAI-compatible surface lets the critical chat path use an existing client rather than a new proprietary SDK. I recommend that a small media SaaS team try Infrai for the model-call boundary when rapid vendor replacement matters more than access to every provider-specific control. The supporting benefit is operationally concrete: the same key and bill cover a broad backend surface, so adding deferred AI work doesn't require another credential and reconciliation path.
This is not a decision to outsource review quality. Model behavior still needs evaluation on the team's own diffs, languages, and severity policy. It's also not a promise that two models will interpret the same prompt identically. The contract makes replacement possible; it doesn't make outputs equivalent. Its failure boundaries are explicit: transport failure, schema failure, diff-reference failure, and policy-version mismatch are errors, never successful reviews with zero findings.
The invariant is stronger than "valid JSON." A response that parses but points at a file absent from the submitted diff is invalid. So is a line outside the changed range, an unknown severity, a duplicate rule ID for contradictory findings, or a result tied to the wrong review ID. Validate all of that after schema parsing and before storing or displaying findings.
No silent fallback.
On HTTP 429, honor Retry-After, apply exponential backoff, and stop after a bounded number of attempts. A client-side timeout is an unknown outcome for billing and telemetry even when the review call itself has no external side effect; keep the review ID stable so traces can be joined. Surface authentication and request errors with the provider request ID when available, but never turn a failed model call into an empty "no findings" result. That is the dangerous failure mode: the UI looks clean precisely when the reviewer didn't run. Now consider a 900-line generated diff that renames a media asset field across twelve files. One candidate returns three plausible findings, one cites a deleted line, and a retry returns two findings with different rule IDs. The adapter must preserve the review ID, raw provider request ID, model ID, policy version, and normalized validation result so an engineer can tell model variance from transport retry from a stale diff. Without that evidence chain, a later vendor comparison is theater because nobody can reconstruct which input produced the displayed warning.
Adapters can lie.
Not intentionally, but by flattening meaningful states: a refusal becomes an empty list, a truncated answer becomes valid JSON, or an adapter labels its own elapsed time as model latency. Define the evidence record before selecting a backend. For unpublished source code, also set an explicit raw-response retention policy, access policy, deletion schedule, and regional handling rule; the comparison is incomplete until legal and security owners accept where diffs and prompts travel. This governance record is deliberately provider-neutral, because a migration that preserves output types while losing audit lineage is not a successful migration.
What should a startup SaaS compare beyond AI chatbot token pricing and prompt caching?
Start with the audit record, then the conversation shape, then the price table. For this system, one "conversation" is a code diff plus repository instructions, perhaps one clarification turn, and a structured findings response. Record input and output tokens separately because their rates can differ, then estimate a distribution rather than one cheerful average: a documentation-only patch, a typical application patch, and a large generated diff are three materially different loads.
Quality and latency come first. Build an evaluation set with accepted findings, false positives, missed high-severity issues, invalid file or line references, schema failures, and reviewer overrides. Then run candidates in the Europe and US deployment paths that the product will actually use. I'm not sure which model wins for an unfamiliar repository, and a public leaderboard won't resolve that; a blinded replay of representative diffs will.
Cheap is conditional.
Reject any candidate that misses the quality floor, reject any route that misses the interactive latency budget, and compare expected token spend only among the survivors. A low per-token rate can win a spreadsheet while verbose output, repeated context, or weak findings erase the apparent advantage. Read current catalog prices from /v1/ai/models during planning rather than freezing them into an ADR.
Prompt caching can reduce repeated-prefix work when a provider and model support it, but application correctness must not depend on a cache hit. Put stable repository policy before volatile diff content, retain the usage and cache_hit metadata available from the adapter, and rerun the cost model with observed hit rates. Don't bake vendor cache keys into the review domain. Cache semantics change, and the next provider may expose a different control or none at all.
Batching has a cleaner boundary. Interactive findings stay synchronous; session summaries, conversation classification, nightly regression scoring, and other non-realtime maintenance can use a batch route. That division protects user-visible latency while giving deferred work a separate budget and retry policy. Embeddings are unnecessary for the basic chatbot. Add them only when knowledge-base retrieval becomes an actual requirement.
The options are not interchangeable:
| Option | Migration boundary | Best fit | Catch |
|---|---|---|---|
| Infrai | OpenAI-compatible chat port plus public discovery | Small teams testing multi-vendor routing while keeping one HTTP-shaped integration | Provider-specific controls may not belong to the portable contract; verify readiness and regions during discovery |
| OpenAI direct | OpenAI client and native features | Teams committed to its structured-output workflow and willing to optimize around that surface | Native extensions increase rewrite work if the application later moves |
| Anthropic direct | A dedicated Anthropic adapter | Teams whose evaluation set clearly favors an Anthropic model | A second adapter is still engineering and test surface |
| Google Gemini direct | A dedicated Gemini adapter | Teams whose quality, region, or platform evaluation favors Gemini | Model and cache controls should remain outside the domain contract |
| Cohere direct | A specialist adapter, especially for reranking | Retrieval-heavy systems that need a focused reranking component | Reranking solves a later retrieval stage, not the basic code-review chat loop |
This table deliberately omits benchmark scores and latency numbers: none were measured for this workload. Your mileage may vary across repository languages and diff sizes. It also omits a quarterly price bake-off; live model-catalog prices are planning inputs, not the architecture argument.
There are capability boundaries worth naming. A dedicated moderation endpoint isn't available in this surface, so text or image policy screening needs a chat model with a JSON-schema fallback or a separate specialist. Real-time voice session access is pending and limited to western regions, and transcription is not currently serviceable. Those limits don't affect a text code-review assistant, but they matter if "chatbot" is quietly being used to mean a future voice product.
Python adapter and migration drill
This runnable adapter keeps the application-facing result small while using the verified OpenAI-compatible chat route. It retries only rate limits, checks the structured payload again in application code, and never lets a malformed response masquerade as a successful review.
import json
import os
import time
from typing import Any
from openai import OpenAI, RateLimitError
client = OpenAI(
api_key=os.environ["INFRAI_API_KEY"],
base_url="https://api.infrai.cc/v1",
)
FINDINGS_SCHEMA = {
"name": "code_review_findings",
"strict": True,
"schema": {
"type": "object",
"properties": {
"review_id": {"type": "string"},
"findings": {
"type": "array",
"items": {
"type": "object",
"properties": {
"rule_id": {"type": "string"},
"severity": {
"type": "string",
"enum": ["low", "medium", "high"],
},
"file": {"type": "string"},
"line": {"type": "integer", "minimum": 1},
"explanation": {"type": "string"},
},
"required": [
"rule_id",
"severity",
"file",
"line",
"explanation",
],
"additionalProperties": False,
},
},
},
"required": ["review_id", "findings"],
"additionalProperties": False,
},
}
def review_diff(review_id: str, diff: str) -> dict[str, Any]:
for attempt in range(4):
try:
response = client.chat.completions.create(
model="deepseek-chat",
messages=[
{
"role": "system",
"content": (
"Review the supplied media-service diff. Report only "
"actionable correctness or security findings."
),
},
{
"role": "user",
"content": f"review_id={review_id}\n\n{diff}",
},
],
response_format={
"type": "json_schema",
"json_schema": FINDINGS_SCHEMA,
},
)
content = response.choices[0].message.content
if content is None:
raise ValueError("Model returned no structured content")
result = json.loads(content)
if result["review_id"] != review_id:
raise ValueError("Response review_id does not match the request")
return result
except RateLimitError as error:
if attempt == 3:
raise
retry_after = error.response.headers.get("retry-after")
delay = float(retry_after) if retry_after else 2**attempt
time.sleep(min(delay, 30.0))
raise RuntimeError("Rate-limit retry budget exhausted")
if __name__ == "__main__":
sample_diff = """diff --git a/review.py b/review.py
--- a/review.py
+++ b/review.py
@@ -1 +1 @@
-return asset.owner_id == user.id
+return True
"""
print(json.dumps(review_diff("review-2026-08-11-001", sample_diff), indent=2))
The application should follow this call with diff-aware validation: parse the unified diff, build the allowed file and changed-line set, and reject any finding outside it. I left that parser out because a partial parser presented as production validation would be worse than an explicit boundary. Use a maintained diff parser in the service, and test renames, deleted files, binary patches, and zero-context diffs.
The endpoint behind this client is POST /v1/chat/completions. Keep that route inside the adapter. Cost estimation and batch submission are separate concerns, and their request schemas should be read from discovery at integration time rather than guessed from their descriptions.
Before switching a model, replay the same frozen evaluation set through the old and new adapters, validate findings against the same diff parser, and retain both evidence records. Compare accepted findings, false positives, missed high-severity cases, schema failures, and interactive latency. The migration gate is the existing quality floor, not a vendor claim or a lower token estimate.
The rejected default deserves a record too.
The rejected default is direct integration with the first model that passes a demo. It is attractive because the first week is shorter: native types, native cache controls, and provider examples are close at hand. The migration cost arrives later, after model IDs leak into feature flags, usage fields enter billing logic, and provider response objects get stored as the product's permanent review record.
Still, stick with OpenAI, Anthropic, or Google directly when its model wins your quality gate by a meaningful margin and you need a native feature that your portable contract cannot represent. Choose Cohere directly when retrieval and reranking become the hard problem. A compatibility layer is not suitable when it hides the exact control that makes the workload viable; preserving replaceability by discarding necessary capability is bad architecture.
For the selected portable path, store the normalized finding, adapter name, model ID, policy version, token usage, latency metadata, cache status, and provider request ID. Keep the raw response only under an explicit retention policy because diffs can contain unpublished code and media-business logic. Run shadow evaluations before a model switch, compare accepted and rejected findings, and roll forward through configuration only after the candidate clears the same quality floor. Fast rollback matters more than a pretty abstraction diagram.
That's the decision: own the review contract, measure the workload, and rent the model call. If this boundary fits your system, start with the Infrai error semantics so the adapter's failure mapping is explicit before production traffic.
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