Most teams compare AI API gateways by headline model coverage or token price. Those matter, but they are not enough for production SaaS work.
If an OpenAI-compatible gateway will sit between your app and your users' AI usage, it becomes part of billing, reliability, security, and support. This checklist is a practical way to evaluate vendors before routing real traffic. Context: FerryAPI is one OpenAI-compatible AI API gateway. I am affiliated with it, so this article is intentionally written as a general vendor checklist rather than a fake-neutral review.
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- API compatibility and migration friction
Start here because migration cost decides whether the gateway is practical.
Ask:
- Does the gateway expose an OpenAI-compatible
base_url
and API-key interface?
- Can existing OpenAI SDK clients switch by changing only
base_url
, api_key
, and model names?
- Which endpoints are supported: chat completions, responses, embeddings, image, audio, batch, streaming?
- Does streaming behave like the upstream SDK expects?
- Are error responses close enough to OpenAI-style errors for existing retry and logging code?
- Can the gateway preserve request and response shapes, or does it require a custom SDK?
- Are model aliases documented and stable?
- Can teams run a staging-only or small traffic-slice migration before full rollout?
Red flag: the vendor says "OpenAI-compatible" but requires a proprietary SDK for common chat/completions use cases.
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- Provider and model access
A gateway is useful only if model access matches the application.
Check:
- Which providers and model families are supported today?
- Are supported models listed publicly, or only after signup?
- Can you pin exact models rather than vague "best" or "auto" choices?
- Is fallback/routing optional or mandatory?
- Are provider outages surfaced clearly?
- Does the vendor support both low-cost and high-capability choices?
- Are limits for rate, context length, output size, and regions clear?
Practical test: run the same 10 to 50 real prompts through your current provider and the gateway. Compare latency, outputs, token accounting, and error behavior.
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- Cost controls and billing governance
For SaaS teams, the gateway's value is not only cheaper tokens. It is preventing uncontrolled spend and explaining where spend came from. Ask:
- Can you set prepaid balances, hard caps, or per-key quotas?
- Can each customer, project, or workspace have separate API keys?
- Can you track usage by API key, project, model, and time period?
- Is billing based on actual token usage, credits, markup, subscription, or a mix?
- Are price changes communicated before they affect production traffic?
- Can you export usage data for internal billing or customer invoicing?
- Are failed requests billed? If yes, which failure types?
- Can compromised keys be disabled or rotated quickly?
Red flag: pricing is lower on the homepage, but the dashboard cannot explain where every unit of spend came from.
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- Reliability and operational behavior
Production LLM traffic needs boring reliability.
Ask:
- Is there a status page or incident history?
- Are retry, timeout, and fallback behaviors documented?
- Can you configure failover order, or is routing opaque?
- Does the gateway add meaningful latency? What is p50/p95 in your own region?
- Does streaming fail gracefully under provider errors?
- Can the vendor isolate tenant traffic and avoid cross-customer leakage?
- What happens when balance is depleted or quota is reached?
- Are maintenance windows announced?
Practical test: simulate exhausted quota, invalid key, unavailable model, long context, and streaming cancellation before production launch.
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- Security and data handling
If prompts may include user data, treat the gateway as a security-critical vendor. Check:
- What is logged: prompts, completions, metadata, IPs, headers, API keys?
- Can prompt/content logging be disabled?
- How long are logs retained?
- Are secrets encrypted at rest and in transit?
- Are upstream provider keys hidden behind the gateway?
- Does the vendor support key rotation and scoped keys?
- Is there role-based access control for dashboard users?
- Are audit logs available for key creation, balance changes, and admin actions?
- Which jurisdictions and subprocessors are involved?
- Is there a DPA, SOC 2, ISO 27001, or equivalent evidence if your org needs it?
Red flag: no clear answer on whether prompt content is stored, replayed, or used for analytics/training.
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- Developer experience
A gateway should reduce operational burden, not become another integration project.
Ask:
- Is there a concise quickstart for OpenAI SDK migration?
- Are examples available for Python, Node.js, curl, and common frameworks?
- Is model naming easy to discover?
- Are error codes and troubleshooting steps documented?
- Is the dashboard usable for non-engineering operators who manage spend?
- Is support reachable when keys, billing, or production traffic break?
- Are there examples for staging/prod key separation?
Practical test: ask one engineer who did not evaluate the vendor to follow the docs from scratch. Time the migration.
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- Fit by team type
Solo founder or indie hacker
Prioritize fast setup, transparent prepaid spend, low minimum commitment, a clear model list, and minimal SDK changes.
Avoid enterprise-only sales flows, required contracts before testing, and opaque routing with no usage detail.
SaaS team
Prioritize per-customer/project API keys, usage records for customer billing, quotas and balance controls, reliable exports, and staging/prod separation.
Avoid a single shared key with no attribution, no way to cap abusive customers, and unclear handling of failed requests.
Platform or enterprise engineering
Prioritize security documentation, audit logs, RBAC, DPA/compliance evidence, incident process, and configurable routing/fallback.
Avoid no formal support path, no retention policy, and no operational transparency.
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- Quick scoring matrix
Score each item from 0 to 3:
- 0 = not available or unknown
- 1 = available but weak or manual
- 2 = good enough for production
- 3 = strong and well documented
Categories:
- OpenAI-compatible migration
- Model/provider coverage
- Per-key usage tracking
- Quotas / prepaid controls
- Reliability transparency
- Security / data retention clarity
- Developer docs
- Support / incident handling
- Pricing clarity
- Export / billing operations
Interpretation:
- 24 to 30: strong candidate for pilot and production evaluation
- 16 to 23: usable, but identify gaps before routing critical traffic
- Below 16: keep as experimental unless the missing areas are irrelevant to your use case
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- Pilot plan
A safe pilot can be small and evidence-driven:
- Create a staging key.
- Point one non-critical service to the gateway using OpenAI-compatible
base_url
and key settings.
- Run a fixed prompt suite across current provider and gateway.
- Compare success rate, p50/p95 latency, token accounting, output quality, and error behavior.
- Set a hard spend cap or prepaid balance.
- Move a small percentage of real traffic only after staging results are acceptable.
- Review usage export and billing records after the pilot.
- Document rollback steps before increasing traffic.
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Where FerryAPI fits
If evaluating FerryAPI, the most relevant areas to inspect are: A good first test is simple: take an existing OpenAI SDK integration, switch the base URL and API key in staging, then verify whether your existing retry, logging, and billing assumptions still hold.
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Final thought Do not evaluate an AI API gateway only by the model list. Evaluate the operating system around the model list: keys, quotas, usage records, reliability behavior, security posture, and rollback safety.
That is what decides whether the gateway can safely carry production SaaS traffic.