# AI API gateway vendor evaluation checklist for SaaS teams > Source: > Published: 2026-06-04 22:33:17+00:00 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. ## 1. 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. ## 2. 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. ## 3. 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. ## 4. 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. ## 5. 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. ## 6. 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. ## 7. 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. ## 8. 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 ## 9. 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. ## 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. ## 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.