Choose an enterprise AI inference gateway by first deciding what it must govern: traffic to model APIs, self-hosted inference, agent and tool interactions, or some combination. Then evaluate compatibility, security, policy controls, routing, observability, deployment ownership, and workload-specific performance. A feature list is a starting point—not proof that a gateway fits your architecture or threat model.
Start by defining what the gateway must handle
“AI gateway” can describe products with different responsibilities. Some provide a common API layer in front of external model providers; others route requests to models your organization hosts; some also govern agent workflows and calls to tools. These categories can overlap, but they are not interchangeable.
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- Model API proxy: mediates application requests to one or more model providers, potentially adding shared access controls, routing, and telemetry.
- Self-hosted inference router: directs requests among models or serving systems operated in your own environment. The Kubernetes inference project describes this self-hosted generative-model focus.
- Agent and tool governance layer: may cover agent activity and tool interactions as well as model requests. Databricks documentation describes governance across models, agents, MCP servers, and tools.
Inventory the providers, models, API protocols, client libraries, and deployment environments you need to support. If teams use agents or tools, ask explicitly whether the gateway mediates those interactions or only model inference. Confirm how provider-specific capabilities are exposed, what happens when an API feature is unsupported, and how changes to provider interfaces are handled.
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Map the controls the gateway enforces against those already provided by your identity system, applications, cloud platform, and model providers. AWS guidance recommends secure API-key handling and integration with existing identity providers and SSO; that does not establish that a particular gateway implements the controls your organization needs.
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- How do applications and workloads authenticate? Can user or workload identity be carried through when required?
- Can authorization be scoped by team, application, environment, model, or operation?
- Where are provider credentials stored, who can administer them, and how are they rotated?
- Which access controls belong to the gateway, and which remain the application’s or provider’s responsibility?
- What network boundaries apply to the gateway, model endpoints, and administrative interfaces?
Trace a request from application to model and back. Record what request and response content the gateway can inspect or store, where logs are sent, and what redaction, access, and retention controls apply. AWS security guidance for inference endpoints discusses input validation, output filtering, PII sanitization, identity-based authorization, and network isolation. Treat these as safeguards to assess in context, not as a guarantee of compliance or protection by themselves.
Evaluate governance and guardrails against your policies
Find out how policies are created, tested, versioned, approved, and audited. Check whether they can differ by team, application, model, or environment, and whether they apply to prompts, responses, and tool calls. Establish what happens when a policy blocks or fails to evaluate a request: the system should have a defined, observable behavior rather than an ambiguous fallback.
Use a proof of concept to test the policies that matter to your organization, including how violations are surfaced and who can change a rule. Product documentation can show that a control exists; it cannot establish that the control meets your threat model, regulatory obligations, or internal approval process. AWS, Databricks, and Microsoft documentation describe gateway or governance capabilities, but those descriptions should be verified against your own requirements.
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Routing may be based on a configured model name or rules, or it may consider request content, task complexity, serving capabilities, latency, or capacity. AWS describes rule-based and semantic routing; Kubernetes and Google Cloud document model-aware or capability- and metrics-informed approaches. Compare the decisions a candidate can make with the signals and objectives relevant to your workload.
Ask whether operators can see which provider or model served a request and why. Then test traffic splitting, mirroring, priority, fallback, retries, timeouts, and behavior when a target is unavailable or overloaded. Clarify whether a retry can send a request to a different model, whether that is acceptable for the application, and how routing decisions are recorded.
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Do not assume that a routing feature will reduce cost, improve quality, or lower latency for your requests. Choose an objective—such as availability, latency, cost, or task quality—and compare outcomes using representative requests and defined failure conditions.
Require usable observability, cost attribution, and audit data
Decide what operators need to diagnose incidents and what finance or platform teams need to understand usage. At a minimum, assess request rate, latency, error rate, and capacity or saturation signals. Check whether telemetry includes token usage, provider and model selection, and enough application or team context to attribute consumption.
Confirm how metrics and logs can be exported to the monitoring, alerting, and incident-response systems already in use. AWS guidance recommends centralized observability and exporting metrics to established observability and incident-management tools. Google Cloud documents inference request metrics and Cloud Monitoring and Logging integration; these are examples of documented product capabilities, not comparative evidence about vendors.
Make content logging a separate decision from operational telemetry. If prompts or responses are captured, specify which fields are retained, redacted, access-controlled, or excluded, and test those settings. A gateway can centralize visibility, but captured content may itself contain sensitive information.
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Assess managed cloud services, platform-integrated gateways, and self-hosted options against your data boundaries, required regions, network topology, expected scaling, and operating model. Include the less visible ownership questions: who upgrades the gateway, manages its availability, tunes capacity, handles incidents, and integrates it with existing identity and observability systems?
The Kubernetes inference project documents a self-hosted routing focus, while AWS and Google Cloud document cloud deployment patterns and integrations. These examples do not determine which model is right for your organization; compare them with your infrastructure constraints and staffing.
Check product status, regional availability, limits, and reliability commitments directly. Microsoft labels its AI Gateway tier documentation as preview and warns that features, regions, limits, telemetry fields, and setup flows may change, with best-effort reliability. Preview status is time-sensitive, so confirm current terms and status with Microsoft before procurement.
Run a proof of concept with representative traffic
Build a test set from the request shapes and traffic patterns your applications actually generate. Run it in the intended deployment and network environment; results from a different topology may not predict production behavior.
- Check compatibility: send requests from the clients and protocols in scope, including provider-specific features your applications rely on.
- Exercise streaming: verify streamed responses and measure time to first token as well as end-to-end latency where streaming is part of the workload.
- Test policy behavior: check allowed and blocked requests, policy failures, response handling, and any tool interactions in scope.
- Test routing and resilience: confirm the selected destination, traffic-splitting behavior, retries, timeouts, failover, and operation when a target is unavailable or at capacity.
- Measure under load: record latency, throughput, errors, and capacity behavior using the same request mix and conditions for each candidate.
- Verify operational evidence: confirm that metrics, usage attribution, alerts, audit records, and any content-logging settings behave as configured.
Set acceptance thresholds before testing, based on your application’s needs. Google Cloud documents predicted latency-based routing and inference request metrics, but those capabilities are not vendor-neutral benchmarks. The available documentation does not establish comparative performance across gateway vendors; only a controlled test of your workload can do that.
Quick Recap
Use a consistent comparison checklist
| Evaluation area | Questions to resolve |
|---|---|
| Scope and compatibility | Which models, providers, protocols, clients, environments, and agent or tool interactions are covered? |
| Security and privacy | How are identity, authorization, credentials, request and response content, logs, and network access controlled? |
| Governance | Can policies be administered centrally, audited, and applied consistently to the traffic in scope? |
| Routing and resilience | Which routing signals are available, and how do failover, retries, traffic splitting, timeouts, and rollouts behave? |
| Observability and cost | Which operational and usage fields are available, exportable, and attributable to an application or team? |
| Deployment and operations | Where does it run, who owns scaling and upgrades, and does it fit existing identity and observability systems? |
| Performance | Does it meet workload-specific latency, throughput, and availability targets under representative test conditions? |
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