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Teams often adopt AI providers directly from application code. That works for prototypes, but production quickly exposes missing controls around rate limits, cost, prompt boundaries, fallback, and provider visibility.
Case 02 · Flagship
Cloud-Native AI Gateway
A production AI gateway turns model access into a governed platform capability: requests enter one boundary, policy is applied consistently, and observability follows every model call.
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How it works
From request to a governed model call.
Cloud Native AI Gateway by Andrey Lesnikov — one platform boundary for policy, budget, fallback, and OpenTelemetry across model providers.
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Enter one boundary
Apps and agents hit a single gateway instead of scattering provider SDKs, keys, and retry logic across services.
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Apply policy and budget
Rate limits, prompt bounds, and spend caps are evaluated before a model is selected — decisions stay visible on the response path.
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Route with fallback
Primary and secondary providers stay explicit so failover is an infrastructure path, not an app-level improvisation.
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Observe every call
OpenTelemetry traces and cost signals follow the request so incidents and spend spikes have an owner.
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Evidence
From problem to proof.
How the system is framed, operated, and proven — not a slide deck summary.
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The gateway sits between applications and model providers. It evaluates request policy, selects the target provider/model, applies budgets and rate limits, emits OpenTelemetry traces, and keeps provider fallback paths explicit.
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A gateway adds an extra hop and must be operated carefully. The payoff is central control: safer provider migration, clearer budget enforcement, reusable observability, and one place for policy.
Constraints
Latency, observability, prompt safety, rate limits, and failover behavior.
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Related
Nearby systems.
AI Runtime Platform
AI workloads often start as API calls but become production systems that need identity, policy, scaling, telemetry, and clear ownership
Every model call: policy + trace + budget
AI Infra Control Plane
Private AI platforms need shared governance across identity, policy, audit, cost, and SLO signals — not scattered service configs
Private AI with explicit platform contracts
Cost and Token Observability
AI and cloud costs can grow quietly when usage is disconnected from teams, services, and deployment changes
Token spend visible before finance week
Available for meaningful infrastructure conversations