Many AI features begin with a model name embedded directly in the application.
const response = await client.generate({
model: "specific-model",
input
});
This is easy to implement, but it connects product behavior to a provider decision. When pricing, latency or model quality changes, application code must change with it.
A more durable approach is to define a service objective.
Define what the feature needs
interface IntelligenceObjective {
task: "reasoning" | "coding" | "vision" | "extraction";
minimumQuality: number;
maximumLatencyMs: number;
maximumCostUsd: number;
}
interface IntelligenceRequest {
objective: IntelligenceObjective;
input: string;
feature: string;
customerId: string;
}
The product specifies the expected result and operating limits. It does not select the provider.
async function runFeature(request: IntelligenceRequest) {
return intelligenceUtility.execute({
input: request.input,
task: request.objective.task,
constraints: {
quality: request.objective.minimumQuality,
latency: request.objective.maximumLatencyMs,
cost: request.objective.maximumCostUsd
}
});
}
The intelligence layer can now evaluate eligible models, apply policies and record the result.
Record every decision
A production response should include more than generated text.
interface IntelligenceResult {
output: string;
selectedModel: string;
provider: string;
latencyMs: number;
inputTokens: number;
outputTokens: number;
estimatedCostUsd: number;
}
This makes it possible to compare models by feature outcome instead of benchmark scores alone.
VectorNode is being developed around this idea: a programmable intelligence utility that turns model capabilities into measurable production resources.
The important abstraction is no longer one API format.
It is the service objective between the product and the intelligence it consumes.
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