{"slug": "design-ai-features-with-budgets-not-model-names", "title": "Design AI Features With Budgets, Not Model Names", "summary": "A developer proposes decoupling AI features from specific model names by defining service objectives that specify task, quality, latency, and cost constraints. This approach allows an intelligence layer to select the best model dynamically and record detailed results for comparison. The concept is being developed as VectorNode, a programmable intelligence utility.", "body_md": "Many AI features begin with a model name embedded directly in the application.\n\nconst response = await client.generate({\n\nmodel: \"specific-model\",\n\ninput\n\n});\n\nThis 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.\n\nA more durable approach is to define a service objective.\n\nDefine what the feature needs\n\ninterface IntelligenceObjective {\n\ntask: \"reasoning\" | \"coding\" | \"vision\" | \"extraction\";\n\nminimumQuality: number;\n\nmaximumLatencyMs: number;\n\nmaximumCostUsd: number;\n\n}\n\ninterface IntelligenceRequest {\n\nobjective: IntelligenceObjective;\n\ninput: string;\n\nfeature: string;\n\ncustomerId: string;\n\n}\n\nThe product specifies the expected result and operating limits. It does not select the provider.\n\nasync function runFeature(request: IntelligenceRequest) {\n\nreturn intelligenceUtility.execute({\n\ninput: request.input,\n\ntask: request.objective.task,\n\nconstraints: {\n\nquality: request.objective.minimumQuality,\n\nlatency: request.objective.maximumLatencyMs,\n\ncost: request.objective.maximumCostUsd\n\n}\n\n});\n\n}\n\nThe intelligence layer can now evaluate eligible models, apply policies and record the result.\n\nRecord every decision\n\nA production response should include more than generated text.\n\ninterface IntelligenceResult {\n\noutput: string;\n\nselectedModel: string;\n\nprovider: string;\n\nlatencyMs: number;\n\ninputTokens: number;\n\noutputTokens: number;\n\nestimatedCostUsd: number;\n\n}\n\nThis makes it possible to compare models by feature outcome instead of benchmark scores alone.\n\nVectorNode is being developed around this idea: a programmable intelligence utility that turns model capabilities into measurable production resources.\n\nThe important abstraction is no longer one API format.\n\nIt is the service objective between the product and the intelligence it consumes.", "url": "https://wpnews.pro/news/design-ai-features-with-budgets-not-model-names", "canonical_source": "https://dev.to/_9de8b28cd0a409b80cfdc/design-ai-features-with-budgets-not-model-names-652", "published_at": "2026-06-30 03:48:34+00:00", "updated_at": "2026-06-30 04:18:49.519596+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-infrastructure", "developer-tools"], "entities": ["VectorNode"], "alternates": {"html": "https://wpnews.pro/news/design-ai-features-with-budgets-not-model-names", "markdown": "https://wpnews.pro/news/design-ai-features-with-budgets-not-model-names.md", "text": "https://wpnews.pro/news/design-ai-features-with-budgets-not-model-names.txt", "jsonld": "https://wpnews.pro/news/design-ai-features-with-budgets-not-model-names.jsonld"}}