A simple API call is not a production AI system
A feature that sends text to a model and displays the response can be a useful prototype. Production work adds prompt versioning, input validation, privacy decisions, retries, usage controls, evaluation, fallbacks and monitoring.
The cost is shaped by the reliability users need and the consequences of a wrong answer.
Common AI feature shapes
Chat assistants, document extraction, classification, recommendations, search with retrieval and workflow automation each have different data and evaluation needs. Agents add tool permissions, state, planning uncertainty and more complex failure modes.
Start with the simplest pattern that solves the workflow.
Costs beyond model usage
Budget for API usage, embeddings, vector storage, hosting, queues, observability, evaluation datasets, human review and engineering time. Usage costs can be small in a pilot and meaningful at scale.
A cost estimate should state expected volume, response size, model choice and caching assumptions rather than hiding them behind a feature label.
Guardrails and operations
Production AI needs permissions, prompt and output controls, sensitive-data handling, logging decisions, abuse limits and a way to review quality over time. These are product responsibilities, not optional polish.
Common questions
Can DevNexia help scope this work?
Yes. Share your goals, current product and constraints through the free assessment form and we can suggest a practical next step.
Are the costs and timelines guaranteed?
No. Custom software estimates depend on scope, integrations, team shape and feedback. A written estimate becomes more useful after requirements are understood.