AI product development
How Much Does It Cost to Add AI to Your App?
The cost to add AI to an app depends less on adding a chat box and more on the reliability, knowledge, permissions and evaluation the feature needs in real use.
The real cost to add AI to an app
A prototype that sends a prompt to a model can take little time. A production capability needs product design, context management, safeguards, evaluation, logging and a useful fallback. If answers affect money, health, access or business decisions, review and traceability become central requirements.
Apptheka Solutions works at $15–20 per hour. We begin with a narrow user decision and measurable quality criteria, then determine whether a direct LLM call, retrieval system, specialised workflow or conventional software is appropriate.
LLM integration
Basic LLM integration covers model access, prompts, structured output, error handling and a user experience for latency or uncertainty. Costs grow with conversation memory, attachments, tool use, moderation and multiple model providers.
Model usage is an ongoing operating cost. Estimate requests per user, tokens per request, response size and peak concurrency. Smaller models, caching and good context selection can reduce spend without damaging the experience.
RAG development
RAG retrieves relevant content before generation. Its work includes ingestion, parsing, permissions, chunking, indexing, retrieval, citations and refresh operations. The difficult part is usually maintaining trustworthy knowledge boundaries, not creating the vector index.
Evaluation should use representative questions and known answers. Measure retrieval quality separately from final response quality so the team can diagnose failures. Content owners need a clear way to update or remove knowledge.
Chatbots and conversational UX
An AI chatbot needs a purpose. Support bots may require account context and escalation; product assistants need awareness of application state; internal bots must respect employee permissions. Generic conversation without actions or trusted knowledge often creates novelty rather than durable value.
Design for misunderstanding. Show what the assistant can do, collect missing information, provide sources where useful and make human help reachable. Conversation analytics should protect sensitive data while revealing failure patterns.
AI agents and tool use
Agents can call tools, sequence actions and adapt a plan. That power adds risk. Each tool needs constrained inputs, explicit permissions, idempotency where possible and confirmation before consequential actions. Logs should make each step understandable.
Start with one bounded workflow rather than an open-ended autonomous system. Compare it against a deterministic alternative. Sometimes a normal rules engine with an LLM at one interpretation step is cheaper, safer and easier to support.
Budgeting implementation and operations
Separate discovery, implementation and recurring operations. Implementation includes product flows, backend work, evaluation and monitoring. Operations include model usage, vector storage, observability, content maintenance and periodic evaluation as models or data change.
Ask vendors how quality will be tested, how private data is handled, what happens when providers fail and how costs are capped. A responsible proposal identifies these decisions explicitly. Contact Apptheka Solutions with your workflow and data sources for a scoped recommendation.