LLMs, RAG, computer vision, and custom ML — the real trade-off, not a price table.
Last updated October 24, 2026
MVP starts from
₹2.5L
MVP timeline
8 weeks
Proposal turnaround
24 hours
Bigger builds fall into Ubikon's published app tiers (₹5L – ₹8L · ₹8L – ₹15L · ₹25L+); everything beyond that is scoped on a free call.
AI project cost depends almost entirely on one decision: are you integrating an existing AI API, or building and training a custom model? API integration (OpenAI, Claude, Gemini) is the cheaper, faster path for the large majority of products. Custom model training is a different scale of project — data collection, labelling, training infrastructure, and ongoing retraining — and should only be chosen when an API genuinely cannot do the job. Below is what each project type actually involves; Ubikon scopes and quotes every AI project individually after a free call, since the API-vs-custom decision alone can change the cost by an order of magnitude.
LLM Integration
Products adding AI chat, summarisation, or generation
RAG Pipeline
Products that need AI to answer from your own data
Computer Vision
Products analyzing images or video
Custom ML Platform
Enterprises needing proprietary models
ML Engineering / Model Work
From prompt engineering for an API integration through to full model training for a custom build.
Backend / API Development
The service layer that calls the model and exposes it to your product.
Frontend / Product
The UI around the AI feature — chat interface, results display, feedback loops.
Data Preparation
Collecting, cleaning, and labelling training data — relevant only for custom model work, not API integrations.
MLOps / Infrastructure
Deployment, monitoring, and retraining pipelines for anything beyond a stateless API call.
Testing & Evaluation
Evaluating model output quality and guarding against regressions as prompts or models change.
API vs custom model
Using an existing provider API is dramatically cheaper and faster than training a custom model — this single decision is the biggest lever on an AI project's cost.
Data availability
Custom models need labelled training data. Collecting and labelling it, if it doesn't already exist, is often a bigger line item than the modelling work itself.
Inference latency requirements
Sub-100ms inference requires optimisation, quantisation, or edge deployment that a simple API call doesn't need.
Ongoing API costs
Provider API usage is billed per token/request and scales with traffic — budget for this as an ongoing operating cost, not a one-time build cost.
Multi-modal requirements
Combining text, image, and audio in one pipeline adds real complexity beyond a single-modality integration.
Compliance (HIPAA, GDPR)
Healthcare or regulated-data AI products often need on-premise inference or zero-data-retention API arrangements, which changes the architecture.
"Using ChatGPT API means we have AI"
Calling a provider API is a starting point, not a differentiator by itself. The value is in the prompts, context management, and UX built around it.
"We need to train our own model"
Most products don't need custom training. A well-prompted existing model beats a custom model for the large majority of use cases, at a fraction of the cost and time.
"AI development is too expensive for startups"
A single, well-chosen AI feature using an existing API is one of the cheaper ways to add real product value — the expensive path is custom model training, not AI itself.
How much does it cost to add a chatbot to my app?
It depends on scope — context handling, conversation history, and the UI around it — so Ubikon scopes this on a free call rather than quoting a generic figure. API-based chat is on the cheaper end of AI work.
Is it cheaper to use OpenAI/Claude or build a custom model?
Using a provider API is cheaper for the large majority of use cases. Custom models make sense only with unique proprietary data, very high inference volume, or strict data-residency requirements.
What are the ongoing costs of an AI product?
Provider API usage is billed by token/request and scales with traffic; a vector database for RAG adds its own hosting cost. These are operating costs, not one-time build costs, and vary by usage — we help you estimate them during scoping.
How long does it take to build an AI feature?
A simple LLM integration is one of the fastest things to ship. A RAG pipeline with document management takes longer. Custom computer vision or model training is the longest path, including data preparation time. Exact timelines are set during scoping.
The 60-second visual version
API integration vs custom model training is the real cost fork in AI development — not a fixed price table.
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