AI development with an engineering team costs team size times months times the monthly rate per engineer. At Ryz's public rates of $7,000 to $15,000 per engineer per month, the five example projects on this page run from about $135,000 for a three-month RAG assistant to about $480,000 for a six-month MLOps platform, and after launch you also pay run costs, mostly model tokens and cloud, which you can estimate with our LLM cost calculator.
Build cost = team size × months × monthly rate per engineer. Run cost = model usage + cloud + the people who keep the system healthy. Everything below is that arithmetic, with the assumptions stated, so you can change any input and recompute.
The team shapes and durations below are illustrative assumptions for a typical first production release, not quotes and not promised timelines. Your quote is scoped to your system, and you get a plan, a price and the names of the engineers before anything starts. To model a different shape, use the AI development cost calculator.
A retrieval-augmented assistant answers questions from your policies, agreements, tickets or research, with citations back to the source. Example team: a tech lead, a senior backend engineer, a senior ML engineer and a senior data engineer. Math: 3 × $10,000 to $15,000 = $30,000 to $45,000, plus $15,000 for the lead = $45,000 to $60,000 a month, times 3 months. What drives cost up: many document sources with different permissions, messy scans that need OCR, and strict citation requirements. Most of the work is ingestion, chunking, access control and evaluation, not the model call. See RAG development and Pinecone vs pgvector.
An agent reads a request, decides which tools to call, and updates your systems: opening tickets, drafting replies, reconciling records. Example team: a tech lead, three senior engineers (backend, ML, integrations) and a mid-level QA engineer focused on agent testing. Math: 3 × $10,000 to $15,000 = $30,000 to $45,000; 1 × $7,000 to $10,000; plus $15,000 for the lead = $52,000 to $70,000 a month, times 4 months. Agents cost more than assistants because every tool they can call needs permissions, guardrails, failure handling and tests. See AI agent development, AI agent testing and AI agents vs chatbots.
Document processing pulls structured data out of PDFs, forms, statements and emails and writes it into the system of record, with humans reviewing low-confidence fields. Example team: a tech lead, two senior engineers (ML and backend) and two mid-level engineers on integrations and review tooling. Math: 2 × $10,000 to $15,000 = $20,000 to $30,000; 2 × $7,000 to $10,000 = $14,000 to $20,000; plus $15,000 = $49,000 to $65,000 a month, times 4 months. Cost rises with the number of document layouts, handwriting, and how many downstream systems need the output. Our marketing compliance work for a leading alternative asset manager reviewed 8,000+ documents and cut turnaround from days to hours. See also AI underwriting document processing.
Fine-tuning adapts a model to your format, vocabulary or task using your labeled examples. Example team: a tech lead, two senior ML engineers and a senior data engineer to build the training and evaluation sets. Math: 3 × $10,000 to $15,000 = $30,000 to $45,000, plus $15,000 = $45,000 to $60,000 a month, times 3 months. Training and hosting compute is billed separately by your provider; check current rates on the OpenAI API pricing page or your cloud's GPU pricing. Most teams should try retrieval and better prompts first: see RAG vs fine-tuning and LLM fine-tuning.
An MLOps platform gives your teams a standard way to train, evaluate, deploy, monitor and roll back models and LLM applications. Example team: a tech lead, three senior engineers (platform, ML, DevOps) and two mid-level engineers. Math: 3 × $10,000 to $15,000 = $30,000 to $45,000; 2 × $7,000 to $10,000 = $14,000 to $20,000; plus $15,000 = $59,000 to $80,000 a month, times 6 months. Cost depends on how many teams and model types the platform must serve and how much existing CI/CD and infrastructure-as-code you can reuse. See MLOps services and MLOps engineer rates.
The build is a one-time cost. These keep running for as long as the system is in use:
Public ranges are wide because they mix very different projects. Azumo's AI development cost guide, for example, says costs range "from $5,000 for a basic chatbot to over $1 million for enterprise-scale systems" (Azumo, August 2026). The examples above sit in the middle of that span because they describe production systems integrated with enterprise data and permissions, not prototypes. A useful sanity check on any quote: divide it by the number of engineers and months and see what monthly rate it implies.
If you do want senior engineers who build in your cloud and repos and ship to production, our AI pod teams work on your team, on US business hours, and usually start in under two weeks once you say go.
With a team, it is team size times months times the monthly rate. At Ryz rates of $7,000 to $15,000 per engineer per month, the example projects on this page range from about $135,000 for a three-month RAG assistant to about $480,000 for a six-month MLOps platform, before model and cloud costs.
In our example, a tech lead, three senior engineers and one mid-level engineer for four months comes to $208,000 to $280,000. Agents cost more than chatbots because every tool they call needs permissions, guardrails and tests.
Model tokens, cloud infrastructure, evaluation and monitoring, and the engineers who maintain it. A two-person senior run team is $20,000 to $30,000 a month at Ryz rates; model spend depends on traffic and can be estimated in our LLM cost calculator.
The team cost can be similar, but fine-tuning adds training compute, labeled data work and retraining whenever your data changes. Most teams get further, sooner, with retrieval and good prompts and fine-tune only when that falls short.
They describe different things: a prototype chatbot and a production system wired into enterprise data and permissions are different projects. Ask any provider for the team size, duration and monthly rate behind a number so you can compare like with like.
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