Planning an AI feature
AI assistant and
LLM cost calculator.
Estimate what an AI assistant inside your product could cost to build and run. LLM means large language model, the system that generates the assistant’s responses. See what changes as usage grows, then share the assumptions with your team.
USD · No signup · Editable assumptions
What does chatbot development add to the budget?
A monthly model bill does not include the work to integrate the feature. Choose a scope, then enter your own hour estimates. These start at zero because the work needs to be scoped; zero does not mean delivery is free.
Keep ongoing ownership in the monthly estimate.
Include retrieval and storage, monitoring, evaluation, maintenance and human review in “Other monthly operating costs” above, or estimate them separately. Avoid counting the same work in both the extra-usage allowance and other costs.
A scope is incomplete until you know who owns incidents, permission changes and model updates. Use our build-versus-buy worksheet to compare those responsibilities.
A worked example: 1,000 active users
This illustrative chatbot cost estimate uses 40 requests per user each month and two model calls per request: 40,000 requests and 80,000 calls. A token is a small unit of text used to measure model input and output.
Assume each call uses 6,000 input tokens at $0.80 per million and 500 output tokens at $4.00 per million. These are editable assumptions, not current provider prices.
- Input: 480 million tokens × $0.80
- $384.00
- Output: 40 million tokens × $4.00
- $160.00
- Extra model usage: 20% of $544
- $108.80
- Other monthly operations
- $300.00
- Monthly operating total
- $952.80
If implementation took 100 hours at an assumed $150 per hour, that would add $15,000 once. Twelve months at the same usage plus that implementation allowance would be $26,433.60. This is arithmetic for planning, not a delivery estimate or COGNI.BUILD quote.
Open these assumptions in the calculator (new tab)
What changes the monthly bill?
More active users, longer conversation histories, retrieved documents, repeated tool calls and retries can increase usage. Adjust each assumption above; a model’s token price alone does not describe the whole feature’s cost.
What does implementation add?
Work on data connections, the interface, permissions and evaluation belongs in a separate implementation budget. The hourly example is a placeholder until that work is scoped.
Which costs are excluded?
Taxes, discounts and growth during the year are not modeled. Estimate search, embeddings, image or audio generation, paid tools and support separately or include them in other operating costs.
Need help turning the estimate into a scoped feature? Explore our AI integration services for SaaS products.
What goes into an AI assistant budget?
A request can trigger several model calls. Each call may send instructions, conversation history, retrieved documents and tool results. Multiplying only the visible answer length can miss much of the bill.
The calculation
Monthly requests = active users × requests per user. Model calls = requests × calls per request. Token cost = calls × (input tokens × input rate + output tokens × output rate) ÷ 1,000,000. Add the extra-usage allowance and your other monthly operating costs.
Implementation = estimated hours × hourly cost. First-year cost = twelve months at the selected usage level + implementation. Setting implementation hours to zero leaves that budget unestimated.
What still needs scoping?
- Data access, integrations, identity and tenant isolation.
- Interface design, evaluation cases and approval workflows.
- Deployment, monitoring, incident response and ongoing maintenance.
- Embedding, search, image/audio generation and paid tools: include these in other operating costs or estimate separately.
Defaults are illustrative assumptions adapted from our technical cost lab, not measured customer results or live vendor prices. Use your current contract rates. Taxes, discounts and growth over the year are not modeled. Quality and security must be evaluated separately from cost.
Where should you start?
Use the AI feature fit check to choose a workflow before estimating a full rollout. COGNI.BUILD helps design and implement assistants connected to your product’s data and tools.