Scoped, not hourly
We price by the scope of the outcome, not by the hour. There is no meter running and no reason to stretch the timeline, so you agree the number before we start.
Cost calculator
What will your AI project cost?
Custom AI development typically costs $20K to $50K for a first production build, $50K to $100K for a full product, and $5K to $10K per month for ongoing engineering. Answer the four questions below to estimate the cost of your AI project as a range in about 60 seconds.
Start smaller
Not ready for a full build? Start with a proof of concept.
You do not have to commit to a full build on day one. A focused AI proof of concept, in the $10K range, is a real piece of engineering, not a throwaway. Proving an AI approach means working with your actual data, testing accuracy and cost per task, and building the evaluation that shows it works, which is what puts a genuine POC in this range rather than a few days of demo work. We build it the way we build production, so it extends into the full system instead of being thrown away, and it is the lowest-risk way to see what AI can do for your business before a larger build. The first step is a free call.
By project type
What AI projects usually cost.
These are typical market ranges for custom AI development, whoever you hire. What you pay in the end comes down to how the work is scoped and who builds it. That is exactly where a KUMO build is engineered to keep the number down.
| Project type | Typical rangeYour KUMO price is custom, and built to come in lower | What drives it |
|---|---|---|
| AI workflow automation | $20K to $35K | Number of systems connected, approval steps, exception handling |
| AI chatbot or assistant on your data | $20K to $40K | Data readiness, accuracy requirements, channels |
| New AI-powered product | $50K to $100K+ | Full product scope: design, backend, AI layer, infrastructure |
| AI added to an existing platform | $30K to $60K | Age of the codebase, integration depth, migration needs |
| Ongoing engineering after launch | $5K to $10K per month | Team size, response times, roadmap pace |
How a KUMO build keeps the cost down
AI and ML engineers who have shipped this
Our AI and ML engineers have built production models and agents before. The research and evaluation work that inflates a first attempt is already solved, so you reach a working build faster and pay for less of it.
A product manager from day one
A product manager shapes the AI feature set with you before code starts. You build the version that moves your metric first, so scope stays tight and the timeline stays short.
Built to the right size
We ship the smallest thing that works in production, then scale what earns it. You are not paying to over-engineer up front, and you are not paying to rebuild it later.
Your KUMO price is a conversation, not a table.
For the reasons above, your KUMO price is scoped to your project and budget, not read off a chart. We shape it around what matters most first, so it can land differently. Tell us what you're building and we'll give you your real number.
Read the range
What moves the price.
Integrations
The number of systems your AI touches is the single biggest driver. A tool that reads one database and writes to one CRM is straightforward. One that spans a CRM, an ERP, a data warehouse, and a few internal services needs connectors, auth, error handling, and reconciliation for each. Every integration is a place data can be missing, stale, or malformed, so each one adds build time and testing. It is why the calculator asks how many systems you connect to before it gives a range. See our AI workflow automation work for how connected builds are scoped.
Data readiness
AI is only as good as the data behind it. If your data is clean, labelled, and in one place, a build moves fast. If it is spread across spreadsheets, PDFs, and legacy systems in inconsistent formats, the first weeks go into pipelines and validation before any model work starts. A chatbot answering from a well structured knowledge base costs less than one that first has to make sense of years of unstructured documents. The full chatbot cost breakdown goes deeper on this.
Reliability requirements
What breaks if the AI is wrong changes the engineering. An internal tool a small team uses can ship with lighter guardrails. A customer-facing assistant needs human review on sensitive answers, disclosure logic, and a fallback when the model is unsure. A high-volume system that runs thousands of times a day needs monitoring, rate limits, and failover so one bad response or one provider outage does not cascade. Internal, customer-facing, and high volume are three different cost profiles, which is why the calculator asks who uses it.
Scale
A build for a hundred users a month and a build for a hundred thousand are different systems, even when the feature looks the same. Scale brings caching, queueing, cost controls on model calls, and infrastructure that holds up under load. It is cheaper to design for the scale you are heading toward than to re-engineer for it later. Founders building from the ground up can plan this in from day one, which is the point of a founders partnership.
Beyond the build
The costs nobody quotes you.
Most quotes cover building the thing. The costs that surprise teams come after it works in a demo.
Token costs at scale
A model call that costs a fraction of a cent is invisible in testing and material when it runs thousands of times a day. A real quote accounts for usage, caching, and cheaper models for routine steps, so the monthly bill is predictable rather than a surprise.
Evaluation harnesses
Without a way to measure accuracy, you cannot tell whether a change made the AI better or worse. Building the eval set and the tests that run on every change is part of shipping something you can trust, and it is rarely in a cheap quote. See what it costs to build an AI agent for how evals factor in.
Monitoring and drift detection
Models and data both shift over time. An answer that was right at launch can quietly degrade as your data or the underlying model changes. Monitoring that catches drift before your customers do is an ongoing line, not a one-time build.
Provider failover
When the AI runs on an external provider, an outage on their side is an outage on yours unless you planned for it. Failover to a second provider, or a graceful fallback, keeps a customer-facing system up when one model goes down.
Industry research reports that 88% of enterprise AI pilots never reach production, and MIT's Project NANDA found 95% of generative AI pilots deliver no measurable P&L impact. The production layer is usually the difference, and it is part of an honest quote.
We do not just build this for clients. CampaignHQ, our own automation platform, has run in production since 2022, so the token costs, evals, monitoring, and failover above are line items we live with ourselves, not theory. Operating our own product at real scale is why our quotes include the parts that keep software working after launch.
Common questions
AI development cost, answered.
How much does an AI chatbot cost to build?+
A production AI chatbot or assistant on your data typically runs $20K to $50K, landing lower when your knowledge base is clean and the bot serves an internal team, and higher when it is customer-facing, spans several channels, or needs high accuracy on messy data. The calculator above narrows it to a range once you answer how many systems it connects to and who uses it.
How much does an AI agent cost?+
An AI agent that takes actions across your systems usually falls in the $20K to $50K starter band for a focused workflow, and moves toward $50K to $100K as it connects to more systems, handles exceptions, and runs at higher volume. The main drivers are the number of integrations and how reliable it has to be, both of which the calculator asks about.
Why do AI development quotes vary so much?+
Because the same feature can be a different system underneath. Two chatbots can look identical while one reads a clean database and the other makes sense of years of unstructured documents. Integrations, data readiness, reliability requirements, and scale each move the number, which is why an honest quote is a range with drivers rather than a single figure.
What does a $20K starter build actually include?+
A Starter Build, in the $20K to $50K band, is a first production build shipped end to end: scope and discovery, the core AI workflow, integration with your existing systems, evaluation and human review built in, and deployment with monitoring. It is a working system in production, not a demo, with the source code and documentation handed to your team.
What does AI cost per month after launch?+
Ongoing engineering after launch runs $5K to $10K per month. That covers a senior team on retainer for new work, evaluation as your data changes, monitoring and drift detection, and provider failover so a customer-facing system stays up. Month-to-month cost also includes model usage, which is why the sections below cover controlling token costs at scale.
Want the number for your exact project?
Tell us what you're building and we'll follow up with a clear scope and a quote inside your range.