How much do managed AI services cost?
Our answer is $6,000/month and up. Here is what moves that number, what the other pricing shapes on the market look like, and how to compare quotes line by line.
Managed AI services cost anywhere from a few hundred dollars a month for a packaged AI tool to several thousand a month for a provider that builds and runs workflows inside your own systems. Ours is $6,000/month: one monthly fee that covers finding the workflow, building it into the systems you already run, and hosting, monitoring, updating, and supporting it afterward. There is no paid discovery project, the first workflow is live within 30 days, and you can opt out at day 90.
That is the short answer. The rest of this post explains what moves managed AI pricing up or down, what the other pricing shapes on the market look like, what tends to be left out of a quote, and how to compare two quotes that do not look alike. If you want the definition of the service model first, read What is managed AI operations?.
What drives the price of a managed AI service?
Four things move the number. Any honest quote should tell you which of them it assumes.
Number of workflows
A workflow is one job the AI does end to end: reading inbound contracts and flagging risky clauses, pulling invoice data into the ERP, answering staff questions from the SharePoint library. Each one has to be scoped, built, tested against real documents, and then watched. More workflows means more to build and more to operate. Our $6,000/month starting point covers one production workflow and the service around it. New workflows get added as results justify them, typically one a quarter, and the proposal states the new monthly fee before anything changes.
Number of systems
A workflow that lives inside Microsoft 365 is simpler than one that reads from SharePoint, writes to a CRM, and posts a status update in Teams. Every connected system adds authentication, a permission model to respect, and an integration that can break when the vendor ships an update. That upkeep is real work, and it shows up in the fee.
Expected model usage
Model usage is billed by the provider (OpenAI, Anthropic, Microsoft, Google) by volume. A workflow that summarizes ten documents a day costs less to run than one that reads every inbound email. We include expected model usage in the quote and pick the model that fits each job. If usage changes materially, we discuss it before it changes your bill. Some providers pass usage through at cost on a separate line, which is fine, as long as you know that before you sign.
Support hours and response expectations
Business-hours support costs less to deliver than 24/7 on-call. We offer business-hours support for the people using what we deploy, and we say so plainly. If a vendor promises round-the-clock coverage, ask who is actually awake at 3 a.m. and what it costs.
What pricing models are on the market?
Managed AI pricing comes in roughly five shapes. They are not really competing for the same job, which is why the sticker prices are so far apart.
Per-seat AI platforms
ChatGPT, Claude, and Microsoft Copilot business plans are priced per user per month. Seats are useful: people draft faster and summarize meetings. A seat does not connect to your contract folder with the right permissions, does not watch a workflow for silent failures, and does not come with someone to call when the output is wrong. Seats are a tool budget, not an operations budget. We keep them in place: your existing seats stay on your own subscription, and we can connect your systems to the assistant your team already uses.
Packaged "AI employee" services
A newer category sells a pre-built AI worker for a flat monthly price. AIQ Labs, for example, announced a Managed AI Employee service in December 2025 starting at $599 per month, according to its press release. These packages make sense for well-defined front-office roles. The question to ask is how much of the work is configuring a template versus building against your own systems and permissions.
Project fee plus retainer
The traditional consulting shape: a fixed-fee build, then an hourly or monthly retainer for changes. The build is usually the large number. The retainer is often small because it covers fixes on request, not ongoing monitoring. This works if you have engineers who will own the system after handover. Most 30–500 person firms do not.
Platform operations with onboarding
Some managed services run the AI platform rather than the business workflow. Claranet's UK government G-Cloud listing for its Managed AI & MLOps Service, for example, prices a one-off onboarding assessment at £13,000 and monitoring and alerting at £980 a month, with per-model and per-developer charges on top. That is built for organizations that already have data scientists shipping models and want the platform kept healthy.
Flat monthly managed service
This is the shape we sell, and the one the managed IT world already uses: one monthly fee that covers building the workflow and running it afterward. No large up-front project. The fee is higher than a seat and lower than a hire, because you are paying for a team that both builds and operates.
What is usually left out of a quote?
The cheapest-looking quote is often the one that leaves the most out. Check these line by line:
- Model usage. Is it included, capped, or passed through at cost?
- Hosting. Who pays for the servers, the vector store, and the logs, and under whose cloud account?
- Monitoring. Is anyone watching the workflow itself, or only whether the server is up?
- Model and security updates. When a provider retires a model version, who tests the replacement on your documents?
- Integration maintenance. When Microsoft or Google changes a permission setting, who fixes the connector?
- Support. Hours, response targets, and who answers.
- Reporting. Does anyone tell you what the workflow did for the business each month?
- Exit terms. Minimum term, notice period, and whether a contract renews automatically.
For transparency, here is what our fee does not include: general IT help desk, devices, and networking (that stays with your IT provider, which is why we partner with MSPs instead of competing with them), your existing ChatGPT, Claude, or Copilot seats, and 24/7 on-call.
What do we charge, and why publish it?
West Palm AI's managed AI service starts at $6,000/month. Here is what that buys, as it appears on our pricing page:
- AI workflows built around Microsoft 365, Google Workspace, SharePoint, Teams, and your line-of-business systems.
- Connectors that bring your company data into the ChatGPT, Claude, or Copilot your team already uses, respecting existing permissions.
- Hosting, monitoring, model and security updates, and integration maintenance.
- Usage and cost oversight, with expected model usage included in the quote.
- Business-hours support.
- A monthly value report and a quarterly review of results.
The terms: you start directly on the monthly plan. There is no paid discovery project. The first workflow goes live within 30 days of signing. The first 90 days are the minimum; if the service is not earning its fee by then, you can leave. At day 90 we offer an annual agreement with an incentive, such as a rate lock or an added workflow. If you decline, month-to-month continues at the standard rate. Nothing renews into an annual term automatically.
We publish the number because the buyers we work with, profitable companies of 30–500 people, already buy managed IT, outsourced accounting, or a fractional CFO on a monthly fee. They budget that way. A hidden price wastes a call for both sides when the number was never going to fit. If $6,000/month is out of range, it is better you know before we talk.
How does the cost compare with hiring?
At $6,000/month, a year of the managed service starts at $72,000. One AI engineer, fully loaded, is a six-figure decision before tools, recruiting fees, or model bills, with a 6–12 month ramp before the first durable system is in production. And one person has to cover building, hosting, monitoring, and support, including the weeks they are on vacation.
That does not make hiring wrong. Once you have several production workflows and a leader who can recruit for them, an in-house team can be the right long-term shape. The full comparison is in Managed AI vs hiring an AI team.
How do you compare managed AI quotes?
Put every quote on the same sheet and ask the same questions:
- What is the first workflow, and when is it live? A quote without a named workflow and a date is a discovery engagement in disguise.
- What is the all-in monthly number at month six? Include usage, hosting, and any per-seat or per-model charges.
- What does it cost to get started? Onboarding fees, paid discovery, or a fixed build project.
- Who operates it after launch? Name the people, not the platform.
- What happens when a model changes? You want a tested process, not "we'll look into it."
- How is value reported? A monthly number against a baseline you agreed on before launch.
- How do you leave? Minimum term, notice, data handover, and whether anything renews on its own.
If a provider cannot answer those in writing, the price is not really a price yet.
Where to start
The first step is a free AI opportunity assessment: a working session on where your team's time goes and which workflows are most worth automating. If there is a strong fit, you get a proposal with the monthly fee, the first workflow, and how we will measure it.
Book a workflow call to start. If you would rather write first, the work-with-us page is the inquiry form. West Palm AI is based in West Palm Beach, Florida, and works with companies across the US; the about page covers who you would actually be working with.