UK

United Kingdom · Technology and data · Principal/Manager (12-16 years)

Principal AI Strategist / AI Solutions Manager

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

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  • Experience bandPrincipal/Manager (12-16 years)
  • Direct reports10-25 reports
  • Reports toDirector, AI Solutions
  • UK framework levelUsually someone running a function, or a director

Also advertised as Head of AI Solutions · AI Portfolio Lead · Senior AI Programme Manager

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 3 of 6 standards
Level 6 Award in Data-led Decision Making Institute of Sales Professionals · Level 6 · covers 1 of 6 standards
ATHE Level 7 Extended Diploma in Computing ATHE Ltd · Level 7 · covers 1 of 6 standards
Level 6 Certificate in Professional Sales Institute of Sales Professionals · Level 6 · covers 1 of 6 standards
Level 6 Diploma in Professional Sales Institute of Sales Professionals · Level 6 · covers 1 of 6 standards
TQUK Level 6 Diploma in Digital Leadership (RQF) Training Qualifications UK Ltd · Level 6 · covers 1 of 6 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Data Science Foundations OTHM Level 7 Diploma in Data Science
Data-led Decision Making Level 6 Award in Data-led Decision Making
Applications of Machine Learning and Artificial Intelligence ATHE Level 7 Extended Diploma in Computing
Data science TQUK Level 6 Diploma in Digital Leadership (RQF)

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Change Fluency taking people through change
Systems Fluency seeing the whole picture
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Your Plan Illustration

Built for Principal AI Strategist / AI Solutions Manager

4 units that map to this job, from the qualifications that cover it.

  1. Data Science Foundations OTHM Qualifications · covers 3 of 6 standards Level 7
  2. Data-led Decision Making Institute of Sales Professionals · covers 1 of 6 standards Level 6
  3. Applications of Machine Learning and Artificial Intelligence ATHE Ltd · covers 1 of 6 standards Level 7
  4. Data science Training Qualifications UK Ltd · covers 1 of 6 standards Level 6
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Principal AI Strategist / AI Solutions Manager?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • AI Governance & Regulatory Foresight
  • AI-Driven Organisational Change Management
Why each one is rising

The future skills this job leans on

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your Principal AI Strategist / AI Solutions Manager plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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3What this role really is

This job means setting the vision and direction for AI work across a business, and making sure it solves real problems. It involves running AI efforts like a business within the business, with responsibility for direction and results.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Cloud AI Platforms (AWS SageMaker, Azure ML, GCP Vertex AI) Strategic/Architect

You won't be deploying models, but you'll be making platform selection decisions (e.g., build vs. buy, single vs. multi-cloud), managing budgets for these platforms, overseeing security posture, and ensuring integration with enterprise systems. You're setting the direction.

MLOps & Data Tools (MLflow, Snowflake, Kubeflow, Databricks) Strategic/Architect

You'll be setting the enterprise MLOps strategy for your domain, evaluating and selecting new platforms (e.g., Domino Data Lab, Seldon), and owning the relationship with data platform vendors. You need to understand the capabilities and limitations to guide your team effectively.

Programming (Python, TensorFlow, PyTorch) Advanced (Conceptual)

You won't be coding daily, but you'll perform rigorous code reviews for architectural soundness, setting coding standards and best practices for the entire AI/ML organisation. You need to understand the technical feasibility and implications of different approaches.

Visualization Tools (Tableau, Power BI, Domo) Advanced

You'll define the BI and visualization strategy for AI initiatives. You'll present insights from these dashboards in executive and board meetings, often using advanced tools like Domo or Diligent Boards for formal reporting. You need to tell the story with data.

Project Management & Planning (Jira, Confluence, Anaplan) Expert

You'll manage a portfolio of projects, integrating Jira data with financial planning tools like Anaplan or Workday Adaptive Planning to track ROI and resource allocation across your entire AI function. This is about strategic portfolio management.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
AI Solution Roadmap & Prioritisation Proposes minor task adjustments to project lead. Proposes solutions for specific project components, seeks approval. Recommends project priorities and technical approaches for a workstream to Director.
Budget Allocation (AI Initiatives) No budget authority. Reports expenses. Suggests tool purchases up to £1K, needs approval. Recommends project-specific spending up to £5K, needs Director approval.
Team Hiring & Structure Provides feedback on candidates for junior roles. Participates in interviews for peer roles, gives hiring recommendations. Leads interviews for junior/mid-level roles, makes hiring recommendations to Director.
Technical Architecture & Platform Selection Follows established technical guidelines. Chooses appropriate tools/libraries within approved tech stack for tasks. Designs end-to-end AI system architectures, recommends platform choices for projects.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

AI Portfolio ROI
The total financial return (revenue lift, cost savings, risk reduction) delivered by the AI solutions under your remit, compared to their investment.
Target · Achieve a minimum of £1.5M in documented business value annually, with a 3x ROI on average per major initiative.

Your team's new predictive maintenance model reduced equipment downtime by 15%, saving £750K, and a customer churn prediction system increased retention by 2%, adding £1.2M in revenue. Total value: £1.95M.

Time-to-Market for AI Solutions
The average time it takes from initial concept approval to a fully deployed, value-generating AI solution in production.
Target · Reduce average time-to-market by 25% year-over-year, aiming for 6-9 months for major initiatives.

Last year, a typical AI solution took 12 months. This year, you've streamlined processes and reduced it to 9 months, hitting your target.

Team Health & Retention
The overall well-being, engagement, and retention rate of your direct and indirect reports.
Target · Maintain voluntary attrition below 10% annually, with an average engagement score of 80%+ in internal surveys.

Your team's Q2 engagement score was 85%, and only one person left voluntarily in the last 12 months, well within the target.

AI Solution Adoption Rate
The percentage of target users or business processes that actively use or are impacted by your deployed AI solutions.
Target · Achieve 80%+ adoption for critical AI solutions within 3 months of full deployment.

The new AI-powered sales lead scoring system was adopted by 92% of the sales team within the first month, leading to a measurable increase in conversion rates.

Strategic Influence & Alignment
How effectively you shape the broader company strategy by integrating AI into key business decisions and securing buy-in from senior leadership.
  • You're regularly invited to executive strategy sessions
  • your proposals for AI investment are consistently funded
  • other departments proactively seek your input on their strategic roadmaps
  • you're seen as a trusted advisor on AI for the SVP and C-suite.
Organisational Capability Building
Your ability to build a robust, scalable, and future-proof AI function, including talent development, process standardisation, and technology adoption.
  • Clear career pathways exist for your team
  • you've successfully mentored and promoted high-potential individuals
  • we have well-defined MLOps standards
  • your team is adopting new, relevant technologies efficiently
  • you've established effective knowledge-sharing mechanisms.
AI Governance & Ethical Leadership
Establishing and upholding strong governance frameworks for responsible AI development and deployment, ensuring ethical considerations are baked into every solution.
  • You've implemented clear processes for model explainability and fairness audits
  • your team consistently adheres to data privacy regulations (e.g., GDPR)
  • you've successfully navigated complex ethical dilemmas in AI projects
  • you're seen as a thought leader internally on responsible AI.
External Reputation & Thought Leadership
How you represent the organisation externally, contributing to our reputation as an AI leader and attracting top talent.
  • You're speaking at industry conferences
  • you're publishing articles or white papers
  • you're actively engaging with industry bodies
  • your network helps us attract high-calibre candidates to the team.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Building Transformative Impact

You'll be working on initiatives that can genuinely change how the business operates, from optimising core processes to creating entirely new customer experiences. This isn't about incremental gains; it's about significant shifts.

Seeing a new AI-powered product feature, which you championed, launch and generate £5M in new revenue within its first quarter.

Leading & Developing Talent

You'll be directly responsible for mentoring, coaching, and growing a team of highly skilled AI professionals. This means helping them navigate technical challenges, develop their careers, and become future leaders themselves.

One of your Lead Architects gets promoted to a managerial role, directly attributing their growth to your guidance and support.

Strategic Problem Solving

You'll be tackling complex, ambiguous business problems where AI is a potential solution, often with no clear path forward. This involves a lot of strategic thinking, trade-off analysis, and innovative solution design.

You're tasked with figuring out how AI can help us enter a new market segment, requiring you to design a multi-year strategy from scratch.

What frustrates people
  • The 'POC Purgatory' problem: Fighting for engineering resources to productise a model that performed brilliantly in a notebook but requires massive infrastructure work to run at scale. It's frustrating when great ideas don't get the runway they need.
  • Managing Hype vs. Reality: Constantly re-setting executive expectations fueled by sensationalist headlines. You'll spend time explaining why you can't just 'point a GPT at our data' to solve a complex business problem in a week.
  • Moving Goalposts: Stakeholders changing the definition of success or the target metric for a model halfway through development, which can invalidate months of work and require significant re-scoping.
  • Budgeting for Uncertainty: Justifying a multi-million pound budget for a project where the outcome is, by nature, experimental and not guaranteed, unlike a traditional IT project with a fixed scope. It takes a thick skin and solid business case skills.
  • The Last Mile Problem: Owning the success of an AI solution when its ultimate failure or success depends heavily on a business process change or a user interface that you don't directly control. It means you're accountable for things outside your direct remit.
What this role does not give you
  • Daily, hands-on coding: While you'll review code and understand technical depth, your day-to-day isn't about writing production-grade Python scripts.
  • Complete control over all variables: You'll be influencing, not dictating, many aspects of data, product, and engineering. It's a leadership role, not a dictatorship.
  • Predictable, routine work: Every quarter brings new strategic challenges, new technologies, and new organisational dynamics. If you like routine, this isn't it.

8Who you work with

You'll directly shape the organisation's AI capabilities and strategy for a significant portion of the business. Your decisions will influence multi-million pound investments, directly impacting our P&L, market position, and ability to compete. It's about building a competitive advantage through intelligent systems.

Inside the business
  • SVP of Product & Engineering
  • CFO and Finance Leadership
  • Heads of Business Units (e.g., Sales, Marketing, Operations)
  • Legal & Compliance teams
  • Executive peers across departments
Outside the business
  • Key technology vendors (e.g., cloud providers, MLOps platforms)
  • Industry bodies and research institutions
  • Potential M&A targets (for strategic AI capabilities)
  • Consultancy partners

9What you need before you start

Not a wish list. The things you would be expected to already have.

  • Proven experience (typically 8-12 years) as a Lead AI Solutions Architect or Senior Data Science Manager, where you were responsible for designing and delivering complex AI systems end-to-end.
  • Demonstrable experience leading and mentoring a team of at least 5-8 technical professionals, including performance management and career development.
  • A strong track record of translating business problems into AI solutions that delivered measurable commercial value, including building and presenting robust business cases.
  • Deep understanding of MLOps principles and practices, having successfully built and managed production-grade machine learning pipelines.
  • Excellent communication and influencing skills, with a proven ability to engage and align senior stakeholders across an organisation.

10What to practise next

Where the job is going, and what to do about it starting this week.

Generative AI & Foundation Models (Strategic Application)

Generative AI is transforming how we build products and automate tasks. You'll need to understand how to strategically apply these models across your portfolio, identify new use cases, and manage the associated risks (e.g., hallucination, data leakage).

Prompt Engineering for Business Outcomes · Fine-tuning vs. RAG (Retrieval Augmented Generatio · Evaluating Generative Model Outputs (accuracy, bia · Cost Optimisation for LLM Inference & Training · Ethical Implications of Synthetic Content

  • This quarter: Attend a workshop on enterprise applications of Generative AI.
  • Next 6 months: Identify 2-3 strategic use cases for Generative AI within your domain and build initial business cases.
  • Next 12 months: Oversee the successful pilot of a Generative AI solution, focusing on business value and risk mitigation.
  • Ongoing: Encourage your team to experiment with Generative AI tools for internal productivity and share learnings.

Quick win: Challenge your team to identify one internal process that could be 10x faster with Generative AI. Even if it's just drafting emails or summarising meeting notes, start experimenting.

AI-Powered Decision Intelligence

Beyond just predictions, AI is moving towards providing actionable insights and automating complex decision-making. You'll need to lead the charge in building systems that not only tell us what might happen but also recommend the best course of action and even execute it.

Reinforcement Learning for Business Optimisation · Causal Inference & Counterfactual Analysis · Multi-Agent AI Systems for Complex Scenarios · Human-in-the-Loop Decision Augmentation · Measuring Decision Quality & Impact

  • This quarter: Read up on decision intelligence frameworks and case studies.
  • Next 6 months: Identify a key business decision process that could be improved with AI-powered recommendations.
  • Next 12 months: Design and oversee the development of a decision intelligence prototype for that process.
  • Ongoing: Work closely with business leaders to understand their most critical decision points.

Quick win: For your next strategic decision, try to formalise the decision criteria and potential outcomes. Even a simple decision matrix can highlight where AI could eventually help.

11Staying current once you are in

What people here do to keep up
  • Regularly attend and present at leading AI/ML conferences (e.g., NeurIPS, KDD, Re-Work).
  • Participate in industry forums and working groups focused on AI ethics, governance, or specific domain applications.
  • Enroll in executive education programmes on AI strategy, digital transformation, or organisational leadership.
  • Actively mentor junior and mid-level AI professionals, both within and outside the organisation.
  • Contribute to open-source AI projects or publish thought leadership pieces on relevant topics.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

AI Governance & Regulatory Foresight

With the EU AI Act and similar regulations globally, the legal and ethical landscape for AI is rapidly maturing. Leaders won't just need to comply; they'll need to anticipate future regulations and proactively build compliant, responsible AI systems. Getting this wrong could mean huge fines or reputational damage.

  • Proactive Regulatory Scanning & Impact Assessment
  • Establishing AI Ethics Boards/Committees
  • Automated Compliance Monitoring (e.g., for bias de
  • Explainability by Design & Audit Trails
  • Data Sovereignty & Cross-Border AI Deployment

AI-Driven Organisational Change Management

Deploying AI isn't just a tech project; it's a change project. As AI becomes more pervasive, leaders will need to be experts in guiding the organisation through significant shifts in processes, roles, and culture. Without this, even the best AI solutions will fail to deliver their full value due to human resistance.

  • Stakeholder Mapping & Influence Strategies
  • Communication Planning for AI Adoption
  • Reskilling & Upskilling Workforces for AI Collabor
  • Building Trust in AI Systems (Human-in-the-Loop de
  • Measuring Adoption & Behavioural Change

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • AI Solution Architecture
  • Business Case & ROI Modeling
  • ML System Design
  • Agile for ML
  • AI Ethics & Governance
  • Stakeholder Translation

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    From Lead AI Solutions Architect (L4)

    2-4 years at L4

    Skills to master

    • Moving from designing individual systems to managing a portfolio of solutions, developing strong business case modelling, and taking on direct people leadership responsibilities (mentoring to managing).

    You're ready to move on when

    • Successfully architected and oversaw the deployment of 3+ complex, high-impact AI systems.
    • Consistently mentored 2-3 junior/mid-level team members, showing strong coaching abilities.
    • Demonstrated ability to influence product roadmaps and secure cross-functional buy-in for technical decisions.
    • Proactively identified and mitigated technical risks for large-scale projects.
  2. 2

    From Senior Data Science Manager (external)

    3-5 years in a similar managerial role

    Skills to master

    • Adapting to our specific technical stack and organisational culture, understanding our unique business domain, and quickly building credibility with senior stakeholders.

    You're ready to move on when

    • Managed a team of 10+ data scientists/ML engineers, with a proven track record of team development.
    • Owned the delivery of a portfolio of data science projects with clear business impact.
    • Experience managing budgets and resource allocation for a data science function.
    • Strong external network and ability to attract top talent.
  3. 3

    From Head of Product (AI Focus)

    4-6 years in product leadership with a strong AI component

    Skills to master

    • Deepening technical understanding of AI architecture and MLOps, shifting from product ownership to solution ownership, and leading a purely technical team.

    You're ready to move on when

    • Successfully launched 2+ AI-powered products that achieved market traction.
    • Demonstrated strong collaboration with engineering and data science teams.
    • Clear understanding of the AI development lifecycle and common challenges.
    • Ability to translate market needs into technical requirements for AI solutions.

15Where this role leads

The long view: This work can lead to setting AI strategy at board level, or into a wider technology leadership role such as chief digital officer.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Principal AI Strategist / AI Solutions Manager is actually changing.

Where it leads

Roles this leads to

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Data Science Foundations, worked against Principal AI Strategist / AI Solutions Manager.

Data Science Foundations Level 7

Applied to your work in Principal AI Strategist / AI Solutions Manager

1. To enable the learner to define the scope and landscape of Data Science and differentiate the roles of Data Scientists from other IT professionals. 2. To enable the learner to evaluate key topics within Data Science, including data administration, governance, and big data sources. 3. To enable the learner to describe the architecture and core elements of Apache Hadoop. 4. To enable the learner to analyse the advantages and disadvantages of utilising Artificial Intelligence techniques in a business context. 5. To enable the learner to critically analyse the impact of Big Data on digital transformation within organisations and its effects on users. 6. To enable the learner to review strategies for ensuring data compliance and explain the responsibilities and challenges faced by data specialists.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

Start my plan, free
See Your Progress Grow Illustration
Principal AI Strategist / AI Solutions Manager
  • AI Solution Architecture
  • Business Case & ROI Modeling
  • ML System Design
  • Agile for ML
  • AI Ethics & Governance
  • Stakeholder Translation
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Principal AI Strategist / AI Solutions Manager is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Director, AI Solutions (L6)

    3-5 years in this Principal role

    This is the natural next step, moving from managing a department's AI portfolio to shaping the multi-year AI strategy for an entire business unit, with significantly larger P&L accountability and board-level visibility.

    • Defining enterprise-wide AI platform strategy
    • Leading large-scale organisational transformation through AI
    • Navigating complex regulatory and geopolitical landscapes for AI
    • Driving multi-million pound P&L for a business unit
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, as a Principal AI Strategist, your time is precious. You're juggling strategic planning, team leadership, stakeholder management, and a dozen other things. The good news? AI isn't just for our products; it's a powerful co-pilot for your own productivity. We're investing in tools to help you automate the mundane, so you can focus on what truly matters: driving our AI vision forward.

Imagine cutting down on routine admin, speeding up your research, and making your communications more impactful, all with a little help from AI. This isn't about replacing your judgment; it's about augmenting it, giving you more bandwidth to lead, innovate, and strategise.

Automated Status Reporting

Use an LLM agent connected to Jira, Confluence, and Slack to automatically generate weekly project status reports, risk assessments, and stakeholder updates. It'll pull the key info, summarise progress, and flag potential issues, all in a consistent, executive-ready format. No more chasing updates or spending hours compiling reports.

Predictive Project Analysis

Apply ML models to historical project data—think Jira tickets, code commits, budget reports—to predict the likelihood of timeline slips or budget overruns for your entire portfolio. This lets you proactively intervene, reallocate resources, or adjust expectations before things go off track, saving you from reactive fire-fighting.

Accelerated Research & Vendor Analysis

Use a private LLM, trained on industry research papers, vendor documentation, and internal knowledge bases, to rapidly summarise new AI techniques, compare MLOps platforms, or draft initial solution architecture documents. Get up to speed on complex topics in minutes, not hours, and make more informed strategic decisions faster.

Stakeholder Comms Co-Pilot

Use an AI writing assistant to translate dense, technical project documentation or complex model performance reports into clear, concise executive summaries, compelling board-level presentations, or business-friendly FAQs. Craft impactful messages that resonate with non-technical audiences, saving you significant time on drafting and refining.

Common questions

Common questions

How do you become a Principal AI Strategist / AI Solutions Manager?

Common routes in include From Lead AI Solutions Architect (2-4 years at L4), From Senior Data Science Manager (external) (3-5 years in a similar managerial role) and From Head of Product (AI Focus) (4-6 years in product leadership with a strong AI component). Times vary with prior experience.

Where can a Principal AI Strategist / AI Solutions Manager progress to?

This role can lead on to Director, AI Solutions (3-5 years in this Principal role), depending on the skills you build.

What new skills matter most for a Principal AI Strategist / AI Solutions Manager?

Increasingly, AI Governance & Regulatory Foresight and AI-Driven Organisational Change Management. These are the skills our record of this role lists as growing.

Free tools

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17Where to go from here

Other roles at Level 6

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in technology and data

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills built here, strategic AI leadership, managing large teams, building business cases, and working across functions, transfer across most industries. Finance, healthcare, retail and manufacturing all need people who can lead AI work.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my plan, free What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.