Who can use this feature

accounts that have monday's AI capabilities enabled

AI credits are the single unit of measure across all monday AI capabilities, giving your team one simple, consistent way to use and manage AI across the platform. Every AI action deducts credits from your account's quota, and the amount deducted depends on the type of task performed and, in some features, the AI model you choose to run it.

This article explains how different AI models affect credit consumption, and how to optimize your usage to get the most out of your credit allocation.

How AI credits work

AI credits apply across all monday AI features under a single unified consumption model. Every AI action deducts credits from your account quota. The amount deducted depends on two factors:

• The complexity and depth of the task performed
• The AI model chosen to perform it, where model selection is available

Credit use by feature

Why different AI models cost different credits

Each AI model has a unique position on the spectrum between speed and intelligence. A more capable model does more reasoning, executes more steps, and produces higher-quality output, all of which requires more credits. Choosing the right model for the task is one of the most effective ways to manage your consumption.

Available models and credit consumption in monday vibe

Both Chat (Discuss) and Build modes consume credits. Chat mode consumes fewer credits than Build mode because the model does not write or execute code. Credits are charged per message in both modes.

Vibe app examples and credit estimates

The credit consumption for building a vibe app depends on its complexity, from a quick single-view dashboard to a full high-complexity app. Here's an estimation of credit consumption for different use cases, based on real usage data from thousands of published apps.

Simple apps

Example: "OKR Progress Tracker"

A team lead built a dashboard to track quarterly goals and progress at a glance. Two prompts created progress bars and status indicators pulled from an existing board. Published and shared in minutes.

• Prompts used: 2–5
• Credits consumed: ~140
• Model used: Claude Sonnet
• Best for: Single-view dashboards, board summaries, status overviews, simple trackers
• Models used: Sonnet + Gemini Flash

Moderate apps

Example: "Leave Tracker"

An HR manager built an app to show remaining PTO, display upcoming team absences on a calendar, and flag scheduling conflicts. The core structure was ready quickly, then refined with department filters, manager views, and color-coded warnings over a dozen iterations.

• Prompts used: 6–15
• Credits consumed: ~220
• Model used: Claude Sonnet
• Best for: Multi-view apps with filters, role-based access, custom formatting, or data from multiple boards
• Models used: Sonnet + Gemini Flash

Advanced apps

Example: "Interview Feedback Portal"

A talent acquisition team built a structured hiring workflow where interviewers submit scored feedback, recruiters see consolidated candidate summaries, and hiring managers get hire/no-hire recommendations with supporting data. Building the forms, scoring logic, and role-specific views took ~36 prompts.

• Prompts used: 16–36
• Credits consumed: ~171
• Model used: Claude Sonnet
• Best for: Multi-feature apps with business logic, multiple user roles, calculated fields, and polished UX
• Models used: Sonnet + Gemini Flash

High complexity apps

Example: "Venue Directory"

A mid-market events company built a comprehensive venue management system with a searchable directory, capacity details, availability calendars, pricing tiers, vendor contacts, and campaign tracking across dozens of locations. Over ~95 prompts, they built filtered search, comparison views, booking status indicators, and reporting dashboards for daily team use.

• Prompts used: 37+ (typically 55–160)
• Credits consumed: ~3,000
• Models used: Claude Sonnet
• Best for: Full operational systems, multi-department workflows, apps replacing standalone SaaS products or custom development
• Models used: Sonnet + Gemini Flash

How to optimize credit usage in monday vibe

Choose the right model for the task

Selecting the right model before you start a task is the fastest way to reduce unnecessary credit spend in monday vibe.

• Simple edits such as changing a colour or tweaking text: use Gemini Flash (~10–20 credits)
• Most builds and everyday edits: use Claude Sonnet (~30–50 credits, also the default in Auto-Select)
• Building a complex multi-page app: use Claude Opus for that task, then switch back to Sonnet
• Prefer not to manage model selection: leave it on Auto-Select

Use Chat mode strategically

Chat mode costs fewer credits per message than Draft mode, since no code is written. Use it to plan, get debugging guidance, or consult before switching to Draft to implement changes.

• Switch to Chat mode to plan or troubleshoot before making changes
• Use Sonnet or Flash in Chat mode
• Switch back to Draft mode only when you are ready to build

Write precise prompts

Vague prompts force the model to guess, leading to reruns. Specific prompts are more likely to produce the right result the first time.

• Weak: "The text is not right"
• Strong: "On the Projects tab, in the timeline card, change the header font from 16px to 14px"

Use UI terms when describing what to change, such as Z-axis for layering, out-of-bounds, tab name, and element name. Specify the source and target clearly, for example "Update status from Stuck to Done" rather than "Fix the status." Include screenshots when describing visual bugs, especially when using Opus.

Credit consumption in monday agents

Credit consumption depends on the complexity and depth of each task your agent performs. The estimates below are directional; exact costs vary based on the specific run, the model selected, and the instructions and skills provided to the agent.

How to optimize credit usage in monday agents

• Give granular instructions rather than letting the agent decide. For example, under Tools, specify the exact Slack channel and define the exact action. Leaving decisions to the agent increases credit consumption.
• Avoid triggering web searches unless they are necessary for the task.
• Review the Activity tab after runs to understand what was executed and how credits were used, then refine your instructions accordingly.
• Use the Usage Limits tab in the admin section under AI Governance to allocate credits per use case and per user.

Agent examples in action

The Recruitment Screener

Processes 50 candidate CVs per day, scoring each applicant against job criteria, building a shortlist, updating status to 'Reviewed' on your board, and notifying the recruiter.

• Credits consumed: ~12,500 per month
• Model used: Claude Sonnet
• Best for: High-volume candidate screening with structured evaluation criteria

The Customer Request Router

Monitors your incoming support board, reviews every new request, assigns it to the correct team based on issue type, and creates an internal update for the team.

• Credits consumed: ~5,400 per month (based on 15 runs per day)
• Model used: Claude Sonnet
• Best for: Automated triage and routing of incoming requests

The Deep Research & Synthesis Agent

Performs deep, multi-source research on 10 complex topics per week, synthesizes findings into a structured monday doc, and updates your project board with key takeaways.

• Credits consumed: ~5,160 per month
• Model used: Claude Sonnet or Claude Opus
• Best for: In-depth research requiring web searches and synthesis

The Sprint Reviewer

Every Friday, reads your entire development board, analyzes which tasks were completed versus carried over, writes a structured retrospective update for the team, and notifies stakeholders.

• Credits consumed: ~63 per run
• Model used: Claude Sonnet
• Best for: Weekly sprint retrospectives and team reporting

Credit consumption in monday sidekick

Sidekick works interactively. Each message you send consumes credits, and a single conversation can include a mix of simple and complex tasks. Consumption depends on the depth of the request, how much board context is read, and which tools are used, such as search, updates, charts, or asset generation.

Sidekick examples in action

Here's how these estimates translate into real-world use, based on task complexity multiplied by how often the task runs for example, a daily task multiplied by the number of days in a month.

Daily board status checks

Scans your sprint board each morning, lists what's overdue, and summarizes what changed.

• Credits potential spend: ~770 per month (22 workdays)
• Best for: Recurring daily status checks on a single board

Ticket triage and routing

Reads incoming tickets upon request, briefs agents on context, and suggests next steps.

• Credits potential spend: ~19,800 per month (15 tickets per day)
• Best for: High-volume ticket triage and context gathering

Building charts and reports

Builds charts and views from your live board data.

• Credits potential spend: ~660 per month (10 reports)
• Best for: Recurring visual reporting from board data

Bulk board updates

Processes large batch updates, such as reassigning owners, normalizing statuses, or importing structured data.

• Credits potential spend: ~1,256 per month (4 batch jobs); heavy one-off rebuilds can run significantly higher
• Best for: Large-scale batch operations and board rebuilds

Governance and monitoring

Admins can use the AI governance tools inside the administration section to track and monitor AI credit usage across the account. This includes credit usage per feature and per user, role-based permissions, and agent activity monitoring.

On the Enterprise plan, admins can also use the AI permissions tab to manage more detailed access to AI features, including which account roles can use them.

FAQs

Which monday AI features let me choose an AI model?

monday vibe allows you to select an AI model. Other features currently use a fixed model.

Why does Claude Opus consume more credits than Claude Sonnet?

Claude Opus performs more reasoning steps and deeper analysis per message, which requires significantly more processing. Use it intentionally for complex tasks, then switch back to Sonnet for everyday work.

How can I optimize credit consumption in monday agents?

Give granular instructions under Tools rather than letting the agent decide, avoid triggering web searches unless necessary, review the Activity tab after runs to refine your instructions, and use the Usage Limits tab under AI Governance to allocate credits per use case and per user.

How can I optimize credit consumption in monday vibe?

Write precise, specific prompts to avoid re-runs, choose Gemini Flash for simple edits, use Chat mode with Sonnet or Flash for planning before switching to Draft, and reserve Claude Opus for complex tasks only.