Airtable in 2025: AI Features, Limitations, and When to Use It

Airtable has spent the last two years racing to add AI features to a product that was originally built as a spreadsheet-database hybrid. The result is a platform with genuinely useful AI capabilities buried inside a product that still has significant limitations at scale.

Here’s a clear-eyed look at where Airtable’s AI features stand and when they’re worth using.

What Airtable’s AI features actually do

AI Fields are computed columns that run a prompt against your row data and populate a result. Useful examples: summarizing a long text field, categorizing customer feedback, extracting structured data from unstructured notes, translating content.

The implementation is straightforward: you define a prompt template using field references (e.g., “Categorize this support ticket: {ticket_text}”), and Airtable calls an AI model and stores the result. AI fields regenerate when source data changes — or on demand.

AI Automations let you use AI as a step in an automation flow. You can prompt an AI model with data from a record and then use the output to update fields, send messages, or trigger other actions.

Cobuilder is Airtable’s AI interface for building bases from scratch — describe what you’re tracking and it creates the table structure. Mixed results; useful for getting started, but you’ll almost always restructure it afterward.

What works well

Airtable AI fields genuinely shine for content enrichment at moderate scale — categorizing 500 records, summarizing meeting notes into action items, scoring leads based on description text. The results are stored in the table, so you pay for the AI call once and keep the output.

For prototyping AI-powered workflows before committing to a real stack, Airtable is fast. You can test whether an AI categorization approach produces useful results without writing any code.

What doesn’t work well

Performance at scale: Airtable starts to feel slow with tens of thousands of records. AI field generation across a large table can take significant time and credits.

AI credit pricing: Airtable’s AI features run on an “AI credits” system layered on top of existing subscription costs. At moderate usage, it adds up. The math isn’t always transparent before you’ve committed.

Complex data relationships: Airtable’s relational model is simplified compared to a real database. If you’re building anything with complex many-to-many relationships or need joins across multiple tables, you’ll hit friction.

Reliability of AI outputs: Like any LLM-based system, AI fields produce inconsistent outputs if your prompt isn’t precise. You need to validate results, especially for anything going into a business process.

When to use Airtable over alternatives

Use Airtable when:

• Your team is non-technical and needs to view, edit, and understand data directly in a clean UI
• You’re managing content, projects, or CRM data at a scale where spreadsheets are breaking
• AI enrichment is a nice-to-have on top of data management, not the core product
• You need quick setup and can live with its pricing at your data volume

Consider alternatives when:

• You’re building something users interact with (Supabase + a frontend is a better stack)
• You need to process data programmatically at scale (a real database + n8n is cheaper and more reliable)
• Your AI needs are complex (multi-step reasoning, custom prompts per row, high volume)

The honest position

Airtable’s AI features are genuinely useful for knowledge work teams who already use it for operations. If you’re a marketing team, a content team, or a project management team running Airtable as your operational hub, the AI fields add real value with minimal setup.

If you’re building a product or a serious data pipeline, Airtable is the wrong foundation — AI features included.