Supabase pricing: plans, free tier limits, and the bills AI is quietly creating

In June 2026, a database company most CFOs had never heard of raised $500 million at a $10.5 billion valuation. Supabase had doubled its valuation in eight months and roughly quintupled it in fourteen. The investor list read like a signal flare: GIC, Accel, Coatue, Stripe.

What earned that number wasn’t a sales team. It was a behavioral shift. Database launches on Supabase grew more than 600% in a single year, and over 60% of them were launched by an AI tool, not a person. Supabase’s user base more than doubled between the Series E and this round.

That 60% should change how finance thinks about this bill.

When Claude Code, Lovable, or Bolt builds someone an app, Supabase is very often the backend it picks, with Claude Code the single largest contributor in 2026. Which means a meaningful share of database spend is now being created by AI, prompt by prompt.

The people prompting have usually never read a pricing page. The $25 tier that feels like pocket change on day one has a habit of multiplying, and the Reddit threads ranking for this exact topic (“the $25/mo pricing tier killed my project”) show how often the math ambushes people.

So let’s do the math properly instead. Here’s every plan, the complete free tier specs, where the real costs hide, how Supabase compares to running Postgres on AWS, and how to keep all of it visible before the invoice does the talking.

How much does Supabase cost?

Supabase pricing follows a tiered model with usage-based charges layered on top. Four plans, billed per organization, and the fastest way to understand them is side by side:

The structure to internalize: the plan fee buys the tier, but compute is billed per project, and egress, storage, and monthly active users are metered on top. Two production projects on Pro is never just $25. It’s $25 plus two compute instances plus whatever those apps consume.

That per-project layering is the single most misunderstood part of the model, and it’s where most surprise bills are born. It matters most at the bottom of the ladder, though, because the free tier is where nearly everyone starts. So let’s spell out exactly what free includes.

What are the Supabase free tier limits?

The Supabase free tier is genuinely generous, which is exactly why it converts so well. Since the limits are the most searched, most second-guessed numbers in this entire topic, here is the complete specification, so you don’t need to trust a forum thread:

Two of those rows do all the pushing toward paid plans. First, the two-active-project cap, which the AI-builder era hits almost immediately: every experiment an AI tool spins up is its own project, and the third one needs a paid organization.

Second, the inactivity pause. A paused project keeps its data, but the app goes dark until someone manually restores it from the dashboard. Fine for a hobby project. Unacceptable for anything with real users, which is precisely the point of the rule.

Genuinely free, then, for two small active projects. The free plan even includes unlimited API requests, which surprises people who expect a request cap. The moment you need an always-on app, a bigger database, or a third project, you’re shopping paid tiers.

Every figure above matches Supabase’s published pricing page as of August 2026, and it’s worth rechecking annually, because Supabase has adjusted included quotas before. Bookmark the source; come back here for what the numbers mean.

What those limits mean in practice, for most teams, is an eventual move to Pro. And Pro is where the real cost story starts, because Pro’s $25 sticker price and Pro’s actual production bill are two different numbers, separated by a set of meters worth understanding before they start running.

What does the Pro plan really cost in production?

Pro is $25 per organization per month, and it’s where most real applications live. It lifts each database to 8 GB, file storage to 100 GB, egress to 250 GB, auth to 100,000 monthly active users, and removes the inactivity pause. It also includes a $10 monthly compute credit, covering exactly one Micro instance.

That compute credit is the tell. Every project runs on dedicated compute billed separately: Micro at $10 a month, Small at $15, Medium at $60, climbing from there. One production app on Micro effectively runs $25 all-in. Upgrade it to Medium for real traffic and you’re near $75.

Corroborated real-world figures across independent 2026 analyses cluster the same way: small production apps land between $35 and $75 a month, and apps serving 50,000 to 200,000 monthly active users typically run $100 to $200. Each additional project adds its own compute meter to the pile.

One design decision deserves genuine credit. Pro ships with a spend cap turned on by default, which blocks overage billing until you deliberately disable it. It’s a rare pro-customer default in usage-based pricing. It’s also the setting teams switch off the moment they need uncapped growth.

That switch-off moment is worth marking in your calendar, because it’s when visibility has to replace the cap as your protection. Before we get to the meters that make that protection necessary, one quick word on the two tiers above Pro.

When do you need the Team or Enterprise plan?

Team, at $599 per organization per month, is not about capacity. It buys compliance and control: SOC 2 and ISO 27001 support, SSO, 14-day backup retention, and granular access controls. If your customers demand a SOC 2 report, Team is the entry ticket. If you’re wondering whether you need it, you don’t yet.

Enterprise is custom-priced and adds HIPAA support, bring-your-own-cloud deployment, and dedicated 24/7 support. It’s the tier for the moment Supabase stops being a startup’s backend and becomes infrastructure a board asks about. Among all the Supabase pricing plans, these two are bought for auditors, not for throughput.

For everyone below that compliance line, the plan fee is quickly a rounding error anyway. The meters are what decide the bill, and four of them do nearly all the damage. Here’s where the money actually leaks.

Where do Supabase’s hidden costs come from?

None of these charges are hidden in bad faith. They’re published, metered, and easy to ignore until they aren’t. Four meters drive nearly every surprise bill, and they compound with each other as an application finds its audience:

Compute sprawl deserves special mention in the AI era. Ten AI-generated experiments idling on Micro compute is $100 a month of nothing much, the same idle-resource pattern we flag constantly in database cost management. Nobody decided to spend that money. It accreted.

Egress got one genuine improvement worth knowing: an August 2025 change made cached egress roughly 3x cheaper, a real win for content-serving workloads. Cache aggressively and the bandwidth line behaves. Serve everything hot and it becomes the bill’s main character.

The pattern is familiar to anyone who’s managed a cloud bill. The sticker price is a story about day one. The meters are the story about month twelve, and month twelve is when teams start asking the comparison question, so let’s answer it properly.

Is Supabase cheaper than AWS RDS or Aurora?

The honest answer: at small scale yes, at large scale it depends on what you’re counting. For a small production app, Supabase’s $25 to $75 a month undercuts comparable managed Postgres on AWS once you count what’s bundled: auth, storage, auto-generated APIs, and realtime, services you’d otherwise assemble separately.

As our RDS pricing breakdown shows, even a modest instance with storage, backups, and data transfer clears Supabase’s floor quickly, before you’ve built any surrounding services. The Aurora vs. RDS decision adds capability, and cost, in the same direction.

At scale, the equation flips on two hinges. Raw compute per dollar favors AWS, especially with reserved capacity and instance-level tuning Supabase doesn’t expose. And egress at $0.09 per GB becomes material for high-traffic apps in ways AWS architectures can engineer around with caching and region design.

Teams also graduate for control reasons, not just price. It’s the same maturity curve that moves specialized workloads toward DynamoDB, MongoDB Atlas, Snowflake, or Databricks as data needs specialize past what one platform serves well.

The practical takeaway for finance: Supabase is usually the cheaper start, and the migration question arrives later. The teams that answer it well answer it with real unit cost data, not with a shocking invoice forcing the decision. Which raises the question of how to stay ahead of that invoice.

How do you keep Supabase costs under control?

Five practices, in the order they pay off. None require new tooling on day one. All of them require someone deciding, in advance, that database spend deserves the same weekly attention as database uptime, a discipline most teams apply to CloudWatch and forget here.

Keep the spend cap on until you consciously outgrow it. It’s the difference between a blocked request and a surprise bill. When you do disable it, stand up real spend monitoring that same week, not a calendar reminder to eyeball the dashboard.

Audit projects monthly. In the AI-builder era, projects multiply themselves. Every abandoned experiment still on paid compute is pure waste with a monthly renewal date. Kill or downgrade anything that hasn’t served a user in thirty days.

Right-size compute against actual utilization. The jump from Micro to Medium is $50 a month per project. Make sustained traffic data trigger the upgrade, not launch-week anxiety, and revisit the sizing quarterly the same way you’d right-size any cloud instance.

Watch egress per feature. One chatty endpoint or unoptimized media route can dominate the bandwidth line for the whole organization. Cached egress costs a third of the hot path, so caching is the single most effective egress decision available.

Assign every project an owner and a purpose. This one sounds bureaucratic and saves the most money. A database an AI tool created for a demo six months ago has no owner, no purpose, and a live meter. Somebody should be able to say what it’s for.

Run those five and Supabase stays cheap relative to what it delivers. But notice what they all assume: that a human is watching. The most important shift in this entire topic is that the spending itself is no longer human-paced, and that deserves its own section.

Why is AI creating Supabase spend faster than finance can see it?

Go back to that 60% figure. More than half of new Supabase databases are launched by AI tools. Each one is small. Collectively, they’re a new spending pattern: no requisition, no approval, and frequently no human who remembers creating the thing that’s billing.

Supabase is simply where this future arrived first, because it’s the backend AI builders default to. The same pattern is coming for every usage-based platform in the stack, from the model APIs we track across OpenAI, Claude, and DeepSeek pricing down to the storage layer.

Now put machine-paced spending against how finance actually operates. In CloudZero’s 2026 survey of 260 finance leaders, 19% said they find out what AI initiatives cost only after the money is already gone. Meters opening at 600% annual growth, reviewed retroactively, if at all.

That’s not a Supabase problem. That’s a visibility problem wearing a Supabase logo, and it’s the defining finance problem of the AI-builder era. The teams handling it well aren’t blocking AI-built apps. They’re the ones who can see, in near real time, what every project costs and what it’s for.

Seen that way, the ten experiments that go nowhere get shut down in week two, and the one that becomes a product gets a real budget in week three. That near-real-time view is exactly the job CloudZero was built to do.

How does CloudZero make Supabase spend visible?

CloudZero is the AI ROI company, and this is precisely the pattern the platform exists for: spend that’s fragmented, usage-based, and growing faster than manual review can track. CloudZero manages more than $15 billion in cloud and AI spend, and its job in a Supabase-shaped world is turning meters into decisions.

Every bill in one place, including Supabase. CloudZero’s AnyCost capability ingests spend from any source, so Supabase charges land in the same normalized view as AWS, Azure, GCP, Snowflake, MongoDB Atlas, and Databricks, alongside AI platform costs from OpenAI and Anthropic.

The full AI unit cost, not a partial one. The app an AI tool built, the database it runs on, and the model tokens it burns become one line of sight. That completeness matters because an AI feature’s true cost spans all three layers, and ROI math built on one layer is fiction.

Cost per project, product, and customer, without perfect tagging. CloudZero’s dimensions map raw charges to what the business actually discusses. That unit-level view is how one customer running more than 50 large language models in production found over $1 million in savings, by seeing which workloads earned their spend.

Anomalies caught in hours, not on invoices. A disabled spend cap plus a viral app is a beautiful problem, if you see it Tuesday instead of on the first of the month. That early-warning discipline is a core reason CloudZero customers average 22% savings in their first year.

Supabase made creating a database nearly free, and that was never the expensive part. The expensive part is not knowing what all those databases are worth. That’s the difference between the AI-builder era showing up on your P&L as innovation or as leakage.

Here’s a concrete way to start, sized to how real teams actually adopt this. Request a demo and bring one real question, like “what did our AI experiments cost last quarter, and which ones earned it?”, then watch it get answered against your own spend data.

Prefer to start smaller? A free cloud cost assessment carries no platform commitment. You get a clear, specific read on where your current visibility ends, what lives inside that blind spot, and what it’s costing you every month not to know.

Frequently asked questions about Supabase pricing

Author Spotlight

Lyne Carolyne

Lyne Carolyne has several years of experience in AI and cloud economics and brings that understanding into the content she creates. Outside work, she's an avid explorer.