What Is PaaS and What Should You Look for in a Platform?

PaaS (Platform as a Service) is a hosting model where you push code and the provider handles the servers, scaling, and operational work underneath. It fits teams that want to run production applications, databases, storage, and static sites without managing clusters or infrastructure tooling. It fits less well when you need deep control over the runtime environment or expect to move between providers frequently.

PaaS vs. IaaS vs. manual cloud setup

The practical difference is how much of the stack you own.

With raw infrastructure, you wire services together yourself: provision a database, attach storage, configure a deploy pipeline, set up scaling rules. A PaaS bundles those pieces so they "work together by default," as Sevalla describes its own product line. The trade is straightforward — you give up some control in exchange for not maintaining the plumbing.

What a PaaS typically bundles

A full platform usually covers four service types that would otherwise be separate cloud products:

• Application hosting — deploy, scale, and run services without managing clusters or infrastructure tooling.
• Database hosting — run production databases without provisioning, scaling, performance tuning, or ongoing maintenance.
• Object storage — store and serve files without bucket management; often S3-compatible.
• Static site hosting — serve static sites without scaling setup or deployment plumbing.

The value is in the integration. When these run on one platform, you skip the work of connecting cloud APIs, permissions, and infrastructure tooling across vendors.

The trade-offs to weigh

Reduced DevOps overhead vs. control. A PaaS removes pipeline maintenance and cluster management. If your workload needs custom kernel settings, specific runtime patches, or unusual networking, that abstraction can become a constraint.

Cost model. Managed platforms often reduce total spend by eliminating idle infrastructure and operational labor. Sevalla cites ClearEstate and Stepler replacing their cloud setups, with Stepler reporting a drop from roughly $3,000 to $650 per month (a 78% cut) and claims of up to 88% less cloud spend across customers. Treat vendor case studies as directional, not guaranteed — your savings depend on your current architecture.

Portability and lock-in. Before committing, check how tightly your app binds to platform-specific features. Standard containers, environment variables, and S3-compatible storage keep exit costs lower than proprietary APIs.

Regions and latency. If your users are distributed, region coverage matters. Sevalla lists 25 data centers plus 260+ PoP servers for edge delivery of static content. Match the provider's footprint to where your users actually are.

Selection criteria to check before committing

1. Pricing model — usage-based, per-service, or flat? Look for a calculator to model your real workload rather than the headline tier.
2. Service coverage — does it handle apps, databases, storage, and static sites, or only one? Fewer vendors means fewer integration points.
3. Deployment workflow — can you push code and have the rest handled, or do you still configure pipelines?
4. Scaling behavior — automatic, or manual rules you maintain?
5. Region and edge coverage — enough locations to keep latency low for your audience.
6. Permissions and isolation — project-level isolation and scoped permissions matter if multiple people or AI agents act on your infrastructure.
7. Exit path — standard formats and compatible APIs reduce migration pain later.

Where teams get stuck

The common failure points are predictable:

• Assuming "no infrastructure" means no configuration. You still define environment variables, build steps, and service connections.
• Ignoring the pricing calculator. Small workloads look cheap until database and storage usage scale.
• Overlooking lock-in until migration. Choosing proprietary features early makes leaving expensive.
• Skipping region planning. A single-region deploy is fine until your user base spreads out.

One newer consideration: some platforms now expose services to AI agents through MCP, letting agents deploy apps and databases, read logs and metrics, and help debug — with project-level isolation and scoped permissions to bound what they can touch. If that matters to your workflow, verify the permission model before enabling it.

Bottom line

Choose a PaaS when operational overhead is your bottleneck and your workload fits standard patterns. Choose raw infrastructure when you need control the platform can't give you. Either way, evaluate on the same axes: service coverage, pricing model, regions, scaling, deployment workflow, permissions, and how hard it would be to leave.