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Kubernetes or PaaS: how to choose where to deploy your application

Choose where to run your app based on your team, not on fashion

Data reviewed on Oct 29, 2026 · LLM Stats indexes and official prices

The data, today

The context

Kubernetes solves real scale and orchestration problems, but it charges an operations tax: configuration, upgrades, networking, resource limits and people who understand it. For many small teams, a PaaS that deploys from Git delivers most of the value for a fraction of the work.

The decision is rarely technical; it is about the team. How many services you have, how much operations experience there is and what your customers require on networking or compliance. If you pick Kubernetes, badly set CPU and memory limits, health probes and load balancers cause most incidents. Here are the guides and prices to decide with numbers.

What to decide

  1. 1

    When is a PaaS enough?

    When you have few services, no platform team and no need for fine network control. You deploy from Git, the provider handles certificates and basic scaling and you focus on the product. Check prices in the hosting catalog.

  2. 2

    When does Kubernetes make sense?

    With many services, particular network or compliance needs, varied workloads or portability requirements across clouds, and people able to operate it. A managed service reduces the work, but doesn't remove it.

  3. 3

    What fails most in production?

    Badly set resource limits: with none, a process exhausts the node; with very low ones, the system restarts containers without warning. The Kubernetes limits guide explains how to measure and set values, and the load balancer guide which problems they solve and which they don't.

Common mistakes

  • Adopting Kubernetes with nobody on the team able to operate it.
  • Deploying without resource requests and limits defined.
  • Choosing by fashion instead of by services, team and real requirements.

Tools and comparators

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Guides to go deeper

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No provider pays for its position. Indexes come from LLM Stats; prices from each provider's standard API. How we measure

Reduce AI costsMeasure tokens per request and pay for big models only when neededAI customer supportWork out cost per conversation without dropping below your quality barDocument search and RAGFind the right passage before asking the model for an answerCoding with AIMeasure cost per coding task and review what the agent writesCut cloud costsFind the hidden spend in egress, operations and idle resourcesHandle traffic spikesAbsorb traffic bursts without dropping requests or the databaseShip to productionDeploy from Git with separate environments and clean configurationChoose a databaseCompare Supabase, Neon, Turso, PlanetScale and D1 by how they billLaunch an MVPDeploy and store data at minimum cost and without surprisesEvaluate new AI providersCompare Kimi, GLM, Qwen and Muse Spark with the leadersCI/CD from GitHub to productionShip every commit with tests and no long-lived keys in GitHubSecrets and environmentsSeparate dev, staging and production and stop leaking environment variablesObservability without surprise billsMeasure what matters with OpenTelemetry and SLOs that don't page anyone for nothingQueues and background jobsProcess heavy tasks without duplicates when something retriesSecure file uploadsReceive files without routing them through your server or blowing up storage costsInfrastructure as codeDecide between Terraform and OpenTofu and keep your infrastructure in GitFast PostgreSQLFind the slow query with EXPLAIN and fix it with the right index
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