Do You Really Need Kubernetes? The Case for Simpler Infrastructure in Early-Stage Products
Do You Really Need Kubernetes? The Case for Simpler Infrastructure in Early-Stage Products
The tech stack choice made by the team directly impacts the productivity of the team. For startups, this becomes much more important given that the team needs to move fast to be able to survive in the market. The time spent in fighting the tech is the time that could have been very well spent in building/fixing features for customers. For startups, this can mean life or death.
Nowadays, I see lot of companies are using Kubernetes (K8s) as the default choice while building new applications. As someone who’s built and scaled multiple products from zero to millions of users, I often wonder if or when a team should move to a K8s based production system.
Kubernetes offerings
Kubernetes offers an impressive set of capabilities like -
- Container orchestration across multiple nodes
- Automated deployments and rollbacks
- Self-healing infrastructure
- networking and service discovery
- Horizontal scaling of application components
These features sound like a dream for any engineering team. But they come with significant costs that are often overlooked.
The Hidden Costs of Early K8s Adoption
1. Engineering Overhead
Kubernetes introduces a steep learning curve. Your team needs to understand pods, deployments, services, ingress controllers, persistent volumes, config maps, secrets management, and more. This represents a substantial investment of engineering time that could be spent building product features instead.
2. Operational Complexity
Running a production Kubernetes cluster is a task in itself. Most cloud providers provide a hosted K8s as service but it also comes with a good cost aspect. Irrespective of hosted or self managed, it introduces additional layer for debugging applications in a production environment.
3. Infrastructure Costs
K8S was designed to be able to handle google scale infrastructure providing a consistent production environment to services in a containerized way. Most of the startups - especially in the initial phase have constraints on the cost. Running K8S cluster can be a costly affair.
The Case for Starting Simple
In the early stages of a product, your primary focus should be:
- Validating product-market fit
- Iterating quickly based on user feedback
- Managing burn rate to extend runway
Simpler deployment options align better with these priorities:
Auto-Scaling Load-Balanced Deployments
Most cloud providers offer straightforward options that cover the core needs of early-stage products:
- AWS: Elastic Beanstalk or ECS with Application Load Balancer
- GCP: Cloud Run or App Engine
- Azure: App Service with auto-scaling
These services provide most of the additional things needed for prod environment out of the box such as:
- Automatic scaling in/out down based on traffic
- Load balancing across instances
- Monitoring such as Health checks
- Simpler operations
The Real Benefits of Starting Simple
- Moving fast: Simple deployments can be set up in hours
- Dev team effort: Your team can focus on product features rather than infrastructure.
- Simpler Operations: Fewer moving parts means fewer things that can go wrong
- Lower Costs: You’re not paying for infrastructure you don’t yet need.
- Easier Debugging: When issues arise, the simpler architecture makes it easier to identify root causes.
When Does Kubernetes Make Sense?
Kubernetes becomes increasingly valuable when:
- multiple engineering teams working on different services
- complex scaling requirements
- specific needs for resource isolation or multi-tenancy
- Separate Infra team
Most early-stage products simply don’t have these requirements.
A Pragmatic Migration Path
The good news is that starting simple doesn’t restrict a future migration to Kubernetes. Here’s a pragmatic evolution path:
- Start with managed platform services
- Move to containerized deployments when beneficial
- Adopt Kubernetes when the complexity/benefit tradeoff makes sense
This approach lets you focus on product-market fit first, then gradually adopt more sophisticated infrastructure as your needs evolve.
Conclusion
Most early-stage products simply don’t have Google-scale challenges.
There is a time in a startups life when your tech stack(including infrastructure) should match your product’s current needs.
When your product succeeds and begins to face genuine scaling challenges, that’s the right time to consider migrating to Kubernetes.
Remember: Big tech companies - all started without Kubernetes (it probably even exist back then). Their focus on product-market fit first. Sometimes, the best technology choice is the simplest one that meets your current needs.