Solved: How did you discover your last profitable startup?

#devops #programming #tutorial #cloud

🚀 Executive Summary

TL;DR: Profitable DevOps and SaaS startup ideas emerge from solving persistent, expensive technical problems like internal toil, fragile scripts, and integration gaps. By identifying and productizing solutions for these widespread engineering pain points, valuable products can be built.

🎯 Key Takeaways

Successful developer tools often originate as internal projects that abstract complexity and achieve cross-team adoption, evolving into mission-critical infrastructure.
Systematically tracking and quantifying ‘toil’ (manual, repetitive engineering tasks) reveals the most expensive problems, which companies are most willing to pay to solve.
Significant value lies in exploiting ‘integration gaps’ between best-in-class DevOps tools, replacing brittle homegrown glue scripts with robust, opinionated integration platforms.

Profitable DevOps and SaaS startup ideas often originate from solving persistent, expensive technical problems that engineering teams face daily. By identifying and productizing solutions for internal toil, fragile glue scripts, and gaps in existing toolchains, engineers can build valuable products that address widespread industry pain points.

Symptoms: The “This Should Be Easier” Syndrome

In any growing engineering organization, certain patterns emerge that signal an unmet need. These aren’t bugs in a single application; they are systemic frictions that slow down development, increase operational load, and lead to burnout. These symptoms are the fertile ground from which profitable, problem-solving startups are born. If you find yourself or your team repeatedly saying “this should be easier,” you’ve likely found a valuable problem to solve.

Common symptoms include:

Recurring Alert Fatigue: The same PagerDuty or Opsgenie alert fires week after week, addressed by a manual fix documented in a runbook. The root cause is complex and never prioritized, but the manual intervention costs hours of high-value engineering time.
The “Script Shrine”: A critical process (like production data sanitization or a complex release rollback) depends on a collection of fragile bash or Python scripts. Only one or two senior engineers understand their nuances, creating a significant bottleneck and single point of failure.
Onboarding by Oral Tradition: Getting a new developer’s environment set up takes days and requires “tribal knowledge” passed down from other team members. The process for getting access to necessary cloud resources, databases, and internal services is poorly documented and manually provisioned.
Configuration Sprawl: Every new microservice requires copying, pasting, and slightly modifying hundreds of lines of boilerplate Terraform, Kubernetes YAML, or CI/CD pipeline configuration. This leads to configuration drift and makes platform-wide changes incredibly difficult.

Solution 1: Mine Your Internal Tooling Graveyard

Many of the most successful developer tools started as internal projects built to scratch a very specific itch. Think of Slack, which began as an internal communication tool for a game development company. Your company’s collection of internal dashboards, CLIs, and Slack bots is a potential goldmine. The key is to identify the one that has moved from a “pet project” to “mission-critical infrastructure.”

Identify the Candidate

Look for internal tools with these characteristics:

Cross-Team Adoption: It’s not just used by the team that built it. The security team, the data science team, and the platform team all rely on it.
Abstracts Complexity: It provides a simple interface (e.g., a Slack command or a simple web UI) for an incredibly complex backend process, like provisioning temporary cloud credentials or spinning up a full preview environment.
High “Bus Factor”: If the tool went down, multiple teams would be blocked. If the original author left the company, there would be a panic. This indicates true dependency and value.

Case Study: The “Temp-Creds” Service

Imagine a common scenario: a developer needs short-lived, permission-scoped credentials to an AWS production database for a debugging session. The manual process involves a Jira ticket, manager approval, and a senior DevOps engineer manually running IAM commands. It’s slow and error-prone.

An engineer builds an internal Slack bot. A developer types a command, a manager approves it with a button click in Slack, and the bot uses AWS STS to generate and DM the temporary credentials. This internal tool saves hundreds of hours per year.

This is a perfect startup candidate. The problem—managing just-in-time, least-privilege access—is universal. Here’s a hypothetical CLI interaction for such a tool:

# Developer requests access
$ access-cli request --role=prod-db-readonly --reason="Investigating ticket PROJ-1234"
> Request created. Please ask your manager to approve request #8675309 in Slack.

# After approval...
$ access-cli get-creds --id=8675309
> Your temporary credentials are now available as environment variables.
> This session will expire in 60 minutes.

$ env | grep AWS
AWS_ACCESS_KEY_ID=ASIAXXXXXXXXXXXXXXXX
AWS_SECRET_ACCESS_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
AWS_SESSION_TOKEN=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx...

The path to productization involves generalizing the tool to support different identity providers (Okta, Google Workspace), multiple cloud providers (AWS, GCP, Azure), and adding tenancy, auditing, and billing features.

Solution 2: Weaponize Your “Toil” Log

SREs define “toil” as the manual, repetitive, automatable, tactical work that lacks enduring value. It’s the work that scales linearly with service growth. By systematically tracking and quantifying this toil, you can pinpoint the most expensive problems your engineering organization faces. The problem that costs the most in engineering hours is often the one companies are most willing to pay to solve.

Quantifying Pain with Data

Don’t rely on anecdotes. Create a simple system (a spreadsheet, a Jira project) to log toil. For every manual task, log:

What was the task? (e.g., “Manually rotate expired TLS certificate for service-X”)
How long did it take? (e.g., 1.5 hours)
How often does it happen? (e.g., “Every 90 days per service”)

After a quarter, you’ll have data. If you find your teams are spending 200 hours per month managing Kubernetes YAML configuration drift across 50 microservices, you’ve identified a million-dollar problem disguised as a mundane task.

Example: The Kubernetes Manifest Sprawl

A common source of toil is managing raw Kubernetes manifests. For each new service, an engineer copies a deployment.yaml, service.yaml, and ingress.yaml, changes a few values, and hopes for the best. This is unmaintainable at scale.

A tool that provides a higher-level abstraction could solve this. Instead of writing hundreds of lines of YAML, a developer could define their service in a much simpler format, and the tool would generate the correct, compliant, and secure Kubernetes manifests.

Before (Raw YAML snippet):

apiVersion: apps/v1
kind: Deployment
metadata:
  name: user-service-prod
  labels:
    app: user-service
    env: prod
spec:
  replicas: 3
  selector:
    matchLabels:
      app: user-service
      env: prod
  template:
    metadata:
      labels:
        app: user-service
        env: prod
      annotations:
        prometheus.io/scrape: 'true'
        prometheus.io/port: '8080'
    spec:
      containers:
      - name: user-service
        image: my-registry/user-service:v1.2.3
        ports:
        - containerPort: 8080
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
# ... and a Service, and an Ingress, and a ServiceAccount...

After (Hypothetical Abstraction):

# In a custom `app-manifest.yaml` file
kind: SmartWorkload
name: user-service
image: v1.2.3
port: 8080
env: prod
cpu: "small" # Maps to a pre-defined resource request/limit
expose:
  public: true
  path: /users

A SaaS product that does this “compilation” from a simple abstraction to best-practice configurations, while integrating with CI/CD and policy-as-code engines (like OPA), provides immense value by reducing both toil and risk.

Solution 3: Exploit the “Integration Gap”

The modern DevOps landscape is composed of dozens of best-in-class tools for specific domains: GitLab/GitHub for source control, Terraform for IaC, Datadog/Prometheus for observability, etc. The problem is that these tools don’t always communicate effectively. The space *between* these tools—the integration gap—is filled with brittle, homegrown glue scripts.

Finding the Gaps

Look at your toolchain and ask where the manual handoffs occur. Where are you piping the output of one CLI tool into another? Where are you parsing JSON from one API to post it to another? These are integration gaps.

CI/CD to Observability: How do you automatically correlate a deployment event in your CI tool with a spike in latency in your monitoring tool?
Security to Project Management: When your container scanner (e.g., Snyk, Trivy) finds a critical vulnerability, how does a ticket get created, assigned to the right team, and tracked in Jira or Linear?
IaC to Asset Management: When Terraform provisions a new S3 bucket, how is it tagged and registered in your central asset inventory system?

A product that provides a robust, opinionated, and reliable bridge between two or more popular tools is often more valuable than the sum of its parts.

Comparison: Glue Script vs. Integration Platform

Let’s compare building a custom script to solve an integration gap versus using a dedicated product.


Aspect	Homegrown Glue Script (e.g., Python/Bash)	Dedicated SaaS Product
Initial Cost	“Free” (developer time)	Monthly/Annual subscription fee
Maintenance	High. Breaks with API changes. Needs constant owner. Becomes technical debt.	Low. Handled by the vendor. Covered by an SLA.
Features	Minimal. Solves one specific use case. No UI, logging, or alerting by default.	Rich. Includes UI, RBAC, audit logs, error handling, retries, and support for many configurations.
Scalability	Poor. Not designed for multiple teams or high throughput. State management is an afterthought.	High. Architected for multi-tenancy and scale from day one.
Support	Depends on the availability of the original author.	Professional support team, documentation, and community resources.

The table clearly shows that while a glue script is fast to create initially, its total cost of ownership is extremely high. A product that fills this gap is selling reliability, scalability, and peace of mind—a compelling value proposition for any engineering leader.