Build vs Buy vs Outsource: When to Hire a DevOps Engineer

The question has changed

Five years ago, "should we hire a DevOps engineer" had a clearer answer: if you're beyond 15 engineers and shipping regularly, yes. The tooling landscape was simpler, the hire made sense, and the alternative was slow delivery.

Today the question is more nuanced. Managed Kubernetes, serverless, platform-as-a-service options, and a wave of DevOps tooling have changed what you actually need internal expertise for. Hiring a full-time senior DevOps engineer is expensive and competitive. But infrastructure that's held together with manual processes and institutional knowledge is a serious risk.

Here's a framework for thinking through it.

What "DevOps" actually covers

DevOps as a job title in practice covers several distinct skill areas:

A single hire rarely covers all of these deeply. A junior DevOps engineer can maintain existing infrastructure; a senior can architect it. Neither automatically does all of the above.

Stage 1: 0–10 engineers, pre-Series A

At this stage, your infrastructure should be minimal by design. The biggest mistake is over-engineering: building a Kubernetes cluster for three microservices when a simple deployment on a managed platform would do.

What's appropriate:

• A managed platform (Railway, Render, Fly.io) or simple EC2 + RDS setup
• GitHub Actions for CI/CD (simple pipelines, no custom runners)
• Basic CloudWatch or Datadog for logs and alerts
• One engineer who owns infrastructure part-time

What's premature:

• Multi-cluster Kubernetes
• Custom internal developer platforms
• A dedicated DevOps hire

If you're spending more than 10% of engineering time on infrastructure at this stage, you've over-built. The goal is to ship product and learn what your infrastructure actually needs to be.

Stage 2: 10–40 engineers, Series A

This is where the question becomes real. Your infrastructure is more complex, deployment friction is visible, and you're probably on AWS/Azure. You have a few options:

Option A: Hire a senior DevOps engineer

When this makes sense:

• You're building something infrastructure-intensive (real-time systems, high compliance requirements, unusual scale)
• Your technical co-founders have strong DevOps backgrounds and can manage this hire well
• You have a roadmap of 6+ months of infrastructure work
• You've found a senior candidate who wants the role and fits the culture

The honest cost: A senior DevOps engineer in the UK or US costs £90k–£140k + benefits. Add 30% for employer taxes, tools, training, and management overhead. You're looking at £120k–£185k per year fully loaded. At a Series A, this is a significant budget line.

The hidden risk: Hiring one senior DevOps engineer means your infrastructure knowledge is concentrated in one person. Turnover — which is high in this market — leaves you with institutional knowledge that walked out the door.

Option B: Outsource to a specialist

When this makes sense:

• You need production-ready infrastructure in a defined timeframe
• Your infrastructure needs are well-defined (Kubernetes cluster, CI/CD, monitoring) rather than open-ended
• You want knowledge transfer — your team should own it after
• You're not ready to manage a specialist hire

The honest cost: Good DevOps consulting typically runs £800–£1,500/day for senior expertise. For a 3-month engagement to build out your infrastructure foundation, you're looking at £50k–£100k — less than a full-time hire's first year, with delivery of a specific outcome.

The caveat: Quality varies enormously. An engagement that builds infrastructure your team can't maintain is worse than doing it yourself slowly.

Option C: Buy managed tooling

What modern managed services replace:

• Kubernetes management → AWS EKS with Karpenter (you still need someone who knows it, but AWS handles the control plane)
• CI/CD → GitHub Actions with Actions Marketplace (covers 80% of needs)
• Monitoring → Datadog, New Relic (opinionated but fully managed)
• Database → RDS, Aurora (no DBA required)

The trade-off: Managed services trade control for simplicity and cost. Datadog is expensive ($200+/month per host at scale). But if you're at 15 engineers and not yet at scale, the cost is manageable and the operational savings are real.

Stage 3: 40–150 engineers, Series B+

At this stage, the infrastructure engineering function almost always needs to be internal. The complexity is too high, the systems too business-critical, and the team too large to manage through external engagements.

What to build:

• A platform engineering team (not just "DevOps") — 1 platform engineer per 15–20 product engineers
• Self-service infrastructure for product teams
• Internal developer platform or golden path tooling
• On-call rotation and incident management

What to buy:

• Monitoring and observability (Datadog/Grafana Cloud for teams that don't want to run their own Prometheus)
• Security scanning (Snyk, Wiz)
• Cost management (Kubecost, Infracost)

What to outsource:

• Specific, high-expertise work: initial Kubernetes architecture, compliance audits, security assessments
• Time-boxed capacity when hiring is slow

The knowledge transfer imperative

Whatever you decide in the short term, your goal should be that your team understands the infrastructure you're running. An external team that builds infrastructure your engineers treat as a black box is a liability, not an asset.

Good infrastructure engagements end with: documented architecture, code your team can read and change, runbooks for common operations, and training sessions where your engineers ask hard questions. If an engagement doesn't include this, negotiate it in.

The question nobody asks

Before deciding between build, buy, or outsource, ask: what problem are we actually trying to solve?

If the answer is "our deployments are slow," that's a CI/CD problem that might be solved with a two-day GitHub Actions rewrite, not a six-month hiring process. If the answer is "we have no visibility when things break," that's a monitoring problem solvable with a week of Grafana setup.

The DevOps investment should match the problem. Start with the specific friction that's costing the team time, solve it as cheaply as possible, and use the learning to inform the next investment. Good infrastructure grows with the team; it doesn't need to be fully built before the team arrives.