The first startup I joined didn’t fail because of a lack of ideas or passion. It struggled because our technology choices couldn’t keep up with the growth we were chasing. Every new customer felt like it might break something.
I’m Phong Lee, and over time I’ve seen how the right technology decisions can help startups scale smoothly—while the wrong ones turn growth into chaos.
Here are the lessons I’ve learned about scaling with technology, told from the perspective of an engineer who has lived through both the painful and the rewarding versions.
Lesson 1: Move fast—but not so fast you can’t fix things
Early on, speed is everything. You’re trying to:
- Find product–market fit
- Learn what customers actually want
- Ship experiments quickly
At one startup, we moved fast in the best and worst ways:
- Best: we shipped features weekly, sometimes daily.
- Worst: we cut corners everywhere—no tests, weak monitoring, minimal documentation.
When the first big customer arrived, every release felt terrifying. We were afraid to touch certain parts of the code because “no one fully understands that module anymore.”
The lesson wasn’t “move slower.” It was:
- Invest just enough in foundations—tests, logging, and deployment pipelines—to keep moving fast safely.
- Accept that not every early decision will be perfect, but avoid creating areas of “untouchable” code.
Lesson 2: Start simple, then modularize with intent
There’s a temptation to over-architect from day one:
- Microservices
- Complex event-driven systems
- Multi-region deployments
Most early-stage startups don’t need that.
What worked best in my experience:
- Start with a well-structured monolith: one codebase, clear boundaries between domains.
- Keep interfaces clean so components can be extracted later.
- Introduce services or queues when you feel real pain, not just because it’s trendy.
At one company, we extracted our first service only after we’d seen:
- A clear performance bottleneck in a specific domain.
- A well-defined API surface that had already stabilized.
- A team ready to own and maintain that service.
Because we waited for the right moment, the extraction went smoothly—and actually reduced complexity instead of increasing it.
Lesson 3: Choose tools your team can grow with
I’ve seen stack decisions made because:
- “It’s what that unicorn company uses.”
- “I saw a cool conference talk about it.”
- “It’s the hottest thing on Hacker News right now.”
The reality is:
- A boring, well-understood stack that your team can hire for and maintain beats a shiny one nobody fully understands.
- Developer experience matters—good tooling, clear patterns, and strong community support pay off as you scale.
At one startup, we chose a popular but niche database. It was exciting—until we needed to hire more engineers and troubleshoot obscure issues at 2 a.m. We eventually migrated to a more standard choice and instantly benefited from better tools, docs, and community wisdom.
Lesson 4: Observe everything early
When your user base is small, failures are quiet. When you start to scale, those same failures become loud—and expensive.
Some of the best scaling investments I’ve seen were in:
- Logging with structured, queryable data.
- Metrics and dashboards for latency, errors, throughput.
- Alerts tuned to real user impact, not just CPU spikes.
On one project, we caught a scaling issue early because our metrics showed:
- A slow but steady increase in response times
- A correlation with specific endpoints as load grew
We fixed it before customers ever complained.
Without observability, you’re scaling blind.
Lesson 5: Automate deployment before it hurts
When deployments require manual steps, I get nervous—especially in a fast-growing startup.
Healthy scaling requires:
- Reliable CI/CD pipelines
- Repeatable, scripted deployments
- Rollback strategies when things go wrong
At one company, we went from “ssh and hope” deployments to:
- Automated builds and tests on each commit
- One-click (or one-command) deployments
- Blue-green or canary strategies for high-risk changes
Suddenly, deployment stopped being the bottleneck, and iteration sped up without increasing risk.
Lesson 6: Align tech decisions with business strategy
The best technology choices I’ve seen weren’t just technically elegant—they were clearly tied to business goals:
- Reducing churn by improving reliability
- Enabling new pricing models with usage-based metering
- Supporting new markets with localization and compliance
As an engineer, I’ve learned to ask:
- “How does this technical project support revenue, retention, or differentiation?”
- “What’s the simplest version that delivers business value now and keeps doors open later?”
When tech and business strategies line up, prioritization becomes much easier.
How I think about scaling with technology now
Today, when I, Phong Lee, join a startup or growth-stage team, I look for:
- A bias toward shipping and learning, not building for hypothetical scale.
- A willingness to pay down critical tech debt before it turns into a wall.
- Pragmatic choices: simple architecture, strong observability, and solid automation.
Scaling with technology is not about choosing the most advanced stack. It’s about:
- Making clear, intentional trade-offs.
- Building foundations that support, not slow, growth.
- Learning and adapting as real users and real load arrive.
If you treat your technology as a living system that grows with your startup—not a static “right answer” you must guess on day one—you’ll be much better prepared for the moment when success arrives and your product suddenly has to support far more users than you expected.