Frequently asked questions

AWS is the most common default because of its ecosystem and documentation maturity. But if your team already knows another platform well, that's usually the better choice — competence beats marginal platform differences for a small team.

Hard. Data egress is expensive, and every managed service you use ('serverless') adds vendor-specific code you'll have to rewrite. The switch is a project, which is exactly why choosing once, deliberately, matters.

For most startups, multi-cloud is overhead you don't need. Pick one primary provider. The exception is when you have a regulatory or continuity reason to run in a second region or platform.

As soon as there's a second person touching the codebase or the product has users — for most startups that's within the first month. Waiting for 'the right time' means the first real deploy happens in a panic.

No. A minimal CI/CD pipeline is a few files in your repo and a free or cheap hosted runner. The hard part isn't the tooling — it's committing to the habit of small, frequent, tested changes.

A straight-to-production pipeline for one microservice or deploy unit — lint, test, build, tag, deploy, rollback. Get that working end-to-end and you've removed more deploy anxiety than any other single step.

Lift-and-shift is fastest and lowest-risk but preserves existing problems. Replatforming takes longer but fixes architecture debt. The right choice depends on whether you plan to keep the system for years (replatform) or just need to get off legacy hosting fast (lift-and-shift first, optimize later).

The most common causes are skipping data validation after the move, cutting over traffic without a rollback plan, and forgetting that cloud bills go up if old, oversized workloads are simply moved as-is.

A straightforward lift-and-shift of a single application typically takes 4–8 weeks. A full replatform with re-architecture can take 3–6 months. Timelines depend mostly on data volume, environment count, and how much custom work is needed to reach parity.

Off-the-shelf makes sense when the problem is standardized — you follow a common workflow and your differentiator isn't in the software. Buying wins on speed and low upfront cost, and you don't have to maintain anything.

Custom software costs more upfront, but licensing fees compound over years. A common pattern is custom starting to pay for itself once you'd otherwise be paying for multiple seats, multiple tools, or paying for features you never use.

Maintenance is the big one — bugs, dependency updates, security patches, and feature requests don't stop after launch. Owners who don't budget for continued development end up with software that quietly rots.

No. Kubernetes solves multi-machine orchestration — most SaaS products scale fine on a single host or a managed container platform first. Bring in Kubernetes when the managed option is genuinely limiting you, not because it's trendy.

Added complexity, a new layer to debug, and a performance cost that rarely matters except in hyper-pedantic cases. The upside (environment parity, reproducible builds) generally outweighs it once the team is past one machine.

If your app is stateless enough that any instance can be replaced by a fresh one (state lives in the database and object storage), it's ready. If it relies on local file storage and sticky sessions, those are the parts to fix first.

It's risky when you pick based only on price. The risks — communication gaps, quality issues, disappearing teams — are all mitigated by choosing a registered company with senior engineers, milestone-based progress, real time-zone overlap, and a contract that defines ownership.

Meet the actual engineers who will do the work before signing. Insist on milestone-based demos. Agree on working-hours overlap for real-time collaboration. And check whether the company is formally registered and accountable — that alone filters out a huge share of bad actors.

The classic pattern is a salesperson promising senior talent, a rotating cast of juniors doing the work, no communication until deadlines, and a team that disappears once paid. These are contractual and cultural failures, not location failures.

A typical MVP for a well-scoped SaaS can range from a focused 8–12 week build for a single core workflow, to 4–6 months for products with complex backend logic. The biggest cost driver is un-scoped features, not the deployment stack.

Admin dashboards for your own ops, deep integrations, multi-tenant customization, and polish pass — these are tempting but rarely what users validate with. The exception is anything you literally cannot launch without.

You need to be able to charge users, but a minimal commerce integration (Stripe Checkout, etc.) is usually enough. PCI-built-in billing platforms mean you can defer building your own billing engine entirely.

Hourly it's usually more than a local salary — but total cost of ownership (recruiting, ramp time, benefits, severance, idle time between projects) often makes augmentation cheaper for time-boxed or variable work. For permanent, central roles, hiring usually wins.

The main risk is treating augmentation as 'outsourced to a vendor' instead of integrating the engineers into your team. The fix is real immersion — your repos, your standups, your standards — which is exactly how we run it.

With a pre-screened bench, an engineer can be working with your team within days of agreeing on the role — compared to the typical 3–6 month recruiting cycle for a full-time hire.