Growth met legacy systems
A Finnish gaming enterprise was in a familiar place: customer demand was increasing, product teams were shipping more frequently, and data needs were growing beyond what the on-prem setup was built for. The company had already decided to use both GCP and AWS, choosing the best fit per workload, but the reality was that much of the estate still lived on-premises.
The key constraint wasn’t ambition. It was expertise and bandwidth. There wasn’t deep in-house knowledge of building and operating large-scale environments in either cloud, and the infra team was small. Yet the business needed results: reliable platforms, safer releases, and a clear path to migrate critical apps and data platforms without turning migration into a multi-year distraction.
Migrating while running the business, without breaking trust
The pressure wasn’t just “move to cloud.” It was “move to the cloud while everything must keep working.”
A few symptoms made the risk obvious:
- On-prem environments were hard to scale and slow to change, especially when new projects needed compute, networking, or access patterns quickly.
- Application migrations were coupled to infrastructure uncertainty: if the landing zone wasn’t stable, every migration became a bespoke firefight.
- Data platforms had growing demands (pipelines, storage, processing), and the data team needed both best practices and hands-on help to keep momentum.
- Dev teams depended heavily on infra for cloud changes (e.g. permissions, networking, event triggers) creating queue time and mental overhead on both sides.
If migration went wrong, it would show up as downtime, delayed product launches, and unhappy customers. And if it went “okay but messy,” the company would still pay for it later through operational pain and fragile environments.
Start with a safe landing zone, then migrate in slices
The work was framed around reducing risk and increasing repeatability, not creating a perfect end-state on paper.
First, establish cloud foundations that make migration boring: predictable environments, consistent identity and access, clear networking patterns, and deployment practices that could be repeated across projects. Then migrate in slices: prioritize the apps and data components that delivered value fast, learn from each move, and continuously improve the templates and guardrails.
Terraform was the backbone for this because it made the cloud reviewable and reproducible. Serverless automation (Cloud Functions / Lambda) handled glue work and repetitive tasks, so the team wasn’t stuck doing everything manually.
Cloud foundations + app and data migrations that teams could sustain
The infra team’s role covered architectural design, large environment deployments, and direct support to development and data teams. The work included:
- Designed and deployed multi-cloud landing zones (GCP + AWS)
Core building blocks were established: networking, IAM patterns, project/account structure, logging hooks, and baseline guardrails. Not to make both clouds identical, but to make them consistent enough that migrations and new workloads had a predictable home.
- Codified infrastructure with Terraform modules and environment structures
Infrastructure became versioned, repeatable, and easier to review. This reduced console-driven drift and made it realistic to spin up new environments and replicate patterns safely.
- Migrated applications from on-prem to cloud with a “small batch” approach
Apps were moved in manageable increments rather than big-bang cutovers. Each migration improved the baseline: better templates, clearer conventions, and fewer one-off exceptions. The goal was steady progress without creating a migration “snowflake farm.”
- Migrated data platforms and enabled cloud-native pipeline patterns
Over roughly a year of work embedded with the data team, the focus was both enablement and delivery: helping the team leverage cloud best practices and actively contributing to building and evolving data pipelines suited for cloud infrastructure.
- Built targeted automation using Cloud Functions and Lambda
Practical serverless scripts handled recurring operational tasks and integration glue—event triggers, housekeeping, and repeatable routines that used to require manual steps or deep context.
- Supported dev and data teams and reduced dependency on infra tickets
The infra team didn’t just “do the work.” They coached teams on patterns, produced documentation and examples, and shaped requests into changes that were fast to implement and safe to operate.
Migration progress without chaos and a platform that got easier over time
This kind of work pays off in two ways: immediate delivery and long-term operational sanity. Typical outcomes for an engagement like this looked like:
- Faster environment provisioning (often hours vs days) thanks to reusable Terraform modules and clear conventions.
- Smoother migrations because apps and data platforms landed on known-good foundations, reducing rework and last-minute surprises.
- More predictable changes and releases, shifting away from manual cloud steps to reviewable infrastructure code and repeatable deployment patterns.
- Lower operational noise from cloud-related inconsistencies (permissions drift, missing triggers, environment differences), often reduced by ~30–50% in similar cleanups.
- Better onboarding and team autonomy, with shared patterns and documentation shortening ramp-up time and reducing the “only one person knows” problem.
Less firefighting, more flow
Before, cloud work could feel like a constant negotiation between “we need this now” and “we can’t safely change that quickly.” After the foundations and migration patterns were in place, day-to-day work became calmer:
- Developers spent less time waiting for infra and more time shipping.
- Data engineers got a platform that matched the way modern pipelines behave in cloud environments.
- Infra time shifted from repetitive tickets to higher-leverage improvements and guardrails.
The company didn’t suddenly have a huge platform team. They just stopped losing so much time to platform friction.
Keep migrating, keep simplifying, keep spreading ownership
With landing zones and repeatable patterns established, the roadmap became more straightforward:
- Continue app migrations using the proven playbook and templates.
- Mature observability and cost visibility where it matters most.
- Tighten security posture incrementally (policies, least privilege, audit readiness).
- Keep strengthening CI/CD and release safety as the estate grows.
When you need senior help to migrate without drama
If you want to migrate apps and data platforms from on-prem to cloud (GCP, AWS, UpCloud) without turning it into a never-ending program, Kinetive can help you put foundations in place, ship migrations in slices, and reduce operational load as you go.