Maybe you're moving back to your own data center to win back margin or satisfy data residency rules. Maybe you're lifting on-prem systems into the cloud because elasticity now matters more than a fixed bill. Either way, your move happens in stages, can be rolled back at any point, and stays uneventful.
Your migration runs to and from the platforms your team already knows.
Cloud versus on-prem isn't a matter of belief. It's a question of economics and strategy. The right answer depends on the shape of your workloads, your unit economics, and where your business needs to be a couple of years out. You make the move when the numbers say it's time, and we make sure it lands.
Workloads that run at a steady, predictable level keep racking up variable charges. Reserved instances took the edge off for a while. Now moving back in-house looks better than signing up for another multi-year commitment.
Regulators, customers, or a new board policy now want your data held in a specific jurisdiction or on infrastructure you control. The map of cloud regions and the map of legal requirements no longer line up.
Your training runs and inference services need GPUs that are scarce in the cloud, expensive in the cloud, or both. Buying the hardware is starting to add up, but the rest of your platform isn't ready to run on-prem yet.
Your data center was sized for last year's load. New markets, seasonal spikes, or one big product win mean you now need to scale on demand. Racking up another set of servers would take six months you don't have.
Each step produces something concrete, comes with a written hand-off, and has to clear a checkpoint before we move to the next one.
Over one to two weeks we catalog every workload, map the dependencies between them, and measure real resource usage. You get a written report with TCO modeling, the risks we've spotted, and a recommended shape for the migration. That includes any workloads we think you should leave exactly where they are.
We design the destination environment in detail, covering networking, identity, storage tiers, backup, disaster recovery, and observability. All of it goes into infrastructure-as-code, so the design is something your team can review rather than just read about.
We pick one or two low-risk workloads and move them all the way through. The pilot proves out the runbook, surfaces the things nobody anticipated, and gives the team a working template before we scale up.
Workloads move in waves, grouped by their dependencies. Each wave has a rollback gate, runs both old and new in parallel where that makes sense, and uses traffic shifting so your users never notice the switch.
Once everything has moved, we tune performance, lock down access, finish the runbooks, and work alongside your team on day-two operations. We're finished when you can run it without us.
These are outcomes you can measure, not a slide deck. Here's the change you should expect to see.
Predictable workloads usually run 30 to 50 percent cheaper on owned hardware once you amortize the cost, and that saving keeps growing year after year.
On every migration we lead, we use traffic mirroring, parallel running, or canary releases so the cutover stays invisible to the people using your product.
Every phase has a rollback path we've already tested. If the new environment throws us a surprise, we step back. No heroics, no all-hands incident.
The destination is defined entirely in Terraform and Ansible, or Crossplane and Pulumi if you'd rather. Standing up another region or recovering from a disaster takes hours instead of weeks.
Most migrations, in either direction, run somewhere between 6 and 16 weeks from kickoff to final cutover. We always begin with a one to two week assessment, so you have a grounded estimate before you commit to a scope.
Yes, and honestly that's usually the smarter move. We often recommend a hybrid setup where your steady-state workloads run on-prem while the bursty or experimental ones stay in the cloud.
We work out the replication strategy up front. It's usually a bulk transfer followed by continuous sync that runs right up to cutover. No workload moves until we've verified that its data is consistent at the destination.
We run the old and new environments in parallel and shift traffic gradually with canary or blue/green patterns. Most cutovers we lead have zero scheduled downtime, because the switch happens at the load balancer or DNS layer once the destination is proven healthy.
Every phase has a rollback gate that we test before we rely on it. If a wave throws a surprise, we drain traffic back to the source environment, which is still running, and regroup. You never get stuck in a half-migrated state with no way back.
We map your legal and contractual obligations to specific regions, jurisdictions, or owned hardware before designing the target. Where regulators require it, we keep regulated data on-prem or in a sovereign region while less sensitive workloads stay in the cloud.
During assessment we model hardware amortization, power, cooling, bandwidth, licensing, and the engineering time to operate each option over three to five years. You see the full picture, not just the headline cloud bill versus a rack of servers.
We start with one or two low-risk, low-dependency workloads as a pilot to prove the runbook. After that, workloads move in waves grouped by their dependencies, with the most business-critical systems sequenced once the process is well understood.
The usual risks are hidden dependencies, data inconsistency, and performance surprises at the destination. We surface dependencies during assessment, verify data consistency before every cutover, and load-test the target so capacity issues show up in a rehearsal rather than in production.
Largely yes. Most of the work happens at the infrastructure, network, and data layers, which our team handles. We only need your application teams for a few targeted checkpoints, such as validating that a service behaves correctly at its new home.
Tell us where things stand today. We'll get back to you within one working day with a straight read on scope, timeline, and budget. There's no commitment attached.
Office
Surabaya, Indonesia
Starting price
From USD 4,000
Typical projects: USD 4,000–25,000
