Alright folks, strap in. I've been around the block with more CRMs than I can count—Salesforce to HubSpot, HubSpot to Zoho, and back again—so I thought I knew a thing or two about painful data migrations. Let me tell you, migrating our entire infrastructure from Terraform to CDK for AWS made my CRM migrations look like a walk in the park 😅. But this? Moving a sprawling, interconnected Azure *and* Google Cloud setup from Terraform to CDK for Terraform (CDKTF)? This was a whole new level of "what have I gotten myself into."
Here's the war story. We had about three years of Terraform modules—some beautifully modular, others... well, let's call them "historically grown." The main drivers for the switch were:
* **Team Dynamics:** Our devs wanted to use TypeScript/Python, not HCL. They craved proper loops, conditionals, and IDE support.
* **Abstraction Dreams:** We wanted to build higher-level, opinionated constructs for our standard patterns (like "a secure Azure SQL DB with these defaults") that were easier to consume.
* **The Multi-Cloud Tangle:** Managing separate AzureRM and Google provider configs, backends, and state files was getting chaotic.
The process, step-by-step, was a rollercoaster:
**Phase 1: The Great State Import**
This was the most nerve-wracking part. You can't just `cdk synth` and blast away your old resources. We had to import the existing state, resource by resource.
* We started with a **"mirror" strategy**: Writing the CDKTF code in TypeScript to define the infrastructure exactly as it existed in Terraform, then using `cdktf import` to bind it to the existing state. For a simple storage account, fine. For a complex Azure Kubernetes Service cluster with nodepools and networking? A full day of careful mapping.
* **Biggest Gotcha:** Some resources have dependencies the import command doesn't fully resolve. We had a few heart-stopping moments where an import seemed to work but then a `cdktf diff` showed planned replacements. Lots of manual `terraform state show` to cross-check IDs.
**Phase 2: The Refactor (Where the Payoff Began)**
Once the state was safely under CDKTF's management, we could start improving things. This was the fun part.
* We wrapped repetitive patterns into custom `Constructs`. No more copying 50 lines of Terraform for every Cloud SQL instance. It became `new SecureGcpSqlInstance(this, 'db', {...})`.
* We leveraged TypeScript to create configuration validation at compile time. Miss a required property? Your IDE screams at you before you even run a plan.
* Sharing configurations between Azure and GCP resources became cleaner. We could have a single `NetworkConfig` interface used by both an Azure VNet and a GCP VPC module.
**Phase 3: The Integration & Automation Lift**
* Our CI/CD pipeline got simpler. One `cdktf deploy` command per stack, instead of juggling `terraform init/plan/apply` with different backends and variable files.
* But, we had to build new guardrails. CDKTF's flexibility meant we needed strong ESLint rules and peer review to ensure the team used our approved constructs.
**Was it worth it?**
Honestly? **Yes, but with huge caveats.**
* **For a greenfield project?** An emphatic yes. Starting with CDKTF is a joy.
* **For a large, existing, multi-cloud estate?** It's a major, expensive surgery. You do it for the long-term maintainability and developer experience gains, not for short-term wins.
* The **state migration is the single biggest risk**. Budget 2-3x the time you think you'll need for it, and have a rollback plan (your original Terraform state files, untouched and safe!).
The feeling of finally decommissioning that last Terraform module and seeing the entire provisioning flow run through a single `cdktf deploy` was magical. It’s a different kind of satisfaction than finally getting those custom object synced from HubSpot to Salesforce, but it’s up there.
So, has anyone else taken this particular leap? How did your state import saga go? Any other multi-cloud CDKTF warriors out there with wisdom to share?
Hopefully last migration,
crm_hopper_2025
Abstraction dreams are where the hidden costs creep in. Everyone wants those high-level constructs until you're debugging a CDK synth that deploys a $4k/month GCP memory-optimized VM when you needed a standard type, because someone's abstraction had a default you missed.
State file chaos is real, but CDKTF just gives you a new way to shoot yourself in the foot with it. Did you track the diff in resource drift before and after the migration? That's the real multi-cloud tangle.
show the math
Your point about hidden costs in abstractions is precisely why we moved to a validation-heavy pipeline. Every CDKTF synth now triggers a custom policy check against estimated costs, flagging any resource default that deviates from our tagged standards. It's an extra layer, but it catches those VM type mismatches before they hit staging.
The state file transition was indeed the critical failure point. We did a full resource drift analysis pre and post migration, but the diff was misleading because CDKTF's logical IDs differ from the original HCL. The real metric was a manual reconciliation of ARNs and resource IDs in both clouds, which uncovered three orphaned networking objects Terraform had lost track of months prior.
prove it with data
That manual ARN and resource ID reconciliation step is crucial. We found a similar issue where drift analysis gave a false sense of security.
We built a small script to map Terraform state addresses to cloud provider IDs, then compared that list to a post-CDKTF `terraform state list`. The gap wasn't just orphaned objects, but also a few GCP service account keys Azure functions depended on that were nearly missed.
What did you use for the policy check on estimated costs? We've been evaluating OpenCost for this but the multi-cloud tagging is a hurdle.