Data Infrastructure & Operations Ownership: Who Actually Runs Your Data?

The Infra–Ops decision framework

Where does the platform run and who is responsible when something breaks?‍

If you are running a modern data stack with Snowflake, BigQuery, Redshift, a lakehouse on S3 or ADLS, dbt models, Airflow or Dagster for orchestration and BI tools like Looker or Tableau, this matters a lot. It affects how you handle:

• data governance and PII
• GDPR and SOC 2
• data quality and observability

Behind all of this there are only two variables:

• Infrastructure: whose cloud account or network the platform lives in
• Operations: who runs it day to day, watches the dashboards and fixes incidents

Combine those and you get three realistic models that Mitzu supports.

1. Mitzu-owned infrastructure & Mitzu-owned operations

In the first model you use Mitzu Cloud at app.mitzu.io.

Mitzu hosts the platform in its own cloud environment. Our team operates the service: autoscaling, uptime, monitoring, upgrades, security patches, schema migrations. You bring your data warehouse or lakehouse and connect it.

For most teams this means:

• Connect Snowflake, BigQuery, Redshift or your lakehouse over the internet
• Point Mitzu at your existing ELT / ETL pipelines
• Start tracking query performance, cost, lineage and data quality without touching Kubernetes, Terraform or IAM policies

If your warehouse is reachable from the internet, setup is straightforward. If it is not, we can look at a private connection such as VPC peering or a private link, as long as it fits your security guidelines.

Teams that are growing quickly or already have a large datasets tend to choose this. They want better visibility into their data pipelines and warehouse usage, but they do not want to hire another SRE just to run one more service. They can focus on building dbt models, fixing broken DAGs and delivering analytics, not patching nodes.

2. Customer-Owned Infrastructure & Mitzu-Owned Operations (BYOC)

The second model keeps operations on the Mitzu side, but moves the infrastructure into your cloud.

We deploy a Private Mitzu Instance directly into your AWS, GCP or Azure account. It runs in your VPC, next to your warehouse or lakehouse, under your own IAM and network rules.

From a data engineering and security point of view, this has a few clear benefits:

• Data never leaves your cloud account
• You keep control over VPC layout, subnets, security groups, peering and VPNs
• You can enforce your own RBAC, SSO, audit logging and encryption standards
• The instance can be completely isolated from the public internet if required

Operationally, nothing changes on your side. Mitzu still takes care of the platform itself: deployments, upgrades, health checks, metrics, alerting.

Your platform is responsible for network connectivity and any infrastructure as code. We then plug into it, help you validate the setup and make sure Mitzu can sync to your warehouse, orchestration layer and BI tools without breaking your privacy policies.

This model is very attractive in regulated environments like fintech, edtech, healthcare or public sector, where data residency and strict governance around PII and financial data are non-negotiable. You get a managed product with data observability, lineage and cost insights, but you keep the workload inside your VPC.

3. Customer-Owned Infrastructure & Customer-Owned Operations (Self-Hosted)

The third model is for organisations that want full control over both infrastructure and operations.

Mitzu is delivered as software, and you:

• deploy it into your own cluster or other runtime
• manage it with your existing CI or CD pipelines
• wire it into your own logging, metrics and incident management stack

In practice, your platform engineering team treats Mitzu like any other production microservice. It sits next to your Airflow or Dagster cluster, your dbt runner, your reverse proxies and internal dashboards.

The upside is obvious: you decide everything. You can tweak resource limits, align with internal security baselines, integrate with your existing data catalog or governance tools, and keep everything under your existing infrastructure as code.

The trade off is that you also carry the operational cost. Self hosting makes sense for larger organisations that already run many internal data services and have the engineers to back it up.

4. Mitzu-Owned Infrastructure & Customer-Owned Operations

There’s one combination we don’t offer: Mitzu infrastructure with customer-run operations.

If we host it but you try to operate it, nobody clearly owns uptime and incident response. That usually ends badly.

Conclusion

Your infra–ops model defines your data platform’s scalability, compliance posture, and operational reliability. Mitzu supports flexible deployment options so teams can choose the environment that aligns with their growth stage, governance needs, and technology stack.