Kimbodo Launch is the deployment engine that stands up secure, enterprise-ready platforms on AWS, Azure, and Google Cloud. You bring the cloud account and the architecture standards. Launch provisions the stack — identity, networking, the product itself, and the operational layer around it — with the same pattern every time.
Twenty-two products across data science, machine learning, business intelligence, orchestration, observability, security, and developer tooling. One landing page. One operating model.
Most organizations already know which platform they want. The delay is everything around it: VPC design, identity, secrets, storage, backups, monitoring, package policy, and the first production-shaped environment that security will actually approve. Kimbodo Launch encodes that work so a data or platform team can move from license to users without waiting on a custom build every time.
The same Launch pattern lands on AWS, Azure, and Google Cloud. Compute, identity, and storage map to native services in each cloud so you keep the account, the IAM model, and the exit path.
Access control, secrets, network boundaries, and auditability are designed in before the first user logs in. Launch is built for teams that already have a security review, not teams hoping to skip one.
A fully configured Posit, Airflow, or observability stack does not need a six-week internal project. Connect the cloud, confirm the architecture, and Launch provisions a production-shaped environment.
When every department stands up its own snowflake install, support and compliance collapse. Launch keeps environments consistent across regions, business units, and future refreshes.
Each product below is something Kimbodo Launch can provision with a best-practice architecture: identity, networking, storage, and operations included. Posit has its own landing page because it is the platform we deploy most often. Everything else lives here so the catalog stays in one place.
The Posit stack is the governed home for R and Python work: develop in Workbench, publish on Connect, and pin packages through Package Manager. Open the dedicated Posit landing page from any card in this section.
Governed IDE for R and Python teams
Posit Workbench is the enterprise workspace for data science. Teams develop in RStudio, Jupyter, and VS Code on shared, policy-controlled infrastructure instead of unmanaged laptops. SSO, resource limits, and session management are part of the product — Launch adds the cloud, identity, and operational layer so Workbench is ready for a security review, not just a demo.
Posit landing pagePublish apps, APIs, and documents
Posit Connect is the production server for Shiny, Streamlit, Dash, Quarto, R Markdown, Plumber and FastAPI, and notebooks. Analysts push from Workbench; Connect handles hosting, access control, scheduling, and content versioning. Launch deploys Connect next to Workbench so publishing is a workflow, not a second infrastructure project.
Posit landing pageCurated R and Python repositories
Posit Package Manager gives you a controlled CRAN, PyPI, and Bioconductor mirror, plus internal package feeds. You pin versions, serve air-gapped environments, and stop every project from resolving dependencies off the public internet. Launch installs Package Manager as the third leg of the Posit platform so Workbench and Connect consume the same approved libraries.
Posit landing pageTraining, tracking, and productionizing models needs more than a notebook server. These platforms cover Kubernetes-native pipelines, experiment tracking, and enterprise AutoML.
ML on Kubernetes
Kubeflow is the open-source machine learning toolkit for Kubernetes. It gives you notebooks, pipelines, training operators, and model serving on the same cluster that already runs the rest of the platform. Launch stands up Kubeflow with the cluster, storage, and identity work that usually stalls an in-house install.
Experiment tracking and model registry
MLflow is the open-source standard for the ML lifecycle: tracking runs, packaging models, and promoting them through a registry. It sits beside whatever you use to train — Kubeflow, H2O, DataRobot, or a custom job — so experiments are comparable and production models have a lineage. Launch deploys a durable MLflow backend, not a laptop-local store.
Enterprise AI platform
DataRobot is an enterprise AI platform for automated machine learning, time series, and governed production models. It is built for organizations that need business users and data scientists on the same platform, with MLOps and compliance controls included. Launch provisions the surrounding cloud, identity, and network so a DataRobot environment matches the rest of your estate.
Open-source and enterprise ML
H2O.ai covers scalable open-source machine learning (H2O-3) and the Driverless AI stack for automated feature engineering and model building. It is a common choice when teams want in-cluster training at scale without surrendering the model to a black box. Launch deploys H2O onto your cloud with the compute, storage, and access pattern your security team expects.
Dashboards and self-serve questions belong next to the platforms that produce the data — not on a separate, unmanaged BI server.
SQL-native open-source BI
Apache Superset is an open-source business intelligence server for SQL-speaking teams. Analysts explore warehouses, build dashboards, and share charts without buying a closed BI suite. Launch deploys Superset with a production database, cache, and SSO so it is a platform, not a Docker compose on someone’s workstation.
Self-serve questions and dashboards
Metabase is the approachable BI tool: people who are not SQL-first can still ask questions, save dashboards, and subscribe to results. It is a strong fit when you want adoption outside the data team. Launch installs Metabase against your warehouse with identity and backup already in place.
Pipelines fail in production for operational reasons — schedulers, secrets, concurrency, and observability — not because someone forgot a DAG. Launch treats the orchestrator as a platform.
The standard workflow orchestrator
Apache Airflow is the most widely deployed open-source orchestrator. Directed acyclic graphs describe batch pipelines, with retries, sensors, and a mature operator ecosystem. Launch provisions Airflow with a real metadata database, a proper executor, and the secrets and logging backends that a production scheduler needs.
Dynamic workflows, hybrid execution
Prefect is a modern orchestrator built around Python flows, dynamic graphs, and hybrid execution — work can run in your cloud while the control plane stays managed or self-hosted. Teams choose it when Airflow’s static DAG model is too rigid. Launch deploys Prefect into the same governed network and identity pattern as the rest of the catalog.
Asset-oriented orchestration
Dagster treats data assets — tables, models, files — as the unit of orchestration rather than tasks. That makes lineage, testing, and “what depends on this?” first-class. Launch stands up Dagster with persistent storage and access control so software-defined assets run in an environment operations can support.
A launched platform that nobody can see is not production. These products cover telemetry pipelines, metrics, dashboards, and the instrumentation standard underneath them.
Observability pipeline
Cribl Stream, Edge, Lake, and Search sit in front of your destinations. You route, reduce, enrich, and replay telemetry instead of paying to store every raw event in every tool. Launch deploys Cribl into your cloud so the pipeline is part of the same identity and network design as the systems it observes.
SaaS metrics, traces, and logs
Datadog is the commercial observability suite: infrastructure metrics, APM, logs, security monitoring, and synthetic tests in one product. Launch does not replace Datadog; it wires agents, identity, and the surrounding environment so a new platform appears in Datadog the day it is launched, not weeks later.
Open-source metrics and alerts
Prometheus is the Kubernetes-native metrics system: pull-based scrapes, PromQL, and Alertmanager. It is the default way cloud-native platforms expose health. Launch deploys Prometheus with durable storage and alert routing so it is an operations service, not a sidecar that disappears with a pod.
Dashboards across every data source
Grafana is the visualization layer teams already know: Prometheus, Loki, Tempo, Elasticsearch, SQL, and cloud monitors in one set of dashboards. Launch installs Grafana with SSO and provisioned data sources so the first dashboard is not “admin / admin” on a public IP.
Vendor-neutral instrumentation
OpenTelemetry is the standard for traces, metrics, and logs. Instrument once; export to Prometheus, Grafana, Datadog, or whatever comes next. Launch includes the collector and the conventions so new platforms emit telemetry in a shape your observability stack already understands.
Identity, secrets, and an internal developer portal are not optional extras. They are the control plane the rest of the catalog hangs from.
Open-source identity and SSO
Keycloak is an open-source identity provider: OpenID Connect, SAML, user federation against LDAP or Active Directory, and fine-grained authorization. Launch deploys Keycloak so Workbench, Connect, Grafana, GitLab, and the rest of the catalog can share one SSO story instead of seven local admin tables.
Secrets, encryption, dynamic credentials
HashiCorp Vault is the secrets platform: static secrets, dynamic database credentials, encryption as a service, and PKI. Applications fetch credentials at runtime instead of storing them in repos and tickets. Launch installs Vault with unseal, audit, and policy patterns that match the environments it protects.
Internal developer portal
Backstage, originally from Spotify, is the portal for service catalogs, tech docs, and golden-path templates. It is how a platform team shows engineers what already exists and how to create the next thing the approved way. Launch deploys Backstage against your identity provider and catalog sources so the portal is live when the first service is.
Source control and a governed editor complete the loop. Launch can stand these up in the same cloud and identity domain as the data platforms.
Git, CI/CD, and DevSecOps
GitLab is the DevSecOps platform: repositories, merge requests, CI/CD, a container registry, and security scanning in one product. Self-managed GitLab is the usual choice when source code cannot leave the estate. Launch provisions GitLab with runners, storage, and SSO so pipelines run next to the platforms they deploy.
Browser-based VS Code
VS Code Server (code-server) puts Visual Studio Code in the browser on a machine you control. Data scientists and engineers get a familiar editor against cloud compute, datasets, and GPUs without copying data to a laptop. Launch deploys it behind your identity provider, alongside Workbench or as a standalone remote workspace.
Link the AWS, Azure, or Google Cloud account where the platform should live. Launch works in your tenancy. You keep billing, IAM, and the data plane.
Confirm networking, identity, environment separation, and the operational standards your security and platform teams already require. Launch maps those decisions onto a known pattern instead of inventing a new one.
Kimbodo provisions the product and the layer around it — compute, storage, access, secrets, and monitoring — as a production-ready environment. Users can sign in. Operations can see it. The next environment can look the same.
Buying Posit, Cribl, or a warehouse license does not put a user on a governed workspace. Cloud, Linux, networking, and security teams are already booked. Kimbodo Launch is the practice we run for that gap: the same people who deploy Posit for customers also deploy the rest of this catalog, with one architecture language.
Tell us which product, which cloud, and what “production-ready” means in your estate. We will map it onto a Launch pattern and tell you how fast it can exist.
Tell us what you are building. We usually reply within one business day.
