Google Cloud Run Experts in Munich
matched in minutes from over 15,000 CVsHire experts who deploy containerized applications, connect Cloud Run with Google Cloud services, and build reliable serverless delivery pipelines. FRATCH matches you quickly and precisely with vetted, available freelancers for your project.
Meet FRATCH Experts in Munich, who have recently used Google Cloud Run
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Serge K.
Last position:
Data Scientist & MLOps (Machine Learning Operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
- Development of feature store and data synchronization between environments
- Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
- Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
- Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
- Development of Copilot Agent Skills
Siegfried-Thor B.
Last position:
Independent AI Risk Auditor & Software Architecture Reviewer at Confidential Client
Conducted an independent technical assessment of an AI-enabled software platform, combining AI risk auditing, cybersecurity analysis and software architecture review. Reviewed application components, APIs, data flows, LLM integrations, retrieval-augmented generation, multi-agent orchestration, authentication, tenant isolation, dependency vulnerabilities, AI governance and model lifecycle controls. Authored a technical audit report with a structured risk register, source-code evidence, regulatory references, corrective actions, AI system inventory, risk classification, remediation roadmap and Key Risk Indicators.
Technologies: TypeScript, JavaScript, React, Next.js, Node.js, REST APIs, MongoDB, Redis, AWS serverless services, OAuth 2.0 / OpenID Connect, LLM APIs, RAG, vector search, multi-agent orchestration, Model Context Protocol, C4 architecture modelling, threat modelling, NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, OWASP guidance, MITRE ATLAS
Mario G.
Last position:
Software and Data Engineer at Plexify GmbH
- Architecture, design and development of an MVP application for a provider of specialized travel experiences
- Technologies: Python, FastAPI, Firestore, Firebase, Docker
Nikolay T.
Last position:
Senior Cloud Data Architect at Cloudreach/Eviden (an ATOS Company)
- Architected a self-service Google Kubernetes Engine (GKE) platform for a major financial institution (Commerzbank), enabling 1000+ users across hundreds of product teams to autonomously provision resources and significantly accelerate development cycles.
- Designed a data-product-oriented platform architecture for the UK Department for Transport (DfT) to serve over 1500 direct end-users and numerous connected third-party systems, enhancing data accessibility and governance.
- Drove business growth by developing the strategic roadmap for the 'One Cloud' business line, targeting a 10% revenue increase.
- Served as a key member of the CTO Authority, providing strategic guidance on internal cloud initiatives and best practices.
Alexander S.
Last position:
Full-Stack Developer (Java/Kotlin/Angular) at Hypoport (Vergleich.de)
Further development of the vergleich.de website
Development of new and improvement of existing backend services as microservices
Deployment via TeamCity and GitHub Actions
Development of monitoring tools
Development of REST APIs
Database migration from Oracle to PostgreSQL
Java and Kotlin (including Spring, Spring Boot, JPA, Hibernate)
Angular (TypeScript) and JavaScript
IntelliJ IDEA and WebStorm
Apache Maven
Oracle DB (and SQL Developer), MongoDB, PostgreSQL DB
Git (GitLab, GitHub, BitBucket)
TeamCity
Jira
Docker
JUnit, Mockito, jasmin
Discover over 15,000 top freelancers
Statistics of experts using Google Cloud Run
Aggregated from the professional profiles of matched freelancers.
Experience
20 years
Position duration
3 years
Positions per freelancer
13
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Government and Administration
Certification focus areas
Information Technology, Business Intelligence, Finance
Bachelor's degree or higher
100%
Master's degree or higher
80%
Doctorate
20%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Munich using Google Cloud Run
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Google Cloud Run experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (83%)
- Banking and Finance (67%)
- Government and Administration (67%)
- Retail (67%)
- Automotive (50%)
- Education (50%)
- Insurance (50%)
- Media and Entertainment (50%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Cloud Run does
Google Cloud Run is a fully managed service for running containerized applications on Google Cloud. It starts containers when requests arrive, scales them with demand, and removes the need to manage servers or Kubernetes clusters for many workloads. Teams use it for web APIs, event handlers, internal tools, scheduled jobs, and lightweight background services.
Where it fits
Cloud Run works well when a service should be packaged as a portable container and exposed through HTTP or triggered by events. Typical uses include:
- Deploying REST and GraphQL APIs
- Hosting web applications and backend services
- Processing Pub/Sub events and Cloud Tasks
- Running scheduled workloads with Cloud Scheduler
- Creating previews and isolated environments
Google Cloud ecosystem
Strong specialists connect Cloud Run with Artifact Registry, Cloud Build, Cloud Deploy, Secret Manager, Cloud SQL, Firestore, and Memorystore. They also configure IAM, service accounts, VPC networking, load balancing, logging, tracing, and monitoring. A sound design accounts for stateless containers, startup behavior, concurrency, timeouts, revisions, and controlled traffic shifts.
When expertise helps
Companies often bring in freelance specialists when a containerized service must move from local development to a secure production setup. Expertise is valuable during a migration from virtual machines or a Kubernetes environment, when delivery pipelines need restructuring, or when several Google Cloud services must work together. For Munich teams, remote delivery can work well when documentation, access rules, and communication are clear; local on-site collaboration may help during workshops or regulated projects.
Delivery and operations
A capable professional defines the container contract, health checks, environment configuration, deployment stages, and rollback path. They set appropriate authentication and authorization, separate public and private services, and prevent secrets from entering images or logs. They also make observability useful by connecting request logs, traces, alerts, and service-level signals to operational decisions.
Choosing a strong specialist
Look for practical evidence with production Cloud Run services rather than container knowledge alone. Ask how the professional handles cold starts, concurrency, retries, idempotency, graceful shutdown, and database connections. Strong specialists explain trade-offs against Google Kubernetes Engine, Compute Engine, and other serverless options, then leave behind reproducible infrastructure, clear runbooks, and maintainable pipelines.
Frequently asked questions
Curious about Google Cloud Run? Here are the answers that come up again and again.
Google Cloud Run runs applications packaged as containers without requiring a team to manage the underlying servers. Companies use Cloud Run for HTTP APIs, websites, event-driven processing, scheduled tasks, and internal services that need flexible scaling.
Cloud Run is usually simpler when services can remain stateless and the team wants Google to manage cluster operations. Google Kubernetes Engine offers deeper control over scheduling, networking, and workloads, but it also requires more platform design and ongoing maintenance.
A strong Cloud Run specialist should understand Docker, Google Cloud IAM, Artifact Registry, Cloud Build, networking, and observability. Experience with Cloud SQL, Pub/Sub, Secret Manager, Terraform, and CI/CD is also useful when the service is part of a wider production system.
For Google Cloud Run, the right level of experience depends on the system’s risk and integration depth, not just the container deployment itself. A simple service may need focused delivery expertise, while a production platform with private networking, event processing, data services, and compliance controls calls for broader Google Cloud experience.
Cloud Run is well suited to remote collaboration because deployments, configuration, logs, and infrastructure can be reviewed through shared repositories and cloud tooling. Munich-based companies should define access procedures, documentation standards, meeting expectations, and whether German-language communication is needed alongside English.
Cloud Run is a strong choice when the application fits a container model and the company wants managed deployment and automatic scaling. Compute Engine is more suitable when a workload needs persistent host control, custom operating-system configuration, or software that does not fit Cloud Run’s execution model.
Ask a Cloud Run specialist to explain deployment safety, authentication, networking, observability, failure handling, and cost controls in concrete terms. Review the infrastructure definitions, pipeline design, rollback process, documentation, and tests rather than judging only whether the service responds successfully.
Cloud Run is the commonly used short name for Google Cloud Run, Google’s managed container execution service. Freelancers working with it should be comfortable with the broader Google Cloud ecosystem because real projects often connect Cloud Run to identity, storage, databases, messaging, delivery automation, and monitoring.
The average hourly rate of freelancers in Munich, Germany who have used Google Cloud Run in their recent projects is 95 €, which corresponds to a daily rate of about 758 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Google Cloud Run in their recent projects, 100% hold at least a Bachelor's degree, 80% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Munich, Germany who have used Google Cloud Run in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers in Munich, Germany who have used Google Cloud Run in their recent projects are German (100%), English (100%), and French (33%).
The most common industries among freelancers in Munich, Germany who have used Google Cloud Run in their recent projects are Information Technology (83%), Banking and Finance (67%), and Government and Administration (67%).
The most common business areas among freelancers in Munich, Germany who have used Google Cloud Run in their recent projects are Information Technology (100%), Product Development (83%), and Business Intelligence (67%).
Main locations of FRATCH Experts, who have recently used Google Cloud Run
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Countries:
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
