FastAPI Experts in Frankfurt
matched in minutes from over 15,000 CVsHire experts who deliver typed Python APIs, async services, and production-ready integrations with tools such as Pydantic, Starlette, and SQLAlchemy. FRATCH finds the right vetted, available freelancer through precise AI matching.
Meet FRATCH Experts in Frankfurt, who have recently used FastAPI
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open-source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self-hosted solutions
- Development of reusable agentic workflows and business applications that enable non-technical employees to solve business problems independently
- Implementation of nine business applications with Single Sign-On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
Techstack: Python, Streamlit, Anthropic SDK (Claude), Azure, Linux, PostgreSQL, MS SQL, Angular
Felix T.
Last position:
Data Engineering and Power BI at Wesernetz Bremen
Independently built a data platform with cross-system process monitoring.
- Designed the Bronze/Silver/Gold architecture and Python-based pipelines for diverse operational sources.
- Cleaned, harmonized, and validated data; processed and stored it in Parquet format.
- Tracked data history and built business-focused fact and dimension models for traceable reporting.
- Automated data delivery with Python and Power Automate (desktop and cloud); used SharePoint for archiving and data access.
- Developed the Power BI solution from data preparation through to visualizing process statuses, work progress, and deviations.
Technologies: Python, SQL, Parquet, Power BI, Power Query, Power Automate, SharePoint, Copilot
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Kevin M.
Last position:
Backend & Infrastructure Engineer at Mileo Systems GmbH
- Engineered production-ready Azure environments using Terraform, ensuring consistent infrastructure parity across VNets and Resource Groups
- Implemented Microsoft Fabric tenant and workspace architecture for multi-stage Medallion data processing pipelines
- Designed secure data pathways using Managed Private Endpoints for isolated Azure Storage access
- Managed Service Principals and authentication tokens for secure REST API integrations
Axel B.
Last position:
Project lead for introducing Okta as an IAM system at Fritz Schäger GmbH & Co KG
- Project management
- Building an internal IAM team
- Okta
- Project management
- IAM processes
Delly F.
Last position:
Dad of 2 daughters at Family
Jens D.
Last position:
Product Owner & Senior Data Scientist at Legal Tech
- Led an international team of six developers in a Scrum environment
- Defined strategic goals for the project in coordination with stakeholders and the development team
- Prompt engineering for language models to improve the accuracy and relevance of generated responses
- Implemented LangChain components for a RAG chatbot to answer legal questions
- Technologies: GPT-4, LangChain, Python (Pandas, sklearn, streamlit), Docker, GitLab, ChromaDB
Peka C.
Last position:
Data Warehouse Project for a Zoo at Alfatraining
- Created a complete entity-relationship model (ERM) for the future operational database
- Implemented the model using an RDBMS
- Designed and implemented a star schema for inventory management
Leonard H.
Last position:
Freelance Software Engineer & Cloud Architect at Leonard Hußke - IT Solutions
- Evaluation of potential providers (Snowflake vs Databricks) and design of the analytics data platform using Databricks
- Data storage and ingestion layer with Amazon S3
- Creation of ETL processes and data transformations with AWS Glue and Databricks Notebooks
- Orchestration with AWS Glue Workflow, Databricks Workflow and Databricks DLT
- Processing of unstructured data, including text, images and video
- Databricks workspace setup and administration
- Implementation of a medallion architecture to ensure data quality
- Evaluation of potential BI tools (Power BI, AWS Quicksight, Tableau)
- Establishment of MLOps using MLFlow
- Introduction of data governance and data lineage using Unity Catalog
Discover over 15,000 top freelancers
Statistics of experts using FastAPI
Aggregated from the professional profiles of matched freelancers.
Experience
10 years (Germany: 13 years)
Position duration
1.5 years (Germany: 1.9 years)
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
50% (Germany: 71%)
Doctorate
13% (Germany: 9%)
Certifications per freelancer
5 (Germany: 3)
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 97%)
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 Frankfurt 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 Frankfurt using FastAPI
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.
FastAPI 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 (100%)
- Professional Services (56%)
- Banking and Finance (44%)
- Government and Administration (44%)
- Education (33%)
- Food and Beverage (33%)
- Advertising (22%)
- Energy (22%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What FastAPI is
FastAPI is a modern Python web framework for building high-performance APIs and backend services. It combines type hints with automatic validation, interactive OpenAPI documentation, and asynchronous request handling. Teams use it for REST APIs, internal services, data products, and machine learning applications.
Typical applications
FastAPI fits projects where clear contracts, speed, and maintainable Python code matter.
- REST and event-driven backend services
- Authentication, authorization, and user management APIs
- Machine learning inference and data processing endpoints
- Microservices connecting web, mobile, and business systems
Ecosystem and tooling
Strong FastAPI specialists work across its surrounding Python ecosystem. They use Pydantic for schemas and validation, Starlette for the web foundation, and Uvicorn or Gunicorn for serving applications. Database work may involve SQLAlchemy, SQLModel, PostgreSQL, Redis, or document stores, while pytest supports automated testing.
When companies need help
Freelance expertise is useful when an existing Python service needs a reliable API layer, or when a team is moving from a prototype to production. Specialists can establish project structure, define contracts, improve response times, connect external systems, and prepare services for containerized deployment. In Frankfurt, this can support local teams through on-site workshops, remote delivery, or a hybrid model.
Delivery and operations
Production work goes beyond writing route handlers. A capable professional designs dependency injection, background tasks, error handling, logging, security controls, and versioned interfaces. They also connect FastAPI services to CI/CD pipelines, Docker, Kubernetes, cloud infrastructure, monitoring, and observability tools without making the codebase difficult to operate.
Signs of strong expertise
Look for professionals who explain trade-offs between synchronous and asynchronous code and can show how they test API behavior.
- Clear OpenAPI contracts and consistent error responses
- Sensible schema design with Pydantic and typed Python
- Secure authentication, authorization, and input handling
- Load-aware database access and reliable deployment practices
- Documentation that helps other teams use the service
For collaboration with Frankfurt-based stakeholders, clear communication in the expected working language matters as much as technical delivery. A strong specialist leaves behind understandable code, useful tests, and an API that can evolve safely.
Frequently asked questions
Quick answers to the questions that come up most around FastAPI.
FastAPI is used to build REST APIs, async backend services, microservices, and endpoints for data or machine learning systems. It is a good fit when teams need typed request handling, automatic API documentation, and efficient Python execution.
FastAPI provides built-in type-driven validation, OpenAPI documentation, and strong support for asynchronous endpoints. Flask is more minimal and flexible, while Django offers a broader full-stack structure with built-in conventions. The right choice depends on the service boundary, existing codebase, and operational needs.
FastAPI work often requires Python typing, Pydantic, SQLAlchemy, PostgreSQL, pytest, Docker, and API security. Experience with asynchronous programming, cloud deployment, queues, caching, and observability is also valuable for production services.
FastAPI projects vary from a focused API integration to a distributed service with authentication, databases, and deployment automation. Judge the required level by the system's risk and complexity, then look for evidence of shipped services with similar traffic, integrations, testing, and operational demands.
FastAPI work is well suited to remote collaboration because API contracts, pull requests, tests, and deployment pipelines provide clear working interfaces. Frankfurt teams should agree on communication routines, documentation standards, time-zone overlap, and whether workshops or stakeholder sessions require on-site participation.
FastAPI code should have clear schemas, predictable error handling, secure dependencies, useful tests, and maintainable project boundaries. Ask a specialist to explain asynchronous choices, database transaction handling, API versioning, and how the service is monitored after release.
FastAPI can support high-throughput workloads when the application uses asynchronous I/O appropriately and its database, network, and deployment layers are designed for the load. Performance still depends on queries, external services, serialization, infrastructure, and capacity planning rather than the framework alone.
FastAPI specialists should clarify the API contract, Python version, data model, authentication method, deployment target, and ownership of existing services. They should also confirm whether the work involves synchronous legacy code, async integrations, production support, or collaboration with German-speaking stakeholders in Frankfurt.
The average hourly rate of freelancers in Frankfurt, Germany who have used FastAPI in their recent projects is 100 €, which corresponds to a daily rate of about 797 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used FastAPI in their recent projects, 100% hold at least a Bachelor's degree, 50% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used FastAPI in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 1.5 years.
The most common languages among freelancers in Frankfurt, Germany who have used FastAPI in their recent projects are German (100%), English (100%), and French (33%).
The most common industries among freelancers in Frankfurt, Germany who have used FastAPI in their recent projects are Information Technology (100%), Professional Services (56%), and Banking and Finance (44%).
The most common business areas among freelancers in Frankfurt, Germany who have used FastAPI in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (89%).
Main locations of FRATCH Experts, who have recently used FastAPI
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.
Berlin 
Hamburg 
Munich 
Cologne 
Dortmund 
Nuremberg Request a free demo
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