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Quick answer
Python & FastAPI Development
UnlockLive IT builds production Python backends and FastAPI microservices for North American teams that need high throughput, clean APIs, and a backend that can power both traditional product features and modern AI workloads. Our Python engineers ship REST and GraphQL APIs, async event-driven services, AI inference endpoints, ETL pipelines, and full Django web applications. Every project includes typed Pydantic schemas, automated pytest coverage, OpenAPI documentation, and CI/CD from day one.
What we build
REST and GraphQL APIs:FastAPI, Flask, or Django REST Framework with full OpenAPI 3 documentation, request validation via Pydantic, and JWT/OAuth2 authentication.
Microservices and event-driven systems:Decoupled services using Celery, RabbitMQ, Kafka, or AWS SQS. Async/await throughout for maximum throughput.
AI and ML inference APIs:Production endpoints serving LLMs, embeddings, classifiers, and computer-vision models with batching, caching, and observability built in.
Data pipelines and ETL:Airflow, Prefect, or custom Python pipelines moving data between data warehouses, OLTP databases, and third-party SaaS APIs.
Internal tools and automation:Scripts, CLI tools, scheduled jobs, and admin dashboards (Streamlit, FastAPI + HTMX) that automate operations work.
Backend modernization:Migrating legacy Django, Flask, or Rails monoliths to FastAPI microservices with zero-downtime cutovers.
Our Python technology stack
Frequently asked questions
Why choose FastAPI over Django or Flask?
FastAPI has become the default Python web framework for new API projects in 2024 and 2025 because of its native async support, automatic OpenAPI documentation, and Pydantic-based request validation. It is 2-3x faster than Flask for typical workloads and dramatically better for AI inference endpoints. Django is still our recommendation when you need an admin interface, ORM, and templating out of the box. Flask remains a good choice for very small projects or where existing team expertise is in Flask.
Can you integrate Python services with our existing Node.js or .NET stack?
Yes. We routinely build Python microservices that sit alongside Node.js, Java, or .NET applications, communicating through REST, gRPC, or message queues. We can also containerize and orchestrate the entire polyglot stack on Kubernetes or AWS ECS.
Do you offer dedicated Python developers on a monthly retainer?
Yes. Our most common engagement model for Python work is a dedicated developer or team on a monthly retainer, with Toronto-based project management. This works well for teams that need ongoing capacity rather than fixed-scope projects. Minimum engagement is typically 3 months at 40 hours/week.
Can FastAPI handle high traffic in production?
Absolutely. FastAPI deployed on uvicorn behind a reverse proxy can comfortably serve tens of thousands of requests per second on a single mid-sized instance. We have shipped FastAPI services handling more than 50 million API requests per day, with horizontal scaling on Kubernetes and asynchronous database access via SQLAlchemy 2.0.
Do you handle AI model serving and MLOps?
Yes. We build inference APIs around LLMs (OpenAI, Anthropic, open-source models via vLLM or TGI), embedding models, and traditional ML models. We handle prompt versioning, request batching, semantic caching, evaluation pipelines, and cost monitoring. For full MLOps including training pipelines, see our AI/ML Development page.
Need a Python backend that scales?
Tell us about your API, data pipeline, or AI inference use case. Book a free strategy call with our Toronto team — we will respond within one business day.
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