Python for AI, data pipelines and backends that need it.

We use Python where it's the right tool — AI microservices, ML pipelines, data engineering, FastAPI backends. We'll also tell you when Node.js or Go is a better fit.

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Years Python in production

80+

Python systems deployed

AWS

ML certified engineers

Clutch

Trusted by

What we build with Python

Six use cases where Python is genuinely the right choice — and where the ML and AI ecosystem has no equal.

AI & LLM Microservices

FastAPI services handling LLM orchestration, RAG pipelines, streaming responses, tool calling, and agent execution. Properly engineered — error handling, rate limiting, caching, observability. The backend layer most teams underestimate.

Data Engineering Pipelines

ETL/ELT pipelines that ingest, transform, validate and load reliably. From API integrations to large-scale Spark jobs. Scheduled, monitored, recoverable — not fragile scripts running on someone's laptop.

Machine Learning Services

Training pipelines, feature engineering, model serving APIs, monitoring systems. The engineering layer that gets ML from Jupyter notebook to production SageMaker endpoint.

Django Web Applications

Content-heavy web apps where Django's ORM, admin panel, and auth add genuine value. E-learning platforms, marketplaces, and internal tools where rapid development of complex data models matters.

Real-Time Data Processing

Streaming pipelines processing events as they arrive — IoT sensor data, clickstream analytics, financial transaction monitoring. Low-latency with stateful stream operations.

Automation & Integration

Production-grade Python automation. Document processing, API integrations, scheduled data jobs, browser automation, report generation — with proper logging, error handling, retry logic.

Who hires us

AI and ML teams who need production engineering

You've built a great model or LLM integration. Now you need a production-grade FastAPI service, proper error handling, streaming, cost controls, and CI/CD. We build the engineering layer around your AI work.

Data engineering teams with fragile pipeline debt

Your Airflow DAGs or bash scripts break weekly. We rebuild pipelines with proper orchestration, validation, monitoring, and recovery — pipelines you can trust with your production data.

Startups building AI-native products in Python

Your product's core is an LLM integration, a recommendation system, or a data processing engine. Python is the right call. We build the full backend — not just the interesting AI part.

CTOs evaluating Python vs Node.js for a new system

Python is the right choice for AI/ML-heavy backends. It's the wrong choice for some high-concurrency workloads. We'll tell you honestly before you commit to an architecture.

Fast API-first.
Async-expert.
Honest about limitations.

Four things that separate EB Pearls Python work from a backend that works in staging and falls over in production.

FastAPI as our default — not Flask

FastAPI async-first design, automatic OpenAPI/Swagger documentation, Pydantic data validation, and dependency injection make it the right foundation for modern Python APIs. We've been building FastAPI services since v0.6 and understand its performance characteristics at production scale.

We know when Python is the wrong choice

Python's GIL makes it genuinely unsuitable for certain high-concurrency workloads. Pure Python is too slow for some compute-intensive tasks without NumPy, Cython or Rust extensions. We'll tell you when Node.js, Go, or another language serves you better.

AI-native Python engineering

We build the Python layer that connects applications to LLMs correctly — with streaming, proper error handling on API failures, fallback model logic, context window management, token counting, and cost controls. Most teams underestimate how much engineering is required to make LLM integrations production-reliable.

Async Python expertise

asyncio, async/await, aiohttp, background tasks in FastAPI — we build Python services that handle concurrency properly. This is non-trivial and is where many Python backends fail under load. Our engineers understand the event loop and async database drivers at the implementation level.

Python or Node.js for your backend?

Not a preference — a decision that depends on what your backend actually needs to do.

Dimension	Node.js	Python (EB Pearls)
ML model integration	Requires Python subprocess/API call	✓ Native — PyTorch, scikit-learn, SageMaker SDK
LLM/AI microservices	Works — but Python ecosystem is richer	✓ LangChain, LlamaIndex, Bedrock SDK native
High-concurrency I/O API	✓ Event loop handles it well	✓ FastAPI async handles it equally well
Data engineering pipelines	✗ Node is not the right tool	✓ Pandas, PySpark, Airflow, dbt native
Team expertise	✓ Right for JavaScript teams	✓ Right for data science and AI teams
Start-up to production time	Fast — large ecosystem	Fast — largest AI/ML ecosystem by far

Our Python stack

★ Marks preferred production choices for Australian enterprise deployments.

Web Frameworks

★ FastAPI
Django + DRF
Flask
Celery + Redis/SQS

AI & ML

★ LangChain / LangGraph
LlamaIndex
Hugging Face Transformers
PyTorch / TensorFlow
scikit-learn
XGBoost / LightGBM

Data Engineering

★ Pandas / Polars
★ PySpark / AWS Glue
Apache Airflow
dbt
Great Expectations

Deploy & Test

★ Docker + AWS ECS
AWS Lambda + Mangum
★ pytest
Black / Ruff / mypy
Locust (load testing)

Real systems.
Production metrics.

Two Python deployments tracked over 9+ months in production.

Throughout the app development process the developers ensure their team work closely with us and also ensure the app meet our needs and aligns with our business goals. They also manage effective communication, collaboration and project management. The app is essential and was also delivered on time within budget. We are extremely satisfied.

Dan Rosensweig

Dominic Gallello

The client is delighted with EB Pearls’ ability to deliver within the established deadlines through a professional approach. They use various communication tools to ensure a solid project management experience. The team also praises their overall good service and constant commitment to learning new things.

John Walton

Throughout the app development process, the development team works closely with us and also ensure that the app meets our needs and aligns with our business goals. They also manage effective communication, collaboration, and project management. The app is essential and was also delivered on time, within budget. We were extremely satisfied with their work.

Brett Thome

Hamid Moghadam

We’re extremely impressed with EB Pearls’ work, technical skill, and ability to adapt, iterate, and learn new skills. Their high attention to detail and eternally positive attitude make working with EB Pearls a wonderful experience.

Sarah Scaife

Product owner, BAXTA

We came to EBPearls with a precise design remit, which they took on board and produced for us on time and on budget. It was a pleasure to have our feedback accepted and acted upon swiftly, and any concerns we had addressed promptly. We love our new website, and the accessibility it offers to our diverse audience.

Dr Melanie Heyworth

CEO & Founder, Reframing Autism

From scope to production

Stage 01

Technical Discovery

Review your use case. Validate Python is the right choice. Assess data and integration requirements. Architecture design with fixed-price quote.

Weeks 1–2

Stage 02

Core Development

FastAPI service or pipeline core. Data models and validation. Integration with AI APIs or data sources. Unit and integration tests.

Weeks 3-7

Stage 03

Integration & Testing

Wire all integrations. Load test critical paths. E2E tests. Error handling and retry logic. Staging deployment.

Weeks 8-10

Stage 04

Production & Ops

Deploy to your AWS environment. Structured logging and observability. CI/CD pipeline. Documentation and handover.

Final 2 weeks

How to work with us

Fixed-Price Project

Defined scope, price, and timeline. Best for well-scoped Python services where the inputs, outputs, and integrations are clear before we start.

AUD $30,000–$130,000+

Monthly Retainer

Dedicated Python engineering on your AI or data platform continuously. Best for teams building incrementally on an evolving Python backend.

From AUD $10,000/month

Data Pipeline Audit

We assess your existing Python pipelines — identifying fragility, performance issues, and data quality gaps — and produce a prioritised remediation plan.

From AUD $8,500

Every question answered.

Can't find what you need?

Python is right when your backend runs ML models or calls AI APIs natively, you are building data pipelines, or your team is data science-first. Node.js for high-throughput real-time APIs and JavaScript teams. For AI-native products Python is almost always the right call.

Focused FastAPI backend: AUD $30,000–$60,000. Full data pipeline or ML service: $60,000–$130,000. Ongoing Python retainer: from $8,000/month.

ETL/ELT pipelines with Airflow or AWS Step Functions, data transformation with Pandas and PySpark, feature engineering for ML, data quality validation with Great Expectations. Reproducible, monitored pipelines — not one-off scripts.

Focused FastAPI backend: 6–10 weeks. Full data pipeline with Airflow and integrations: 10–16 weeks. ML model serving: 10–14 weeks. Scope clarity at the start is the primary variable.

pytest as primary. Hypothesis for property-based testing. httpx for async API testing. Black, Ruff, and mypy for code quality. Locust for load testing.

Yes — type hints throughout, mypy in CI, Pydantic for runtime validation at API boundaries. This catches entire classes of bugs before they reach production.

FastAPI is our default for new APIs. Django for content-heavy applications. Flask for lightweight microservices. We choose based on the problem, not habit.

Yes — FastAPI microservices handling LLM orchestration, RAG pipelines, streaming responses, tool calling, and agent execution. Integrated with AWS Bedrock, OpenAI, and Anthropic, deployed on ECS or Lambda.

FastAPI async handles high concurrency. Celery workers for CPU-bound tasks. Horizontal scaling on ECS. PySpark for data processing. ONNX for ML inference. Python GIL is well understood and routable around for most production workloads.

Yes — FastAPI with Mangum adapter for Lambda, or purpose-built Lambda functions. Cold start optimisation, bundling, and observability included.

When the workload is high-concurrency I/O-bound and the team is JavaScript-native, Node.js is often a better choice. When compute-intensive without numpy/C extensions, Go or Rust may be better. We will tell you honestly.

1 Your Information

2 Book Meeting

3 Confirmation

Build your Python backend right.

Free technical consultation. We review your use case, validate Python is the right choice, and give you a clear scope and estimate.

What to expect on your call

What to expect

1 Share a few details
Complete the form with your contact details and what you need help with.
2 Book your free discovery call
Once you submit the form, choose a time that suits you for your discovery call.
3 Privacy comes first
Sign an optional NDA to ensure the highest privacy level and protection of your idea.
4 Discovery call
We’ll discuss your goals, the support you need and answer your questions. If we’re a good fit, we’ll outline the next steps.

What to expect

1 Share a few details
Complete the form with your contact details and what you need help with.
2 Book your free discovery call
Once you submit the form, choose a time that suits you for your discovery call.
3 Privacy comes first
Sign an optional NDA to ensure the highest privacy level and protection of your idea.
4 Discovery call
We’ll discuss your goals, the support you need and answer your questions. If we’re a good fit, we’ll outline the next steps.

Python for AI, data pipelines and backends that need it.