Python development services that ship real software, not slide decks
Full Scale is a Python development company that delivers custom Django, Flask, and FastAPI builds, data engineering pipelines, and legacy modernization through senior Filipino engineers who join your team. We have placed hundreds of Python engineers, including the team behind enterprise systems at AMC Theatres. Delivered via staff augmentation: you direct the work, we handle hiring, payroll, and HR. First sprint in 7 days.
from fastapi import APIRouter, Depends
from .schemas import OrderIn, OrderOut
from .service import OrderService
router = APIRouter(prefix="/orders")
@router.post("", response_model=OrderOut)
async def create_order(
payload: OrderIn,
svc: OrderService = Depends(),
) -> OrderOut:
return await svc.create(payload)Python teams trusted by enterprises, scale-ups, and Fortune 500s
Previously founded VinSolutions ($150M+ exit) and Stackify
I have been shipping Python in production for fifteen years
I started my career as a developer in the early 2000s, and Python has been part of every engineering org I have built since. At Stackify, the APM and logging platform I founded, we built and supported a Python agent that instrumented production Python applications for thousands of engineering teams. The pattern I saw over and over with that data in front of me: most production Python performance problems are ORM mistakes, not Python being slow. I have hired and managed Python developers across four companies, and I know what separates a real Python engineer from someone who picked it up over the weekend.
Full Scale is a Python development company built around senior Filipino developers and the Product Driven framework. We have hired hundreds of Python engineers in the Philippines over the years, we test them on real architecture problems rather than syntax quizzes, and we have delivered Python development services for fast-growing SaaS companies and for AMC Theatres. When the engagement is a full SaaS build, see our SaaS development company page. If you need Python development services from a team that actually builds things, rather than a vendor that subcontracts the work, you are in the right place.
Five reasons Python is the right stack for data-driven software
If you've already committed to Python, you don't need to read this. If you're still evaluating whether Python is the right call for your project, or whether to migrate an existing system toward it, these are the technical arguments that hold up in production, not in a vendor slide deck.
The default language for data, ML, and AI
If your roadmap touches machine learning, data pipelines, or AI features, Python is not a neutral choice. NumPy, Pandas, PyTorch, and scikit-learn live here, and the research-to-production path is shorter than in any other language. For a data- or AI-heavy product, that gravity is a real advantage.
Readability that speeds up delivery
Python's clean syntax means fewer lines, faster iteration, and code that a new engineer can read on day one. For products that need to move quickly and stay legible as the team grows, that velocity compounds. The honest trade-off is raw runtime speed, which is why we'll tell you when a hot path belongs in Go, Rust, or a C extension.
Mature frameworks and deep library support
Django for batteries-included web apps, FastAPI for modern async APIs, Flask for lightweight services, and a library for nearly every problem on PyPI. Less custom plumbing, more shipping, and a stack that's been hardened in production for two decades.
One language across web, data, and automation
The same team can own an API backend, a data pipeline, and the automation glue between them without context-switching languages. For organizations that want to share people and code across web and data work, that versatility keeps the org simpler.
A huge talent pool and a safe long-term bet
Python is taught everywhere and consistently ranks as one of the most-used languages in the world, so the hiring pool is enormous and global. Choosing it for a system you'll run for years is low-risk, as long as the workload isn't CPU-bound enough that the GIL becomes the bottleneck.
AI-powered Python engineers, trained on Product Driven principles
Most Python teams adopting AI are shipping more code without shipping better software. The slop volume climbs, production bugs follow, and engineers whose only skill is typing faster end up costing more in cleanup than they save in keystrokes.
Full Scale Python developers are trained on something different: the Product Driven approach from Matt's book, combined with the full modern AI toolkit (GitHub Copilot, Claude, Cursor). They think first, type second, and use AI for the parts where judgment doesn't add value. That combination is rare, and it is what you should be looking for when you hire Python developers in 2026. Python is also where most AI work lives, so when the project is squarely on the AI side you can hire AI developers from the same vetted bench.
Product Driven engineering
Our engineers are trained on the five pillars from Matt's book: Vision, Focus, Clarity, Ownership, and Courage. The result is developers who push back on bad product decisions, ask whether a ticket should exist before writing it, and own the outcome of what ships. They are not order takers.
Read Product Driven, the bookAI as a thinking partner
Every Python engineer on our bench works with GitHub Copilot, Claude, and Cursor every day. They use AI to explore options, scaffold the boring parts, generate test suites, and review their own pull requests before a human ever sees them. Judgment stays with the engineer, the grunt work moves to the machine.
AI without product thinking is just a slop machine, and the Python engineers I want on my team don't get caught by that. They reason about the system before they reach for Copilot, and they use AI for the parts where judgment doesn't matter. That's who we hire and train at Full Scale.
The engineering team behind AMC Theatres
The Python development services we deliver across the full build lifecycle
Greenfield custom Python development, a Django build, a FastAPI service, a data engineering pipeline, a legacy modernization, a performance fix that has been open for six months: our engineers ship across all of it. The difference is how we deliver. Every one of these Python development services runs through staff augmentation, so you get senior Python engineers embedded in your team and billed for engineering hours, not a fixed-bid project shop that disappears at handoff. Here are the Python development services we get hired for most often.
Custom Python application development
Custom Python development means greenfield builds on Django, FastAPI, or Flask, using modern patterns like service layers, Pydantic models, dependency injection, and typed Python. We start with a real domain model rather than a CRUD scaffold, so the resulting codebase survives the first 18 months without a rewrite.
Read our Python outsourcing guideDjango web application development
Django web work covers multi-tenant SaaS, admin tooling, role-based access, audit logging, and the unglamorous reporting that enterprise software lives or dies on. We have shipped Django at every scale, from seed-stage MVPs to platforms serving millions of users.
Hire Django developersPython API development & system integration
We build REST APIs with FastAPI and Django REST Framework, gRPC for internal services, and contract testing in CI. Third-party integrations get circuit breakers, proper retry logic, and idempotency keys, which gives you an API layer your downstream consumers don't curse at.
Hire API developersServerless Python on AWS, GCP, and Azure
We build serverless Python on Lambda, Cloud Functions, and Azure Functions, using Terraform or SAM for infrastructure, EventBridge or Pub/Sub for orchestration, and structured observability for production debugging. That is the full serverless stack, with no "we deployed a Lambda" shortcuts.
Python automation and internal tooling
A lot of real Python work is automation: ETL glue, internal tooling, scripts that move data between systems, and the workflow engines that keep operations running. Our engineers ship Airflow DAGs, Prefect flows, and the unsung Python services that power how a company actually runs.
Python performance & debugging
Our Python performance work covers py-spy and cProfile for hot-path profiling, tracemalloc for memory leaks, async deadlock investigation, and production debugging when things get weird. These are skills most offshore Python shops have never developed, so hire us when your application is slow and nobody knows why.
Architecture patterns our Python engineers apply in production
Most offshore Python shops deliver a working application at handoff. What determines whether it's still working and maintainable 18 months later is the architecture decisions made in the first sprint. These are the patterns our engineers reach for, and the reasoning behind when each one earns its complexity.
Clean / Layered Architecture
A service layer between the framework and the domain, whether the app is Django or FastAPI, so business logic doesn't live in views or models. Use cases stay independent of the web and persistence layers, and the codebase survives a framework or database change without a rewrite of the core.
Domain-Driven Design (DDD)
Bounded contexts, aggregates, value objects, and domain events. We reach for DDD when the domain is complex enough to justify it, not as a default, but as a deliberate choice for systems where the business rules are the hard part.
Async & Task Queues
Because the GIL limits CPU parallelism, scaling Python is about getting IO-bound work off the request path: async with FastAPI and asyncio, background jobs on Celery or RQ, and message-driven processing. We pick the model that matches the workload instead of forcing threads.
Data & ML Pipelines
Python's home turf: batch and streaming pipelines orchestrated with Airflow or Prefect, Pandas and PySpark for transforms, and a clean handoff from notebook to production service. We build pipelines that are tested and observable, not a pile of cron jobs.
Cloud-Native & Serverless
Containers on Kubernetes or serverless on Lambda and Cloud Run, with config externalized, secrets out of code, and observability through OpenTelemetry. We package Python for the platform instead of shipping a dev setup to prod.
Microservices & Distributed Systems
Service decomposition, sync vs async communication, distributed transactions via saga orchestration and the outbox pattern, and tracing across boundaries. We don't recommend microservices when a well-modularized Django monolith is the right call.
Opinionated takes on Python from engineers who ship it
Most vendors tell you Python is the right choice for everything. We'll tell you when it isn't. These are the actual opinions we hold based on building and maintaining Python systems in production, not talking points from a sales deck.
Data pipelines, machine learning and AI features, automation, and API backends on Django or FastAPI. Anything where time-to-iteration matters and the team wants one readable language across web and data. If your product leans on data or ML, Python is the default and it isn't close.
CPU-bound hot paths and high-concurrency-per-core work where the GIL bites, which belong in Go, Rust, or a C extension behind a Python API. Ultra-low-latency systems. Mobile apps, which want native or React Native. And very large statically-typed enterprise monoliths where a compiled type system catches more at build time, though type hints plus mypy narrow that gap a lot. We'll tell you when to push a workload out of Python rather than force it.
We ship type hints checked with mypy, dependencies pinned with Poetry or uv, Pydantic validation at the boundary, async where the work is IO-bound, and real tests with pytest. We refuse bare except clauses that swallow errors, mutable default arguments, business logic stuffed into Django views or fat models with no service layer, import-time side effects, and untyped dicts passed around in place of real types.
Python 2 to 3 migrations that stalled and left a codebase straddling both. Django monolith to microservices splits that kept one shared database, so you get distributed-systems cost with none of the isolation. Sync Flask to async rewrites that never rethought the IO model, so nothing actually got faster. And teams that added type hints but never ran mypy in CI, so the hints rotted into decoration.
From first call to production: how a Python project runs at Full Scale
Staff augmentation without a delivery framework is just headcount. Here is what the engagement actually looks like from the first conversation to a deployed production system and the ongoing work that comes after.
We scope the engagement together: what to build first, what specializations to staff, what the first sprint should deliver. You walk away with a staffing plan and a candidate shortlist, not a 40-page requirements document.
You interview our pre-vetted candidates and select who starts. We handle employment, payroll, and equipment setup on the Philippines side. Your engineer gets access to your repo, your tools, and your standups. First commit typically happens within the first week.
Your engineer works in your sprint cadence, under your tech lead, committing to your repo. You see the work in progress, not at a scheduled demo. Architecture decisions happen in your standups, not behind a project management wall. The sprint velocity is yours to direct.
Our engineers write tests as part of delivery, not as a post-sprint cleanup task. Unit and integration tests in pytest, type checking with mypy in CI, and coverage for the critical paths. AI-assisted PR review (Copilot, Cursor) before human review. Code that ships is code that's been tested.
Your engineers own the production deployment: infrastructure as code, observability from day one (OpenTelemetry, Datadog, or Sentry), and runbook documentation for the most likely failure modes. They stay on after launch. Post-launch bugs go into your backlog like any other work, not into a 'warranty period' clause in a contract.
How a Python development project starts at Full Scale
No discovery phase you pay for before a line is written. No 6-week RFP process. We scope in a single call, assemble pre-vetted engineers, and have code shipping in the first week.
Scoping call
30 minutes. We learn what needs to be built, what's already in the codebase (if anything), what the first sprint should deliver, and what specializations the project needs. We don't pitch on this call. We scope.
Team assembly
We pull 1–3 pre-vetted Python engineers whose skills, seniority, and prior project experience match what the project requires. You see their full profiles and actual project history before the interview.
Technical interview
You interview candidates the way you would any senior hire: live system design, async and data-modeling questions, and Python-specific technical depth. Pass on anyone you don't believe in. We keep looking.
Contracts & setup
One contract with Full Scale. We handle all employment, payroll, equipment, and HR logistics in the Philippines. Your engineer gets repo access, tool access, and sprint 1 is planned.
First delivery
Your engineer joins your standups, commits to your repo, and ships code in the first week. Our delivery team stays in the loop through ramp-up to make sure velocity doesn't stall. They own the work through launch and beyond.
Signing a contract is not the same as shipping software
Most Python outsourcing failures aren't engineering failures. They are delivery model failures. The fixed-bid agency model creates incentives that work against you: speed over quality, handoffs over ownership, scope control over outcomes. Staff augmentation realigns those incentives. Here are the six ways the agency model breaks down on real Python projects.
Fixed-bid scope creep destroys budgets
Agencies win the bid with an optimistic estimate, then recover their margin through change orders. Every requirement that wasn't in the original spec becomes a billable revision. By go-live, the 'fixed' price has doubled and the relationship is adversarial.
The agency disappears after handoff
Fixed-bid projects end at deployment. The engineers who built your system move to the next bid. You own every production bug and architectural decision without the institutional knowledge of the people who made them. Post-launch support becomes a new contract negotiation.
No visibility until it's too late to change
Black-box delivery means you see the product at the end of a sprint cycle or, worse, at handoff. By the time you learn the architecture doesn't fit your use case, the codebase is already built around it. Staff aug keeps engineers in your repo and your standups from day one.
Speed incentives drive wrong architecture
Fixed-bid agencies are paid to ship fast, not right. That means all the logic crammed into Django views, N+1 ORM queries under real load, sync IO blocking the request path, and not a type hint in sight. You inherit a Python codebase optimized for handoff velocity, not long-term maintainability.
Engineer rotation breaks continuity
Agencies staff projects with whoever is available, not whoever is best-matched. Project managers cycle. The developer who built your core data pipeline gets rotated to another engagement. New engineers read code they didn't write for onboarding, and the velocity cliff arrives around sprint 8.
Production failures become "out of scope"
Memory bloat that surfaces after a week of uptime, a pipeline that silently drops records under load, security gaps that weren't in the spec, agencies classify these as new work. With staff augmentation, your engineers own what they shipped and have incentive to build it right the first time.
Python expertise tuned to your industry
As a Python development company that has been around for over a decade, we have placed dedicated Python developers into nearly every industry that runs production Python. Domain knowledge cuts onboarding time in half, so we match developers to projects where they have already shipped real code.
SaaS & Scale-ups
B2B SaaS on Python is our home turf, with engineers who have shipped multi-tenant Django, subscription billing on Stripe, role-based access, and white-label theming. They have worked on Python SaaS at every scale, from seed-stage to public companies.
From FastAPI services to Django monoliths and serverless Python
Whether you want to hire Django developers for a greenfield web application, hire FastAPI engineers for a high-throughput API, or outsource Python development on a legacy Flask system, the bench covers every layer of the modern Python stack. Pick what you need. We will match a Python programmer fluent in it.
Hire dedicated Python developers, two ways
Most clients start with a single dedicated Python developer and grow into a full team. Either way, you get full-time engineers who sit on your standups, work your hours, and ship code against your roadmap. Both options are our staff augmentation model at the core: dedicated, long-term engineers embedded in your team rather than freelancers, shared resources, or a project shop on the side. See the full breakdown of how we hire dedicated Python developers across every engagement we staff. When a single hire should ship both the Python API and the frontend, you can hire offshore full stack developers from the same bench. When a Python service needs to migrate hot paths to a faster runtime, you can also hire Golang developers from the same vetted bench.
Dedicated developer
Full-time, exclusive, sits on your standups.
- Full-time Python engineer assigned only to your project
- Works your hours, your tools, your codebase
- Joins your standups, reports to your tech lead
- We handle payroll, HR, equipment, retention
- Replace within 30 days if it isn't a fit
Dedicated Python developers, starting at $35 an hour
That rate is fully loaded. Every engineer we staff on your Python project is a senior developer in the Philippines working full-time under your direction, and we cover the payroll, benefits, HR, and equipment. The same role hired locally in the US costs $150K to $195K a year, which is the delivery math that brings most teams to the table.
- Full-time, dedicated Python engineer
- Pre-vetted by senior Python reviewers
- Works your hours, your tools, your codebase
- Payroll, HR, equipment, benefits handled by us
- US-based account manager you can escalate to
- 30-day replacement guarantee if it isn't a fit
Full Scale has made the Inc. 5000 four years in a row and is Great Place to Work certified. We have been doing this since 2018, and pricing isn't the only reason clients stay with our Python development company, it's the easiest reason to call.
Why we deliver Python projects from the Philippines
Every Python project we deliver is staffed from the Philippines. You can also hire dedicated developers in the Philippines across every other stack we work in, with the same vetting bar, retention, and engagement model that Python clients get.
English-fluent by default
The Philippines is the third-largest English-speaking country in the world. Standups, code reviews, and customer calls work the way they do with any US team member.
Real time-zone overlap
Most of our Python engineers work US business hours with 4-8 hours of real-time overlap with East and West Coast teams, so decisions happen live during shared hours rather than crawling through 24-hour async handoffs.
Deep engineering talent pool
Cebu and Manila produce tens of thousands of CS and IT graduates a year. Python is the most-taught language at the country's top engineering programs, deep enough to staff a full Python project team without compromising on seniority.
Cultural alignment with US teams
Filipino engineers grow up on US business norms, US TV, and US tech culture, so agile rituals, direct feedback, and collaborative workflows feel familiar from day one. These teams integrate fast rather than needing constant management.
Staff augmentation vs the other ways to get Python software built
Every Python delivery model has a different set of trade-offs. Fixed-bid agencies offer a contract; consultancies offer a proposal. Staff augmentation offers engineers who embed in your team and work under your direction from day one. Here is how those models compare on the things that actually determine whether a Python project succeeds.
| Factor | Full Scale (staff aug) | Fixed-bid agency | Consultancy / SI | Build in-house |
|---|---|---|---|---|
| Time to first sprint | 7 days | 4-8 weeks | 6-12 weeks | 3-6 months |
| You control architecture decisions | ||||
| Visibility into work in progress | ||||
| Engineers dedicated full-time to your project | ||||
| Scope flexibility when requirements change | ||||
| Budget predictability | ||||
| Engineers own what they ship post-launch | ||||
| You own all IP from day one | ||||
| Engineer continuity across the project | 93%+ retention | varies | low | varies |
| Fully-loaded cost vs US in-house team | ~40-50% | ~60-80% | ~100-150% | 100% |
The numbers behind a Python staffing partner that actually works
From the people we actually staff teams for
Full Scale's development team was pivotal in elevating our facility management software. Their expertise turned complex challenges into seamless functionalities, enhancing user experience and operational efficiency.
With Full Scale's developers, we transformed the commercial real estate landscape. Their team's proficiency in agile development and proactive communication accelerated our product release.
The team at Full Scale brought our vision to life with their development skills. They helped us navigate technical requirements with ease, resulting in a robust platform our users trust.
Deeper guides to Python development and architecture
Offshore Python development
When offshoring Python work is the right move, and how to do it well.
Outsource Python development
How to outsource Python development without losing control or quality.
Nearshore vs offshore
When each model wins, from a CEO who has run both.
Outsourcing vs offshoring
The distinction most CTOs get wrong, and why it matters.
What offshore development really costs
The real numbers behind offshore rates and total cost.
The ROI of offshore development
The math behind 50-80% development cost reductions.
Everything you wanted to know about Python development services
Get Python development services from a team that has actually built Python systems before
30-minute discovery call with the Python development company that delivers custom builds, Django and FastAPI services, data engineering pipelines, and modernization through dedicated developers from the Philippines. We'll learn what you're building, walk you through which Python, Django, FastAPI, or data engineers are on the bench, and you'll meet candidates within a week. You won't get pressure or a sales pitch on the call.