Python Development for the Era of AI.

Because Python is the language of AI and ML in 2026 and AddWeb operates a dedicated AI engineering unit four years deep, plus four production AI products in our own portfolio (AddWeb AI, La Liga Score Predictor, EcomSupport360, WeWP). When you hire our Python team, you are hiring engineers who ship AI in production for clients and for AddWeb itself. That alignment is rare in the Python services market.

Most Python engagements at AddWeb fall between $5,000 (focused API or automation sprint) and $150,000+ (enterprise AI or ML platform build with data engineering, MLOps, and production deployment). Discovery-only sprints start at $2,500. We provide a written, line-itemed estimate within 48 hours of a scoping call no boilerplate ranges, no padded contingencies.

A focused Django or FastAPI build ships in 6 to 12 weeks. An ML pipeline with model training, deployment, and monitoring runs 10 to 16 weeks. Full enterprise AI platforms with multi-model orchestration, MLOps, and data engineering run 16 to 28 weeks. We commit to a fixed timeline after a 1 to 2-week paid discovery and publish a weekly burndown so you always know what is at risk.

Django fits content-heavy applications, admin-driven workflows, CMS-like products, and full-stack web apps where developer productivity matters more than raw latency. Its batteries-included approach (ORM, admin, auth, forms) accelerates time-to-market. FastAPI fits API-first products, ML model serving, real-time data flows, and microservices where async performance, OpenAPI auto-generation, and Pydantic validation matter most. Many of our clients run hybrid architectures Django for the application layer, FastAPI for ML inference and high-throughput APIs. We do not default to either framework; we recommend based on the product.

Yes this is core Python work for our team. We ship feature stores, training pipelines (PyTorch, TensorFlow, scikit-learn, XGBoost), model registries (MLflow, Weights & Biases), model serving (TorchServe, BentoML, KServe, FastAPI), drift detection, A/B testing, retraining triggers, and full CI/CD for ML. We do not stop at proof-of-concept notebooks production-grade MLOps is how AddWeb’s own four AI products stay reliable, and that same discipline is what client engagements receive.

Yes. We build batch and streaming pipelines with Apache Airflow, Prefect, and Dagster for orchestration; PySpark and Polars for transformation; Pandas for analytics workloads; and Kafka, Kinesis, or Pub/Sub for streaming. Data sinks include Snowflake, BigQuery, Redshift, Databricks, and self-managed PostgreSQL or ClickHouse. We architect for data quality, lineage tracking, and cost monitoring from day one not as an afterthought.

OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, and fine-tuned open-source models hosted on AWS SageMaker, Azure ML, or GCP Vertex AI. Frameworks: LangChain, LlamaIndex, Haystack, DSPy for orchestration; PyTorch and TensorFlow for custom model training; Hugging Face Transformers for fine-tuning and inference; FAISS, Pinecone, Weaviate, pgvector for vector storage. We pick by use case not by what we built last quarter.

Yes it is one of our most common engagements. We start with a 1 to 2-week code and architecture audit, deliver a written rescue plan with risk ratings (performance, security, test coverage, dependency debt, model quality), and quote a recovery roadmap. We have inherited Python codebases from prior agencies, in-house teams, and freelance developers including Django monoliths, abandoned Flask apps, half-finished ML pipelines, and legacy Python 2 systems that needed Python 3 migration.

Yes. Python 2 to Python 3 migrations, Django version upgrades (1.x to 5.x, including LTS hops), Flask to FastAPI rewrites for performance-critical APIs, and full architecture modernization. Migrations are scoped after a code audit — we never quote without a dependency review, test coverage assessment, and risk register.

Pytest for unit and integration tests, pytest-cov for coverage tracking (target 80 percent+ for business logic, 95 percent+ for payment and security paths), Ruff and Black for linting and formatting, mypy for type checking, Bandit for security scanning, and full CI/CD with GitHub Actions or GitLab CI. For ML projects we add data validation tests (Great Expectations, pandera), model performance regression tests, and pipeline integration tests. Quality is not optional.

Yes. AWS (Lambda, ECS, EKS, SageMaker, Step Functions), Azure (App Service, AKS, Azure ML, Functions), Google Cloud (Cloud Run, GKE, Vertex AI, Cloud Functions). Infrastructure-as-code with Terraform or Pulumi. CI/CD with GitHub Actions, GitLab CI, or AWS CodePipeline. Observability with Datadog, Sentry, or open-source Prometheus and Grafana. We architect for cost first most clients see meaningful infrastructure savings after our review.

Post-launch we offer SLA-backed Python maintenance plans (24×7, 12×5, or 8×5 coverage), proactive dependency updates with security patching, performance monitoring, model drift detection for ML projects, and quarterly architecture reviews. Most clients retain a smaller version of their build team for ongoing feature work this is why our 98 percent retention rate is what it is.

Both. Fixed-price suits well-scoped MVPs, API builds, and one-off ML pipeline work. A dedicated team (typically 3 to 8 engineers plus PM and ML architect for AI work) suits ongoing product development or platform-grade builds where the roadmap evolves week-to-week. Many clients start fixed-price for the MVP and shift to a dedicated team post-launch.

Four ways. First, see the four AI products AddWeb operates in production  AddWeb AI, La Liga Score Predictor, EcomSupport360, and WeWP  most competing Python agencies cannot point to a single AI product they own. Second, read our 74+ verified Clutch reviews and Trustpilot reviews. Third, see our G2 recognition and DesignRush #1 Greenville ranking. Fourth, book a 30-minute strategy call where a senior Python or ML engineer not a salesperson gives you a free technical perspective on your project, codebase, or ML idea. Most decisions get made off that call.