Backends & APIs
REST, GraphQL, gRPC, WebSocket, event-driven. Node.js, Python, Go based on context. Architecture that scales from MVP to 100M requests/month. Full observability, automated tests, CI/CD deployment.
The context
In 2026, an app or site without a solid backend is a time bomb. At 100 beta users, everything works. At 10,000 users, latency explodes. At 100,000 users, the server crashes Monday morning. Every downtime costs conversions, trust and SEO — Google demotes sites with degraded Core Web Vitals from slow APIs.
The classic trap: start in « fast MVP » mode with Firebase Functions and some Node, then realise at scale that everything is wired badly. Typical re-architecture under pressure cost: 3-6 months of product freeze and €80-150k. Our approach: build from day one for realistic 18-month load, not theoretical ideal.
Our conviction: a solid backend stands on 4 pillars. Clear architecture (microservices or modular monolith depending on context), full observability from day 1, automated tests passing every commit, reproducible deployment without human intervention. One principle: if it must break, break cleanly and you must know before your customer.
99.9%
Guaranteed uptime
Max 8h43 cumulative downtime per year
< 100ms
p99 latency
On 99% of API requests under nominal load
100M
Requests/month
Typical load held without degradation by our backends
0
Silent regression
Sentry + Grafana alerting before a user notices
What we build
From MVP monolith backend to distributed event-driven architecture, we pick just-enough complexity — no more.
The universal standard.
Robust REST API with OpenAPI 3.1, clean versioning, pagination, filtering, rate-limiting, JWT or OAuth 2.1 auth. Interactive Swagger or Scalar documentation. Most common case: 70% of our projects.
When the frontend needs flexibility.
Apollo Server or Yoga, schema-first with type codegen frontend-side. N+1 solver (DataLoader), persisted queries for prod, WebSocket subscriptions for real-time. Ideal for complex apps with multiple consumers (web + mobile).
When inter-service performance matters.
Internal services in gRPC (Protobuf, HTTP/2, bidirectional streaming). 10× faster than REST for server-to-server calls. Service mesh (Linkerd or Istio) for observability and traffic management.
Chat, live dashboards, gaming.
Socket.IO, native WebSocket or Server-Sent Events depending on case. Horizontal scaling with Redis pub/sub. Auto reconnect, presence tracking, guaranteed message ordering. For apps where each second counts.
When services must stay decoupled.
Apache Kafka, RabbitMQ or AWS SQS/SNS. CQRS architecture, event sourcing where relevant. Strong decoupling: a service can fail without breaking others. Ideal for fintech, e-commerce, IoT, multi-tenant platforms.
When data must move.
Transformation pipelines between systems: data lake → analytical warehouse, CRM ↔ ERP sync, external feed aggregation. Apache Airflow, Dagster, Prefect or custom Python code based on volume. Automated data quality tests.
Our approach
We start by modelling your business domain cleanly. A solid data schema is worth two years of avoided refactoring.
/step/01 Workshop with your team to understand the business. Modelling entities (users, resources, events), use cases, business invariants. Architecture choice (modular monolith vs microservices) based on complexity and team.
/step/02 DB schema (PostgreSQL most often), API schema (OpenAPI 3.1, GraphQL SDL or Protobuf). Contract tests with examples. Your validation before any business code line.
/step/03 Incremental implementation, continuous deployment to staging. Unit tests on business logic, integration tests on endpoints, load tests (k6 or Artillery) to validate performance before prod.
/step/04 Production deployment with progressive rollout. Sentry (errors), Grafana (metrics), Loki (logs), Tempo (traces) setup. Slack/PagerDuty alerting. Ops runbook for typical incidents. 30-day guarantee after live.
Tech stack
Proven stack on multi-million-request production deployments. Technical choices aligned with needs, not fashion.
Languages
Node.js (TypeScript) · Python · Go · Rust
TS for most (DX, ecosystem). Python for data/ML. Go when performance matters (high-throughput microservices). Rust for extreme cases (parsing, crypto).
API frameworks
Fastify · NestJS · FastAPI · Hono · Echo
Fastify for pure TS perf. NestJS for big projects (DI, structure). FastAPI for Python (Pydantic validation). Hono for edge (Cloudflare Workers). Echo in Go.
Databases
PostgreSQL · MongoDB · Redis · ClickHouse
PostgreSQL by default (solid relational, JSON support). MongoDB if truly document-first. Redis for cache and queue. ClickHouse for analytics.
Queues & event bus
Kafka · RabbitMQ · BullMQ · AWS SQS/SNS
BullMQ (Node + Redis) for simple jobs. RabbitMQ for complex routing. Kafka for event sourcing and massive streaming. AWS SQS when already on AWS.
Observability
Sentry · Grafana · Loki · Tempo · OpenTelemetry
Sentry for errors (essential). Grafana stack (Loki logs, Tempo traces, Prometheus metrics). OpenTelemetry for standardised instrumentation.
Deployment & infra
Docker · Kubernetes · Vercel · Railway · AWS · Cloud Run
Vercel or Railway to start fast. Kubernetes (GKE/EKS) past 3-5 services. Cloud Run for containerised serverless. AWS if ecosystem already in place.
Measurable guarantees
99.9%
Guaranteed uptime
On rolling 30 days. Vercel/AWS/GCP SLA always behind. Proactive maintenance included.
<100ms
p99 latency
99% of API requests respond in under 100ms under nominal load. Measured by you with Pingdom or equivalent.
80%+
Test coverage
Unit tests on business logic + integration tests on endpoints. CI pipeline blocks regressions.
0
Silent regression
Sentry catches 100% of errors, Slack/Pager alerting. You know before your customer that there's a problem.
定价
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