Backend development services that hold under real load

API design and implementation, database architecture and migrations, authentication and authorization, background job processing, third-party and payment integrations, caching and performance work, and the deployment setup that gets all of it into production reliably. If your product has a login, stores data, or talks to another system, that is backend scope. The parts teams underestimate are usually the data model and the API contract, because those are the two decisions everything downstream inherits and the two that are expensive to change later.

NestJS on Node, Django and FastAPI on Python, Java for microservices, and Next.js route handlers for backend-for-frontend layers. On data, MySQL 8 and PostgreSQL with TypeORM or Prisma, Redis for caching and rate-limit state, and Bull or RabbitMQ for queues and background jobs. For real-time we build on WebSocket, bidirectional gRPC streaming, and LiveKit rooms for live audio and video. We pick based on what your team can maintain after we hand over, not on novelty.

We deploy and operate the backends we build, which is a narrower offer than a cloud consulting practice, and we would rather say so than sell you the wider one. Our production deployment experience is on AWS with EC2, CloudFront, and S3, and on Azure with Blob Storage, Container Registry, and Kubernetes. We also use Vercel for edge-delivered applications and run self-managed containerized infrastructure with Docker Compose, Nginx, and PM2 where a managed platform would add cost without adding value. If you need cloud strategy, a large-scale datacentre migration, or FinOps as a standalone engagement, we are not the strongest choice for it.

That is the approach we prefer. On one platform migration we ran an entirely new backend against a live 143-table production database with zero schema-altering migrations, because a legacy application still depended on that schema until cutover. Legacy password hashes were made compatible with the new authentication layer, so existing users kept their credentials and never knew the platform changed underneath them. The work is incremental and reversible at each step rather than one scheduled big-bang cutover, which is the version that tends to actually succeed.

It depends on whether your data model can genuinely be partitioned and whether your teams need to deploy independently. We have built both deliberately. When a shared database schema could not be re-partitioned, a modular monolith of roughly 70 feature modules was the correct answer and we said so. When separate real-time, orchestration, and reporting workloads needed to scale differently, microservices were, split across gateway, file storage, session, orchestrator, and reporting services. You get the recommendation with the reasoning attached, and the reasoning is the part that matters six months later.

By instrumenting and constraining them. Content-keyed caching so an identical request costs nothing on repeat and invalidates the moment the underlying record changes, per-route rate limiting and daily quotas backed by Redis and checked only on cache miss, and a single composed deadline across the whole pipeline so a slow model call cannot stack retries indefinitely. Per-stage token counts, durations, and cache-hit flags are logged structurally, so cost per request is a number you can query rather than estimate. On one production pipeline, a repeat identical request eliminates all five model calls and returns at zero token cost.

Security assessment is part of delivery, not a phase bolted on afterward. We review configuration, secrets management, and access control, and remediate what we find before release. In practice that means credentials in managed environment configuration rather than in source, centralized permission checks instead of the same logic duplicated at every call site, server-side signature verification on inbound webhooks and payment callbacks before anything is persisted, and application-level encryption for sensitive payloads where the threat model warrants it. On a fintech payments product that meant AES-256-GCM on the request body with the symmetric key wrapped using the server's RSA public key.

Yes, for the systems we build, and we will be specific about your current state first. Our stronger engagements run automated pipelines through Azure Pipelines or GitHub Actions with static analysis, container registry publishing, and Kubernetes rollout. Others we have inherited had manual release processes we are still improving, so CI/CD maturity across our portfolio is genuinely uneven rather than uniformly strong. Multi-stage Docker builds, health checks, dependency-ordered startup, and migrate-on-boot are the baseline we aim for, so the same image runs in every environment.

A direct answer, because you would find out anyway: automated backend test coverage varies across our portfolio and is thin on several systems we inherited. What we run consistently is a build and lint gate on every change, static analysis where a pipeline exists, unit and end-to-end test tooling on our stronger engagements, code review discipline including a hard rule that schema-to-entity mappings are verified before merge, and a documented manual smoke-test gate before release. If you want comprehensive automated coverage, we scope it as real engineering work with real hours rather than imply it is already there.