Backend, APIs & Databases
Scalable APIs, event-driven microservices, and the databases underneath them — the engine room behind everything we build.
Backend work is the part of the product nobody notices until it breaks: a schema that starts timing out under real load, an API with no versioning strategy, a monolith that can't scale past a single Postgres instance. We build and rebuild that layer — REST, GraphQL, and gRPC APIs with proper auth and rate limiting, schemas indexed and sharded for the query patterns you actually run, and event-driven services on Kafka or RabbitMQ that scale horizontally instead of falling over under load. The language and database fit the workload, not the other way around: Node.js and Go for high-throughput services, Python and Java where your team or ecosystem already lives, PostgreSQL and MySQL as relational defaults, and MongoDB, DynamoDB, or Cassandra when the data genuinely doesn't fit a table.
This is for teams past the MVP stage: a startup whose Node.js backend is buckling under real traffic, a product team that inherited a MySQL database nobody wants to touch, or an engineering lead who needs realtime notifications or live data — WebSockets, edge functions — added without rewriting the whole stack. It's also for consolidations and migrations, where the cost of getting it wrong is downtime or lost data, not just a missed deadline. When the bottleneck isn't the primary database at all, we add Elasticsearch or ClickHouse alongside it for search and analytics, or a Redis caching layer in front of it, rather than forcing every problem through the same relational hammer.
Our approach is disciplined because the stakes are: we design schemas and API contracts before writing a line of service code, we run migrations with verification steps and a clean rollback path so they ship with no data loss, and we build on a stack — Node.js, Go, Python, Java, PostgreSQL, MySQL, MongoDB, Redis, Kafka, gRPC, Elasticsearch, ClickHouse — chosen for what actually holds up in production, not what's trendy. Focused engagements, like an API build or a query-tuning pass, start at $8,000; full backend platforms and migrations start at $25,000+, scoped against your real system rather than a generic estimate.
Our process
Audit & Baseline
We profile your existing schema, indexes, and API surface across Postgres, MySQL, or your NoSQL store to find the actual bottlenecks and data-loss risks before proposing any changes.
API & Event Design
We spec REST, GraphQL, or gRPC contracts with auth and rate limiting, and map event flows and topics for Kafka or RabbitMQ before implementation starts.
Build & Integrate
Engineers build services in Node.js, Go, Python, or Java, add Redis caching, Elasticsearch/ClickHouse, or read-replica strategies, and wire up WebSocket or edge-function channels for realtime features.
Migrate & Harden
We execute zero-downtime cutovers with rollback paths, apply sharding where needed, and load-test before handing off documented, versioned endpoints.
What we offer
API Development
Documented REST, GraphQL, and gRPC APIs with auth, rate limiting, and versioning
Database Architecture
Schema design, indexing, query tuning, sharding, and read-replica strategies
NoSQL & Document Databases
MongoDB, DynamoDB, and Cassandra data modeling for flexible schemas and high-write workloads that don't fit a relational table
Search & Analytics Engines
Elasticsearch and ClickHouse for full-text search, log analytics, and sub-second aggregate queries at scale
Caching & Performance Layers
Redis and Memcached caching strategies, connection pooling, and query tuning that cut response times without new hardware
Zero-Downtime Migrations
Safe migrations and consolidations (e.g. MySQL to PostgreSQL, or relational to NoSQL) with no data loss
Microservices & Events
Event-driven, message-queued systems with Kafka or RabbitMQ that scale horizontally
Realtime Services
Messaging, notifications, and live data via WebSockets and edge functions
Technologies we use
Frequently asked questions
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Backend, APIs & Databases
Backend development is the engineering of the server-side layer of a product: the APIs, services, and databases that store data, enforce business rules, and answer every request a website or app makes. Prixelo builds and rebuilds that layer for teams past the MVP stage, such as a startup whose backend is struggling under real traffic, a product team that inherited a database nobody wants to touch, or an engineering lead who needs realtime features without a full rewrite. Deliverables include documented REST, GraphQL, or gRPC APIs with auth, rate limiting, and versioning; schemas indexed for the queries you actually run; event-driven services on Kafka or RabbitMQ; caching, search, and analytics layers; and zero-downtime migrations with a rollback path. Work runs in four steps: audit, API and event design, build, then migrate and harden. Focused engagements such as an API build or query-tuning pass start at $8,000; full backend platforms and migrations start at $25,000+.
Questions buyers ask
What does backend development include?
Backend development covers everything behind the interface: API design and build, database schema and indexing, business logic, background jobs, caching, and integrations with other systems. At Prixelo that means REST, GraphQL, or gRPC APIs with auth and rate limiting, relational or NoSQL data models, Redis caching, Kafka or RabbitMQ event flows, Elasticsearch or ClickHouse for search and analytics, and WebSocket channels for realtime features.
Should I use REST, GraphQL, or gRPC for my API?
It depends on who calls the API. REST is the simplest choice for public or partner APIs and works well with HTTP caching. GraphQL suits frontends that need flexible queries across many related resources without several round trips. gRPC fits internal service-to-service traffic where low latency and strict contracts matter. Many systems use more than one, for example gRPC internally and REST or GraphQL at the edge.
When does it make sense to move from a monolith to microservices?
Usually only when a single codebase is the actual bottleneck: teams block each other on deploys, one part of the system needs to scale very differently from the rest, or failures in one area take everything down. Microservices add network calls, distributed data, and operational overhead, so a well-structured monolith is often the better choice for smaller teams. We split services where the evidence supports it, not by default.
How do you find out why a backend or database is slow?
We start by measuring rather than guessing. That means profiling the slowest API endpoints, reading query plans, checking missing or unused indexes, looking at connection pooling and lock contention, and reviewing how data is cached. The output is a short list of bottlenecks ranked by impact, so fixes such as an index, a cache layer, or a read replica go where they actually change response times.
What do we receive at the end of a backend project?
You receive working services plus the material your team needs to run them: documented and versioned API endpoints, schema and migration scripts, event and topic maps for any message queues, and load-test results from before handoff. Migrations come with the verification steps and rollback path used during cutover, so your engineers can understand, operate, and extend the system without depending on us.