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AI Systems Programming – Backend, API & AI Engine

Dedicated backend and API for your AI system. Microservices architecture, AI model integration, scaling to millions of requests, and readiness for future extensions.

Author: Paweł Wiszniewski · Updated:

Key takeaways

Dedicated backend and API for your AI system. Microservices architecture, AI model integration, scaling to millions of requests, and readiness for future extensions.

  • ›Scalable AI backend ready for millions of requests
  • ›Multi-model AI integration with routing and fallback
  • ›LLM response caching with Redis

Price: from €3,500

SERVICE DETAILS

I design and build backends for AI systems — the infrastructure behind a working product. Typical projects: REST/GraphQL APIs integrating multiple AI models (routing, fallback, cost management), task queue systems with Celery/Bull and Redis, real-time data processing pipelines, microservices architecture with Docker and Kubernetes, or serverless on AWS Lambda/Google Cloud Run. AI systems programming is not just calling the OpenAI API — it is caching, rate limiting, token cost logging, retries with exponential backoff, and latency monitoring for every model.

› INVESTMENT:

from €3,500
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Paweł Wiszniewski · my approach

What I do differently

An AI backend differs from a regular one in that its most expensive and least predictable part is the model. So I design it with model routing, caching, queues and rate limits, and log every call with its cost and latency. In the PozyczkoBank project I combined Python Serverless with Gemini so automation took over most of the editorial work. I plug AI answer quality tests into CI just like code tests — I write about how to do this on the blog.

Case study: PozyczkoBank Fintech AI

My take and a real-world example

My take

I think an AI app built on a single model provider with no fallback is a single point of failure. A model is an external service like any other — and it can stop working just the same.

From the market

On 10 June 2025 the OpenAI API and ChatGPT returned elevated error rates worldwide for several hours. Companies whose products ran on a single model were effectively offline. That's why I design AI backends with fallback to another provider's model, queues and caching.

Sources: OpenAI status: elevated error rates for API and ChatGPT, 10 June 2025

Who this is for

  • Companies with a frontend or product that need a solid AI backend.
  • Teams whose AI prototype can't handle growing traffic.
  • Products combining several AI models, data sources and external systems.
  • Organizations that need cost control, logging and security for AI calls.

What you get

  • API and service architecture design for the AI system.
  • Model integration (OpenAI, Anthropic, Google, open weights) with routing and fallback.
  • Job queues, caching and rate limits that keep cost and latency in check.
  • Logging, tracing and answer quality monitoring.
  • API documentation and load tests.

How results are measured

  • API latency and error rate.
  • AI cost per request or per user.
  • Service availability and incidents.
  • Model answer quality in evaluations.

Key Benefits

Scalable AI backend ready for millions of requests — designed for growth, not rebuilt every time you hit the next traffic threshold.

Multi-model AI integration with routing and fallback — when the primary model is unavailable or too expensive, the system automatically switches to Claude or Gemini.

LLM response caching with Redis — reduces token costs by 30-60% for repeated queries without degrading answer quality.

Cost and latency monitoring for every AI model — you know exactly what you pay per call and can optimise model selection for ROI.

API documentation (OpenAPI/Swagger) and integration tests — your frontend developers and integration partners connect without asking about every endpoint.

The Process

1

Technical requirements and architecture

I define requirements: expected request volume, latency SLA, AI models, data privacy constraints. I design the system architecture — from database schema to the choice between serverless and containers.

2

Core API and AI model integration

Building the backend foundation: API endpoints, AI model routing logic, authentication system, and API key management. Monitoring (Prometheus + Grafana or Datadog) configured from day one.

3

Performance and cost optimisation

I implement caching, task queuing, rate limiting, and fallback strategies. Load testing (k6 or Locust) and optimisation until the target SLA is met.

4

Deploy, CI/CD, and documentation

Configuring the CI/CD pipeline (GitHub Actions or GitLab CI), deployment to the target infrastructure (AWS, GCP, Hetzner), API documentation, and an operational runbook for your ops team.

Dedicated AI backend vs single direct API call vs generic backend

CriterionDedicated AI backendDirect API calls onlyGeneric backend
Multi-model routingRouting with automatic fallbackSingle provider, no fallbackNot designed for it
Token cost controlCaching cuts repeat costsPays full price every callNo AI cost awareness
ScalabilityBuilt for millions of requestsBreaks under loadDepends on design
ReliabilityRetries, rate limits, monitoringFails on provider outageGeneric error handling
Cost / latency visibilityPer-model loggingHard to traceNot tracked
Model swappabilityAbstraction layer, no code changeHardcoded to one providerRefactor needed
Best forProduction AI systems at scaleQuick experimentsNon-AI CRUD apps

Common mistakes

  • Calling the most expensive model for every task, including the simplest.
  • No timeouts, retries or fallback when a model provider goes down.
  • Logging without cost and latency, which makes optimization hard later.
  • No answer quality tests when prompts or models change.

When this service is not worth it

  • The app makes a few AI calls a day — a simple integration is enough.
  • You already have a mature backend team that only needs consulting.
  • Requirements are unclear and there's no product to use the backend yet.

Frequently Asked Questions

How does an AI backend differ from a standard backend?

AI systems programming requires handling AI model specifics: variable latency (100ms–30s), streaming responses, token cost management, semantic caching, and multi-provider fallback. A different set of challenges from typical CRUD APIs.

Which cloud providers do you support?

AWS (Lambda, EC2, ECS, RDS), Google Cloud (Cloud Run, GKE, BigQuery), Hetzner (VPS for cost-conscious projects), and Vercel/Render for smaller deployments. The choice depends on project requirements and operational budget.

Can I swap AI models without changing the application code?

That is one goal of a well-designed architecture — an abstraction layer over model APIs so that swapping an OpenAI model for Claude or Gemini requires no changes to business logic. Model configuration lives in ENV variables or an admin panel.

How do you price backend projects?

Based on complexity: number of endpoints, external integrations, performance requirements, and test coverage level. A simple backend API for an existing application starts at €3,500. Complex microservices systems with multiple AI models and full monitoring — from €8,000.

Can you add AI features to an existing backend?

Yes. I often add the AI layer to an existing system as a separate service with its own API, so the rest of the app doesn't need rewriting.

How do you control AI model costs?

Routing simple tasks to cheaper models, response caching, batch processing where answers aren't needed instantly, per-user limits and cost monitoring for every call.

How do you keep data secure in an AI backend?

Encryption, service-level access control, masking personal data before it reaches the model, data processing agreements with model providers and audit logs. For stricter requirements models can run on private infrastructure.

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BIAŁYSTOK, PL
+48 732 022 086pawel.wiszniewski95@gmail.com