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Cerebral Hacks

AI Infrastructure & MLOps

The engineering layer behind reliable, observable, and secure AI in production — deployment, monitoring, and access control that holds up under real usage.

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Infrastructure Architecture Foundation

A deployed model isn't a reliable system. We engineer production MLOps.

Deploying a model endpoint is only the first step. We engineer the hardened infrastructure layer that guarantees high availability, cost governance, distributed telemetry, and security compliance under enterprise load.

Cloud Orchestration

Auto-Scaling Container Clusters

Resilient microservices deployment on AWS with auto-scaling compute pools and isolated VPC networking.

How we engineer it: We configure containerized services on ECS/EKS with dynamic scaling policies and zero-downtime rolling updates.

DockerAWS ECSTerraform
Multi-Tenancy

Tenant Isolation & Security

Enterprise security layers ensuring tenant database partitioning, scoped API keys, and credential encryption.

How we engineer it: We build tenant-isolated data partitions, rate-limiting token buckets, and inline PII masking proxies.

PostgreSQL RLSOAuth2 / JWTRedis
Resilience

Dynamic Multi-Model Failover

Intelligent routing across model providers to mitigate provider downtime and rate limit spikes.

How we engineer it: We route requests through a high-availability proxy with exponential backoff retries and fallback to lightweight models.

LiteLLM ProxyFastAPIRedis Cache
Telemetry & Evals

Distributed Tracing & Evals

Real-time monitoring of p95/p99 latency, token expenditures, and automated quality benchmark regression gates.

How we engineer it: We integrate Prometheus/Grafana alerts, Langfuse trace spans, and CI/CD evaluation pipelines for every prompt change.

LangfusePrometheusGitHub Actions
Infrastructure Architecture

How We Architect AI Infrastructure & MLOps

The engineering layer behind reliable, observable, and secure AI in production.

Cloud Deployment

Production Container Orchestration

Hardened container deployments on AWS with auto-scaling compute pools, secure VPC networking, and zero-downtime rolling updates.

Containerized microservices running on ECS / EKS clusters
Dynamic auto-scaling policies based on request concurrency
Isolated VPC subnets with strict security group egress rules
DockerAWS ECSTerraform
Security & RBAC

Multi-Tenant Access Control

Enterprise security architecture enforcing strict tenant isolation, role-based access control, and encrypted credential storage.

Tenant-partitioned databases with Row-Level Security (RLS)
Scoped API keys with rate-limiting and quota enforcement
Automated PII redaction on inbound and outbound payloads
PostgreSQL RLSOAuth2 / JWTRedis
Observability

Execution Tracing & Telemetry

Distributed tracing capturing prompt versions, token consumption, latency breakdowns, and tool execution graphs across every user session.

Span-level tracing for LLM generation and tool executions
Real-time token cost and latency percentile (p95/p99) metrics
Centralized log aggregation with error alerting
LangfusePrometheusGrafana
Evaluation Gates

Reliability & Quality Benchmarks

Automated regression testing pipelines that benchmark model accuracy, response grounding, and failure rates on every code change.

CI/CD evaluation suites testing edge-case synthetic datasets
Grounding and hallucination boundary scoring
Automated prompt drift detection and metric comparison
DeepEval / RagasGitHub ActionsPython
Resilience

Failure Handling & Fallback Routing

Multi-model fallback routing, automated retry policies, and graceful degradation paths when upstream providers experience rate limits.

Dynamic failover between OpenAI, Anthropic, and Gemini
Exponential backoff with jitter on transient provider errors
Fallback to lightweight models during high-traffic spikes
LiteLLM ProxyFastAPIRedis
Optimization

Cost & Latency Optimization

Semantic caching, prompt token compression, streaming response pipelining, and model tiering to minimize cloud operational expenses.

Semantic vector caching in Redis to prevent duplicate LLM calls
Streaming SSE responses with low time-to-first-token (TTFT)
Tiered routing directing simple queries to lightweight SLMs
Redis Vector CachevLLMFastAPI

The 5-Stage Infrastructure Lifecycle

Hardening AI deployments with end-to-end telemetry, automated fallbacks, and cost governance.

01Understand

Traffic patterns & SLAs

Audit latency requirements, request volumes, concurrency limits, and compliance boundaries.

02Design

Resilient cloud topology

Architect VPC networks, container cluster scaling, caching layers, and database replica pools.

03Build

Containerization & CI/CD

Provision Docker containers, automated GitHub Actions pipelines, and infrastructure-as-code.

04Evaluate

Stress testing & failover

Simulate provider outages, measure p99 latency spikes, and benchmark token usage under load.

05Operate

Telemetry & alerting

Configure real-time Prometheus/Grafana alerts, log aggregation, and automated cost alarms.

Frequently Asked Questions

Common questions about our MLOps observability, cloud deployment, security, and cost optimization.

We implement distributed telemetry that captures token usage, input/output schemas, model latency distributions, and tool execution call stacks to rapidly diagnose anomalies.

Your AI works. Is it ready for production?

Bring us the prototype, architecture, or latency challenge. We'll help harden the deployment, configure telemetry, and reduce inference costs.

Talk to our engineersSee how we built Qurofy