// DEPLOY

From commit to production in minutes.

Automated. Monitored. Cost-optimized.

Automated deployment pipelines with scaling, monitoring, and cost optimization built in. Ship with confidence, scale on demand, and only pay for what you use.

Red Bull
SAP
Rotax
Bundesliga
LASK
OeFB
Vinzenz Gruppe
Linde
KEBA
TSV 1860
Stadium ADS
DasMerch
FoxyFitness
LAOLA1
Event24
Gastro Fighters
Peter Affenzeller
Red Bull
SAP
Rotax
Bundesliga
LASK
OeFB
Vinzenz Gruppe
Linde
KEBA
TSV 1860
Stadium ADS
DasMerch
FoxyFitness
LAOLA1
Event24
Gastro Fighters
Peter Affenzeller
// AUTOMATED PIPELINES

Ship with confidence, every time.

Every code change is automatically tested, validated, and deployed. Nothing reaches your users without passing comprehensive quality checks — type safety, automated tests, and security scans. Your team ships faster because the pipeline catches issues before they become problems.

Zero-downtime deployments are the standard. Your users never see a maintenance window, and updates roll out smoothly. When something doesn't work as expected, rolling back takes seconds — restoring everything to a known good state instantly.

The deployment infrastructure itself is version-controlled and validated. No manual server configurations, no "it works on my machine" surprises. What runs in testing is exactly what runs in production, every time.

Deployment Pipelineautomated
Quality Check18s
Test45s
Build52s
Deploy19s
Code to production in under 3 minutes
zero downtime · instant rollback
// FLEXIBLE INFRASTRUCTURE

No vendor lock-in. Ever.

We choose the right infrastructure for each workload — not the most expensive or the most convenient. Web applications deploy to the edge for global performance. Heavy AI processing runs on the compute infrastructure that matches the demand.

Cost-effective options are used where they make sense. Not every workload needs premium cloud pricing. Batch processing, development environments, and local AI inference run on dedicated infrastructure at a fraction of the cost — without compromising reliability.

The same code runs identically everywhere. Containerization ensures consistency across all environments. If you ever want to switch providers, the migration is a configuration change — not a rewrite. Your infrastructure strategy stays flexible as your business evolves.

Infrastructure Mapmulti-cloud
VercelFast Global Frontend
Websites and apps delivered from the nearest edge location worldwide
AWSAI Processing Power
GPU-accelerated AI inference and scalable compute for heavy workloads
HetznerCost-Optimized Workloads
Dedicated servers for batch processing and private AI at a fraction of the cost
Same deployment · every platform
// SMART SCALING

Pay for what you use. Nothing more.

Infrastructure scales based on actual demand. Traffic spike? New capacity spins up automatically. 3 AM quiet period? Resources scale down to near-zero. You pay for what you use, not for peak capacity sitting idle.

Smart provisioning goes beyond basic auto-scaling. Predictable traffic patterns get scheduled capacity. Batch processing runs on discounted infrastructure. Baseline load gets reserved pricing. The combination can cut infrastructure costs in half compared to naive cloud usage.

AI workloads have unique scaling needs that we account for: model loading time, memory constraints, and request batching. The infrastructure handles these gracefully — pre-warming capacity, batching for efficiency, and routing overflow intelligently during demand spikes.

Auto-Scalingsmart resource management
Capacity vs Traffic (24h)auto
0:0012:0023:59
Economy
60-70% savings
batch & background jobs
Guaranteed
30-40% savings
always-on baseline
Flexible
pay as you go
traffic spikes
AI Scaling Strategy
pre-warm capacitybatch requestscloud overflow
// REAL-TIME MONITORING

See everything. Fix anything.

Dashboards show the complete picture: application performance, infrastructure health, AI model quality, and cost tracking. Your team sees exactly what's happening, in real-time, with the context needed to make fast decisions.

Proactive alerting catches problems before users notice. Performance degradation, cost anomalies, quality shifts — each triggers the right response before it impacts your business. When an AI model's output quality drifts, you know immediately, not when customers complain.

Intelligent anomaly detection learns your system's normal patterns and flags deviations that static rules miss: gradual slowdowns, subtle quality changes, or cost creep that stays under individual thresholds but adds up over time.

Monitoring · System Overview
all systems normal
Web Frontend
99.99%
API Backend
99.97%
AI Processing
99.94%
Database
99.99%
Alert Rules
Response time stays fastOK
Error rate stays lowOK
AI output quality monitoredOK
Cost anomalies detectedOK
AI Anomaly Detection
learns patterns · detects gradual drift · flags cost creep
Pipeline #847passed
main · 2m 14s
Commit0s
Quality Check18s
Test45s
Build52s
Deploy19s
Test Results
Passed142
Skipped3
Failed0
Deploy Target
Vercel · Edge
AWS · eu-west-1
Hetzner · nbg1
feat: add streaming SSR for LLM responses
a3f2e8d · 2 minutes ago
// TECH STACK

Built with

AWSVercelHetznerGitLab CIGitHub ActionsDockerGrafana

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