EU LLM Hosting Playbook: Cost‑Smart Strategies for CTOs Scaling GenAI
“Your GPU bill shouldn’t frighten your CFO more than your LLM thrills your users.”
European tech scale‑ups are racing to build AI features, but the first real‑world bottleneck isn’t the model—it’s where and how to host it. One wrong choice can triple run‑rate or derail data‑residency promises. In this playbook we decode the variables, pricing mathematics, and EU‑friendly deployment patterns so you can scale Large Language Models (LLMs) without burning capital or compliance headroom.
GDPR & Schrems II pressure: Data can’t hop the Atlantic without legal gymnastics.
Latency to EU customers: 20–40 ms wins user retention vs. 120 ms trans‑atlantic hops.
Capital efficiency: GPU cost inflation punishes runaway experiments.
Vendor diversity: Reduces concentration risk when hyperscalers tighten quotas.
“Location matters—put your LLM where your users and regulators live.”
Archetype | Typical Fit | Core Stack | Cost Floor | Ops Overhead |
|---|---|---|---|---|
Single‑GPU Cloud VM | Chatbots, POCs | A100/H100 VM + vLLM | €0.15–0.38 / M tokens (24×7) | Low |
Spot / Batch Pool | Nightly doc summarization | K8s Jobs + Spot GPUs | €0.08–0.19 / M | Medium |
Reserved / Bare‑Metal Cluster | Steady SaaS traffic | 1‑ & 3‑yr commits | €0.10–0.25 / M | Medium |
Managed API / Serverless | Spiky, unpredictable | Pay‑per‑token API | €0.46 / M | Very Low |
All numbers assume Mistral‑7B at 1 800 tok/s on A100‑80 GB; see cost math below.
“Utilisation is destiny—hardware wins only when it’s busy.”
At full throttle a single A100‑80 GB pushes ≈6.5 M tokens per hour. Blend that with EU hourly GPU rates and you get the €/token curves:
Commitment | GPU €/h | €/1 M tokens (24×7) | 8 h/day | 1 h burst |
|---|---|---|---|---|
On‑demand | €1–2.5 | 0.15–0.38 | 0.46–1.14 | 3.7–9.2 |
Spot | ~‑50 % | 0.08–0.19 | 0.23–0.57 | 1.8–4.6 |
1‑yr Reserved | ~‑25 % | 0.11–0.29 | 0.33–0.86 | 2.7–7.0 |
3‑yr Reserved | ~‑35 % | 0.10–0.25 | 0.30–0.75 | 2.4–6.0 |
Own Rack | Cap‑ex | ~0.23 | 0.69 | 5.5 |
Managed API | — | 0.46 | same | same |
Reality check: OVHcloud lists A100‑180 at €2.75/h (corporate.ovhcloud.com), while Scaleway’s H100 clocks in at ~€2.73/h (datacrunch.io). Mistral‑7B API clocks €0.25 input + €0.25 output per million tokens (llmpricecheck.com).
Interactive Chat (≤20 req/s, p95 < 1 s) → Single‑GPU VM or Managed API.
Nightly ETL summarisation (bulk) → Spot pool with K8s Jobs.
Privacy‑critical fine‑tuning (70 B) → Bare‑metal cluster or colocation.
Edge/field devices (offline) → Quantised 4‑bit model on Jetson.
A fintech in Berlin shaved 52 % off inference spend by moving from always‑on A100s to serverless endpoints at identical p95 latency, because traffic spiked only during trading hours.
Confusing VRAM with Bandwidth: Sharding solves capacity but not memory bandwidth.
Ignoring egress fees: Moving embeddings to vector DB in another region can dwarf GPU cost.
Under‑estimating cold‑start in serverless endpoints - prefetch weights or use warm pools.
Assuming “EU region” means GDPR compliance: You still need DPA and sub‑processor review.
Track €/token, not €/GPU‑hour. Dashboard it in Grafana.
Batch everywhere: vLLM or TensorRT‑LLM autogroups requests.
Autoscale by queue depth, not CPU—token generation is mostly compute‑bound.
Pre‑generate KV cache for system prompts to trim 20–30 % latency.
Quantise read‑heavy models (INT8/4‑bit) for 1.7–2× cost cut with negligible quality loss.
European AI adoption no longer hinges on model availability; it lives and dies on operational cost and compliance execution. Choose an archetype that mirrors your traffic shape, monitor €/token religiously, and evolve towards dedicated hardware only when utilisation justifies the leap.
Need hands‑on help? Our cloud architects specialise in cloud cost optimisation and infrastructure as code migrations—reach out for an AWS migration support session.
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