Fordel StudiosInfrastructure that runs AI workloads without surprising your budget.
The inference cost crisis is real: teams that launched with direct OpenAI API calls are hitting unit economics walls at scale. vLLM and TGI make self-hosting viable for high-volume workloads. INT4/INT8 quantization cuts memory requirements significantly. We design infrastructure that matches your workload to the right cost model — API, self-hosted, or hybrid.
The inference cost crisis has a simple shape: LLM API pricing is linear with token volume, and product margins often are not. Teams that built on GPT-4 API calls at prototype scale discover at production scale that the cost model does not work for their unit economics. The infrastructure response is not just "use a cheaper model" — it is understanding the full optimization surface: model routing, semantic caching, quantization, batch processing, and the self-hosting crossover point.
vLLM (UC Berkeley, open source) and TGI (Text Generation Inference, HuggingFace) changed the self-hosting calculus by making high-throughput LLM serving on commodity GPU hardware practical. vLLM's PagedAttention algorithm dramatically improves GPU memory utilization. INT4 and INT8 quantization reduces model memory footprint by 4-8x with manageable accuracy trade-offs on many tasks. The crossover point where self-hosting beats API pricing is now within reach for workloads that were API-only two years ago.
| Architecture decision | Common anti-pattern | Right-sized approach |
|---|---|---|
| LLM inference | One large GPU instance, always-on | Auto-scaling with spot fallback, right-sized per model |
| Model serving | Raw HuggingFace inference | vLLM or TGI for throughput, quantized for memory efficiency |
| API vs self-hosted | API for everything | API for low-volume/complex, self-hosted for high-volume/simple |
| Training pipelines | Sequential steps, one machine | Orchestrated DAG with checkpointing and spot instance support |
| Observability | Cloud default metrics | Custom: tokens/sec, latency P95, cost/request, GPU utilization |
We design infrastructure architectures that separate training infrastructure (GPU-heavy, bursty, latency-tolerant) from inference infrastructure (latency-sensitive, auto-scaling, different instance families). This separation lets each be optimized independently. Training runs on spot instances with checkpointing for cost efficiency. Inference runs on auto-scaled on-demand instances sized to measured P95 latency targets.
AI infrastructure layers we build
Profile your actual latency and throughput requirements. Model the cost at current and projected volume for API, self-hosted vLLM/TGI, and quantized variants. Select the architecture that meets latency requirements at acceptable cost.
Deploy vLLM or TGI for self-hosted LLM serving with INT4/INT8 quantization where accuracy trade-offs are acceptable. Configure PagedAttention, continuous batching, and auto-scaling to maximize throughput per GPU.
Kubernetes (EKS, GKE, AKS) for multi-service deployments. Simpler workloads on Cloud Run or ECS. Model serving on SageMaker or Vertex AI where managed scaling is worth the abstraction cost.
Model updates are deployment events with the same canary rollout, evaluation gates, and automated rollback as application deployments. A model that degrades performance on eval triggers rollback before production traffic switches.
Custom metrics for AI workloads: tokens/second, latency P95, cost/request, GPU utilization. Budget alerts by workload type, idle resource detection, and spot instance savings tracking. Cost visibility is built in on day one.
vLLM and TGI model serving
We deploy vLLM (PagedAttention, continuous batching) or TGI (HuggingFace) for self-hosted LLM serving at production throughput. Both deliver significantly higher throughput per GPU than naive inference serving. We configure quantization (INT4/INT8) where accuracy trade-offs are acceptable.
Inference cost modeling
Before recommending self-hosted vs. API, we model the actual cost at your volume for both approaches. The API-to-self-hosted crossover point depends on model size, request volume, and hardware costs. We give you the numbers before you commit to an architecture.
AI workload infrastructure patterns
Model artifact versioning and storage, inference endpoint auto-scaling tuned for cold-start latency, training pipeline orchestration with spot instance support and checkpoint/resume, and feature store integration for consistent training/serving.
Deployment automation with eval gates
Model deployments follow the same canary rollout and automated rollback as application deployments — with the addition of evaluation gates that must pass before traffic shifts. A model that regresses on eval does not promote to production.
Infrastructure as code
All infrastructure is defined in Terraform or AWS CDK. Nothing is created manually in the console. Reproducible environments, change history, and the ability to recreate from scratch are the baseline operational requirements we hold ourselves to.
- AI infrastructure architecture document with cost model at current and projected scale
- vLLM or TGI serving setup with quantization configuration and auto-scaling
- Infrastructure as code (Terraform or CDK) for all resources
- CI/CD pipeline with model deployment automation, evaluation gates, and rollback
- Observability stack: AI-specific metrics, dashboards, alerting, and distributed tracing
- Cost governance: tagging taxonomy, budget alerts, idle detection, spot instance configuration
Infrastructure designed for AI workloads avoids two major cost categories: over-provisioned inference charging for idle compute, and training pipelines wasting GPU hours on inefficient orchestration. The self-hosting analysis surfaces the crossover point where switching from API to vLLM/TGI creates meaningful cost reduction.
Common questions about this service.
When does self-hosting with vLLM beat OpenAI API pricing?
It depends on model size, request volume, and latency requirements. The crossover point is lower than most teams expect for high-volume workloads on smaller models. We model the cost for your specific volume before recommending — quoting a generic crossover point is not useful because the variables matter significantly. The analysis covers model serving cost, operational overhead, and the engineering time to maintain self-hosted infrastructure.
What does INT4/INT8 quantization actually cost in quality?
Quantization trade-offs are task-dependent. For classification, extraction, and summarization tasks, INT8 quantization typically produces negligible quality degradation. INT4 is more aggressive and requires evaluation on your specific task. We recommend measuring against your eval dataset before quantizing production models — not relying on benchmark numbers that may not match your use case.
Managed ML platforms (SageMaker, Vertex AI) or self-hosted Kubernetes?
Managed platforms reduce operational overhead significantly and are appropriate for most production workloads. Self-hosted Kubernetes makes sense when you have specific customization requirements, existing Kubernetes expertise, or data residency constraints. The managed vs. self-managed cost difference has narrowed as managed platforms matured — factor in engineering time to operate self-managed infrastructure.
Do you handle ongoing infrastructure management?
We design, build, and hand off with documentation and runbooks. Ongoing retainer-based infrastructure support is available for teams without in-house DevOps capacity. The infrastructure-as-code approach means ongoing maintenance is incremental and transparent rather than tribal knowledge.
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