vLLM vs Text Generation Inference: 2026 Showdown
Head-to-head operational comparison. Pricing, performance, real-world tradeoffs.
vLLM, Ray, MLflow, vector DBs — running ML in production without burning the org down.
Self-hosted inference is finally cheaper than the API tier for most production workloads above ~100M tokens/month. vLLM with multi-LoRA can serve 5+ specialist adapters from a single 8GB GPU. The vector DB war is mostly over (pgvector + a good index strategy beats most dedicated solutions for under 10M vectors).
These guides cover real deployments — quantization, batching strategies, eval pipelines — not 'here's how to call OpenAI' tutorials.
Head-to-head operational comparison. Pricing, performance, real-world tradeoffs.
Step-by-step working configuration. Production-grade setup, no toy examples.
Ranked picks based on real deployments. No fabricated star ratings.