Production AI engineering
Stop guessing your AI stack.
Set your use case and constraints below. Get ranked, good-fit LLM architectures — real models, real providers, real monthly costs — updating live as you decide.
Llama 3.2 1b Instruct
- • Self-host Llama 3.2 1b Instruct on 1× RTX 3090 (~2 GB).
- • ~$161/mo dedicated · est. 457 tok/s decode.
- • Scored for RAG Q&A (interactive): cost 92 · latency 95 · quality 68.
Llama 3.1 8B
- • Cerebras serves Llama 3.1 8B as a managed API — no infra to run.
- • ~$390/mo at your volume · $0.10/M tokens blended.
- • Scored for RAG Q&A (interactive): cost 80 · latency 97 · quality 71.
⚠ Data leaves your boundary — verify the provider DPA/compliance.
- CerebrasAPIfastcheapest$390/moTry
Qwen3 30B A3B
- • Self-host Qwen3 30B A3B on 2× RTX 3090 (~36 GB, tensor parallel).
- • ~$321/mo dedicated · est. 340 tok/s decode.
- • Scored for RAG Q&A (interactive): cost 83 · latency 83 · quality 77.
⚠ MoE: all experts occupy memory even though only some compute.
Llama 3.1 8B
- • DeepInfra is the cheapest of 2 providers serving Llama 3.1 8B — full price stack below.
- • ~$84/mo at your volume · $0.02/M tokens blended.
- • Scored for RAG Q&A (interactive): cost 100 · latency 75 · quality 71.
⚠ Data leaves your boundary — verify the provider DPA/compliance. Listed cost is per-token list price; committed-use, batch and enterprise contracts differ.
Qwen2.5 7B Instruct
- • SiliconFlow is the cheapest of 3 providers serving Qwen2.5 7B Instruct — full price stack below.
- • ~$195/mo at your volume · $0.05/M tokens blended.
- • Scored for RAG Q&A (interactive): cost 89 · latency 75 · quality 78.
⚠ Data leaves your boundary — verify the provider DPA/compliance. Listed cost is per-token list price; committed-use, batch and enterprise contracts differ.
GPT OSS 20B
- • Self-host GPT OSS 20B on 2× RTX 3090 (~24 GB, tensor parallel).
- • ~$321/mo dedicated · est. 312 tok/s decode.
- • Scored for RAG Q&A (interactive): cost 83 · latency 83 · quality 76.
⚠ MoE: all experts occupy memory even though only some compute.
THUDM/GLM-4-9B-0414
- • SiliconFlow serves THUDM/GLM-4-9B-0414 as a managed API — no infra to run.
- • ~$335/mo at your volume · $0.09/M tokens blended.
- • Scored for RAG Q&A (interactive): cost 82 · latency 72 · quality 82.
⚠ Data leaves your boundary — verify the provider DPA/compliance.
- SiliconFlowAPImediumcheapest$335/moTry
GPT OSS 120B
- • DeepInfra is the cheapest of 10 providers serving GPT OSS 120B — full price stack below.
- • ~$309/mo at your volume · $0.08/M tokens blended.
- • Scored for RAG Q&A (interactive): cost 83 · latency 99 · quality 61.
⚠ Data leaves your boundary — verify the provider DPA/compliance. Reasoning model: extra reasoning tokens raise real cost and latency. Listed cost is per-token list price; committed-use, batch and enterprise contracts differ.
Step 3.5 Flash
- • SiliconFlow serves Step 3.5 Flash as a managed API — no infra to run.
- • ~$510/mo at your volume · $0.13/M tokens blended.
- • Scored for RAG Q&A (interactive): cost 77 · latency 93 · quality 68.
⚠ Data leaves your boundary — verify the provider DPA/compliance. Reasoning model: extra reasoning tokens raise real cost and latency.
- SiliconFlowAPImediumcheapest$510/moTry
Claude Opus 4.8
- • Google Vertex is the cheapest of 3 providers serving Claude Opus 4.8 — full price stack below.
- • ~$31.5k/mo at your volume · $8.08/M tokens blended.
- • Scored for RAG Q&A (interactive): cost 24 · latency 35 · quality 96.
⚠ Data leaves your boundary — verify the provider DPA/compliance. Reasoning model: extra reasoning tokens raise real cost and latency. Listed cost is per-token list price; committed-use, batch and enterprise contracts differ.
Disclosure: some managed-provider links are affiliate links. Ranking is driven only by your inputs and real numbers — never by who pays. A provider appears only when it fits.
We go deep across three lanes
Silicon
Hardware & serving
GPU selection, VRAM math, quantization tradeoffs, KV-cache tuning and cost-per-token analysis with reproducible numbers.
Models
Evals, architecture & tradeoffs
Open-weight model landscape, architecture comparisons, and closed-API vs self-hosted decision frameworks with real data.
Stack
Serving frameworks & orchestration
vLLM, SGLang, TGI and TensorRT-LLM head-to-heads, plus OSS orchestration with Dify, Windmill and n8n.
Latest benchmarks
All posts →You don't need an H100: matching GPU workload to hardware
A real diffusion-TTS pipeline case study. Why memory bandwidth — not parameter count — decides your GPU, and how to burst to cloud GPUs for $0.40 a render.
vLLM in 2026: the complete production setup guide
Install, serve, benchmark and tune vLLM for production inference — with a fully reproducible config and real TTFT/throughput numbers on an RTX 4090.