AI Inference Platform 2026: 7 Ways to Run Models in Production, From Free to Enterprise
Self-hosted, serverless, edge, managed - the options for running inference in production multiplied. This guide ranks seven real platforms by cost, control and effort, so you can pick without a procurement cycle.
💡 What You Will Learn
Self-hosted, serverless, edge, managed - the options for running inference in production multiplied. This guide ranks seven real platforms by cost, control and effort, so you can pick without a procur
📜 Table of Contents
The Seven Ways to Run Inference
Every option below is real and in use in 2026. They are ranked by the effort-to-control ratio, from maximum control to maximum convenience (stars fetched 2026-08-12).
1. Your own GPU, vLLM (88,784 stars) - maximum control, minimum cost per token at scale. Effort: you are the ops team. Best for: steady traffic, teams with Linux skills.
2. GPU cloud VPS + vLLM - rent a 4090/A100 box ($0.30-1.50/hour), run the same stack. Same control as #1, no hardware commitment. Best for: teams without capital or data-center space.
3. Kubernetes + KServe - the enterprise path: autoscaling, canaries, multi-model. Effort is real (you run a cluster), control is total. Best for: orgs already on K8s.
4. Serverless GPU (RunPod, Modal, Banana) - upload your container, pay per second of GPU. Scales to zero. Best for: spiky traffic, batch jobs, prototypes that might go viral.
5. Managed model platforms (Databricks Mosaic AI, Vertex AI, Sagemaker) - the big-cloud option. One ecosystem for data + serving; node-hour or per-token billing. Best for: teams already inside that cloud.
6. Hosted open-model APIs (Together, Groq, Fireworks, OpenRouter) - zero ops, per-token pricing, OpenAI-compatible. Groq is famous for raw speed via LPU hardware. Best for: teams that want to ship today and never touch a GPU.
7. Edge / on-device (llama.cpp 123,466 stars, ONNX Runtime, WebGPU) - the model runs on the user's machine. Zero server cost, full privacy, offline capable. Best for: privacy-critical or offline-first products.
The Two-Axis Decision
Plot your situation on (traffic predictability, ops willingness): - Predictable + willing: #1 or #2 (self-host, save money). - Spiky + not willing: #4 or #6 (serverless or hosted API). - Predictable + not willing: #5 or #6 (managed or hosted). - Privacy-critical: #7 (edge), or #1/#2 behind your firewall.
The Migration Trick
Build against the OpenAI-compatible API from day one. Then moving between #2, #4, #5 and #6 is a base-URL change, not a rewrite. This single decision keeps your options open longer than any framework choice.
The Cost Snapshot (rough 2026 prices)
- Self-hosted 7B on owned GPU: ~$0.10-0.30/hour amortized, unlimited tokens.
- Serverless GPU: $1-3/GPU-hour, pay for what you use.
- Hosted open-model APIs: $0.05-0.30 per million tokens.
- Managed cloud: node-hour or per-token; usually the priciest for steady load.
FAQ
What is the cheapest way to run inference? Owned GPU + vLLM at steady volume; hosted APIs for tiny volumes.
Which is fastest? Groq (LPU hardware) for many open models; self-hosted vLLM with good GPUs is close behind.
Can I mix platforms? Yes - that is the point of the OpenAI-compatible API: route different models to different platforms.
Related reads: AI Inference 2026, AI Model Serving 2026, LLM API Cost Comparison 2026.
