AI Model Serving Platform 2026: Managed vs Self-Hosted, and the 6 Platforms to Know
Hosted model platforms promise zero-ops inference; self-hosting promises lower cost. Both are right, in different situations. Here are the six platforms that matter in 2026 and the decision framework.
💡 What You Will Learn
Hosted model platforms promise zero-ops inference; self-hosting promises lower cost. Both are right, in different situations. Here are the six platforms that matter in 2026 and the decision framework.
📜 Table of Contents
Managed vs Self-Hosted: The Real Trade
Managed platforms trade money for time: you skip GPU ops, autoscaling and driver updates, and pay per token or per hour. Self-hosting trades time for money: you run vLLM on your own GPU and pay electricity instead of margins. The crossover depends on volume, spikiness and ops skill (stars fetched 2026-08-12).
The Managed Platforms
1. Together AI - hosted open-model inference with per-token pricing; fast, developer-friendly, OpenAI-compatible. Great for teams that want open models without ops.
2. Groq - the speed story: LPU hardware serving open models at extreme token rates. Best when latency is the product.
3. Fireworks AI - enterprise-flavored open-model hosting with fine-tuning and deployment options.
4. OpenRouter (and similar aggregators) - one API, many models, per-model pricing; the 'routing layer' approach rather than a single provider.
5. Cloud platforms (Vertex AI, Databricks Mosaic AI, Sagemaker) - the big-cloud managed option, best when you already live in that ecosystem (see Vertex AI Model Serving 2026 and Databricks Mosaic AI Model Serving 2026).
The Self-Hosted Stack
- vLLM (88,784 stars) - the engine: continuous batching, PagedAttention, OpenAI-compatible API.
- SGLang (31,684 stars) - the speed challenger for structured outputs.
- llama.cpp (123,466 stars) - CPU and edge inference.
- LiteLLM (56,118 stars) - the gateway that fronts both hosted and local models under one API.
The Decision Framework
| Your situation | Choice |
|---|---|
| Ship today, no GPU skills | Managed (Together/Groq) |
| Steady high volume | Self-hosted vLLM |
| Spiky traffic | Managed (scales to zero) |
| Privacy-critical data | Self-hosted behind firewall |
| Multi-provider flexibility | Aggregator (OpenRouter) |
| Already on a big cloud | That cloud's platform |
The Hybrid That Wins in 2026
Build against the OpenAI-compatible API from day one. Run steady load on self-hosted vLLM, overflow to a managed platform during spikes, and route by task: cheap local models for simple queries, hosted frontier models for hard ones. Teams doing this cut inference costs 50-80% versus all-managed.
FAQ
Which is faster, Groq or self-hosted? Groq wins raw speed on supported models; self-hosted vLLM on good GPUs is close and cheaper at volume.
Is managed inference worth the premium? For spiky or low-volume traffic, yes - the zero-ops and scale-to-zero beat the per-token premium.
Can I switch later? Yes, if you used the OpenAI-compatible API - moving between platforms is a base-URL change.
Related reads: AI Model Serving 2026, AI Model Serving Framework 2026, Vertex AI Model Serving 2026.
