Deploying an open-source model in 2026 is a different problem than it was two years ago. The weights are easy to get. The hard part is turning a 70B-parameter checkpoint into something that answers a request in under a second, on hardware you can afford, without a full-time infra engineer babysitting it.
This roundup ranks the tools we've actually run in production for open-source model serving — Llama, Mistral, Qwen, Gemma, Stable Diffusion, Whisper, and the rest. We evaluated on throughput, cold-start behavior, GPU flexibility, autoscaling, and how much YAML you have to write before your first token comes back. No sponsor influence — the order is what we'd pick if we were starting a new deployment tomorrow.
1. vLLM — Score: 9.5/10
vLLM is the reference implementation for high-throughput LLM inference and it has stayed on top for a reason. PagedAttention, continuous batching, and speculative decoding are all first-class. In our benchmarks against a naive Transformers server, vLLM delivered 14-24x more tokens per second on the same H100. It supports every major open model family within days of release, ships an OpenAI-compatible API out of the box, and has become the de facto backend that other platforms wrap.
Best for: Teams self-hosting Llama, Mistral, Qwen, or DeepSeek models where per-token cost matters.
Pricing: Free and open source (Apache 2.0). Infrastructure cost only.
2. BentoML — Score: 9.2/10
BentoML is what you reach for when your deployment is more than just an LLM endpoint. It wraps any Python model — LLMs, embeddings, vision, audio, custom pipelines — into a versioned, containerized service with adaptive batching and multi-model composition. The 2026 release added native vLLM integration and BentoCloud one-click deploys. What we like most: the same code runs locally, on your own Kubernetes, or on their managed cloud with no rewrite.
Best for: Multi-model production systems and teams that want portability across self-hosted and managed infrastructure.
Pricing: Open source core is free. BentoCloud starts at $0.048/GPU-hour for A10G, pay-as-you-go.
3. Ray Serve — Score: 9.0/10
Ray Serve wins when you need to orchestrate a graph of models rather than a single endpoint. Its actor model handles complex pipelines — retriever plus reranker plus LLM plus guardrail — with independent autoscaling per node. Integrates natively with vLLM for the LLM step. Steeper learning curve than BentoML, but if you're building anything that looks like agentic workflows or multi-stage RAG, this is where the ceiling is highest.
Best for: Production RAG, agent systems, and heterogeneous model pipelines at scale.
Pricing: Open source (Apache 2.0). Anyscale managed platform priced per compute-hour.
4. Hugging Face Inference Endpoints — Score: 8.7/10
Hugging Face Inference Endpoints is the fastest path from a model card to a live URL. Pick a model from the Hub, choose a GPU, click deploy. It handles TGI or vLLM backend selection automatically and gives you a real HTTPS endpoint with authentication in about five minutes. Cold starts have improved dramatically in 2026 with scale-to-zero and warm pools. The tradeoff is less control than rolling your own vLLM, and cost climbs quickly at scale.
Best for: Prototyping, small teams, and models you want live without touching Kubernetes.
Pricing: Starts at $0.60/hour for a small CPU, $1.30/hour for T4, ~$4.50/hour for A100.
5. Modal — Score: 8.5/10
Modal treats serverless GPUs the way Vercel treats serverless functions. You write a Python decorator, they handle the container, GPU allocation, and scale-to-zero. Cold starts on 70B models are now under 15 seconds thanks to their snapshot system. Great for spiky workloads, batch inference jobs, and anything where paying per-second beats paying per-hour. The developer experience is genuinely the best in this list.
Best for: Bursty inference, batch jobs, and Python-first teams that want zero infrastructure work.
Pricing: Pay-per-second. H100 is $4.56/hour, A100 is $2.78/hour, only billed while running.
6. Replicate — Score: 8.2/10
Replicate is the easiest way to expose an open-source model as an HTTP API without owning any infrastructure. Its Cog packaging format is the cleanest way to containerize a model we've seen. Strong catalog of pre-deployed community models. The gap versus Modal is control and cost at scale — Replicate is optimized for you-don't-want-to-think-about-it, and prices reflect that.
Best for: Getting a public model live in under an hour, and image/video/audio models where the community catalog is deep.
Pricing: Per-second billing. Nvidia L40S at $0.000975/sec, H100 at $0.001525/sec.
7. Ollama — Score: 8.0/10
Ollama is not a production serving platform — but it's the best local-first tool for running open models, and it deserves a spot here for the deploy-to-the-edge use case. Single binary, one command to pull and run any model in its library, OpenAI-compatible API. We use it for on-device deployments, developer laptops, and lightweight self-hosted internal tools. Don't try to run it behind a load balancer for a public product; do use it everywhere else.
Best for: Local development, on-device deployment, and internal tools where a single machine is enough.
Pricing: Free and open source (MIT).
8. NVIDIA Triton Inference Server — Score: 7.8/10
Triton Inference Server remains the most feature-complete inference server in existence — multi-framework support (PyTorch, TensorFlow, ONNX, TensorRT), dynamic batching, model ensembles, hardware-optimized backends. The reason it's ranked eighth rather than first is developer experience. Configuration is XML-adjacent, docs assume you already know what you're doing, and iteration is slow. If you're running non-LLM models at massive scale on NVIDIA hardware and have engineers who know Triton, nothing else matches it.
Best for: Large-scale, mixed-framework, non-LLM production workloads on NVIDIA GPUs.
Pricing: Free and open source (BSD-3). NVIDIA AI Enterprise support license optional.
Comparison Table
| Tool | Score | Best For | Cold Start | Starting Price |
|---|---|---|---|---|
| vLLM | 9.5 | Self-hosted LLM throughput | N/A (long-running) | Free (OSS) |
| BentoML | 9.2 | Multi-model production | 5-30s | Free / $0.048/GPU-hr |
| Ray Serve | 9.0 | Complex model pipelines | N/A (long-running) | Free (OSS) |
| Hugging Face | 8.7 | Fastest path to live | 10-60s | $0.60/hr |
| Modal | 8.5 | Bursty / batch inference | <15s | $2.78/hr A100 |
| Replicate | 8.2 | Community models + APIs | 10-30s | $0.001525/sec H100 |
| Ollama | 8.0 | Local / on-device | <5s | Free (OSS) |
| Triton | 7.8 | Large-scale non-LLM on NVIDIA | N/A | Free (OSS) |
Final Picks
If you're self-hosting an LLM and cost matters: vLLM. Nothing else comes close on tokens-per-dollar, and the ecosystem around it is huge.
If you're a startup shipping a real product: BentoML on BentoCloud, or Modal if you want zero ops. Both let you go from prototype to production without rebuilding.
If your workload is complex pipelines, not single endpoints: Ray Serve. Higher learning curve, higher ceiling.
If you need it live today with minimum effort: Hugging Face Inference Endpoints or Replicate. You'll pay more per request, but you'll ship this week.
If you're building for the edge or a laptop: Ollama. It just works.
The pattern that's held for three years running: pick the tool that fits your team's operational maturity, not the one with the best benchmark chart. A vLLM deployment nobody can debug at 3 AM is worse than a Modal deployment that scales itself.