Open source alternative to Together AI, Fireworks AI and Baseten
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Serve large language models at production throughput with PagedAttention and continuous batching; runs on any GPU hardware with an OpenAI-compatible API.
vLLM exposes an OpenAI-compatible API endpoint that teams can put behind a reverse proxy or API gateway immediately after installation. PagedAttention and continuous batching handle concurrency, delivering near-optimal GPU utilization without a custom batching loop.
Your inference load is low enough that a simple Hugging Face pipeline handles it, or a managed API is cheaper than operating GPU servers at your scale.
Running inference on your own hardware keeps prompts, completions, and user data off third-party servers. This applies to healthcare, finance, and enterprise teams with compliance requirements that commercial inference APIs cannot satisfy.
Your compliance team has approved a commercial inference provider and the operational cost of maintaining GPU infrastructure outweighs the data residency benefit.
vLLM serves multiple LoRA adapters simultaneously from a single base model, so a team with many task-specific variants can host all of them from one deployment rather than loading the base model separately for each adapter.
You are serving a single model variant; LoRA multiplexing adds configuration complexity that is only justified when managing multiple adapters.
vLLM supports 200+ model architectures from Hugging Face, installs in one command, and exposes a standard API, making it the fastest path to running a newly released open model for evaluation or comparison against a baseline.
The model architecture you need is not in vLLM's supported list; check the documentation before planning your evaluation setup around it.
Running large language models in production is expensive and slow without careful infrastructure work. A standard approach of loading a model and processing one request at a time wastes GPU memory and leaves compute idle between tokens. As request volume grows, naive queuing introduces latency spikes and the GPU's KV cache fills up with fragmented allocations that cannot be reused across requests.
The result is that teams either pay for managed inference APIs at per-token rates that compound quickly at scale, or they write custom batching code that never quite matches the throughput the hardware is capable of. Neither path scales cleanly for production deployments serving many concurrent users or long-context requests.
Manages GPU attention KV cache in discrete, reusable blocks, eliminating the memory fragmentation common in naive implementations. This allows the server to handle more concurrent requests without exhausting GPU memory. The practical effect is higher throughput at the same GPU footprint compared to tools that allocate KV cache statically per request.
Schedules new requests as soon as active generation slots free up, rather than waiting for an entire batch to complete. This keeps GPU utilization high under variable load and cuts time-to-first-token for queued requests. Unlike static batching, continuous batching does not force fast requests to wait for slow ones to finish before the next batch starts.
Loads any supported Hugging Face model from the hub: decoder-only LLMs (Llama, Qwen, Gemma), mixture-of-expert models (Mixtral, DeepSeek-V3), hybrid attention models, multimodal models (LLaVA, Qwen-VL, Pixtral), embedding models, and reward models. Point vLLM at a Hugging Face model ID and it handles architecture detection and weight loading automatically.
Exposes an HTTP endpoint matching OpenAI's Chat Completions and Completions API formats, plus an Anthropic Messages API and gRPC endpoint. Applications using an OpenAI SDK need only a base_url change to route requests to a vLLM instance. This allows teams to move from a commercial inference provider without rewriting client code.
Runs the same code across NVIDIA CUDA, AMD ROCm, Intel Gaudi and XPU, Google Cloud TPUs, Apple Silicon, IBM Spyre, Huawei Ascend NPUs, and x86/ARM/PowerPC CPUs. Each backend activates based on available hardware; no separate codebases or platform-specific rewrites are needed. This makes vLLM viable for on-premises GPU clusters, cloud instances, and developer workstations alike.
Supports FP8, INT8, INT4, GPTQ, AWQ, GGUF, and other quantization formats, reducing the GPU memory footprint for large models. Speculative decoding (EAGLE, n-gram, suffix, DFlash) accelerates generation by predicting multiple draft tokens ahead of the main model, reducing latency for typical token distributions.
Together AI is a managed inference cloud running popular open-weight models behind a paid API. vLLM is an inference engine you run on your own hardware.
| Feature | vLLM | Together AI |
|---|---|---|
| License | Apache-2.0 | Proprietary |
| Hosting | Self-hosted | Managed cloud |
| Pricing | Hardware + electricity | Per-token billing |
| API format | OpenAI-compatible | OpenAI-compatible |
| Model selection | 200+ Hugging Face architectures | Curated hosted models |
vLLM is the better choice for teams with existing GPU capacity, data residency requirements, or workloads where per-token billing becomes expensive at scale. Together AI remains the right call when your team needs managed infrastructure, cannot operate GPU servers, or wants curated model access without configuration overhead.
Fireworks AI is a managed inference API focused on speed and low latency for open-weight models. Like Together AI, it charges per token and handles all infrastructure.
| Feature | vLLM | Fireworks AI |
|---|---|---|
| License | Apache-2.0 | Proprietary |
| Hosting | Self-hosted | Managed cloud |
| Pricing | Hardware costs | Per-token billing |
| Latency tuning | Depends on your hardware | Managed, automatic |
For high-throughput workloads on owned hardware, vLLM typically delivers comparable throughput at lower operating cost once hardware is amortized. Fireworks AI's edge is latency optimization and operational simplicity; teams without GPU infrastructure or dedicated operations capacity should weigh those factors.
Baseten is a model deployment platform that handles serving infrastructure and autoscaling for ML models, including LLMs. Unlike Together AI or Fireworks AI, Baseten supports custom and fine-tuned models, making it broader than a pure inference API. It targets teams needing production-ready model endpoints without managing servers.
vLLM gives teams full control over the serving layer and is more cost-effective at high scale on owned hardware. Baseten suits teams that want managed hosting with autoscaling and faster time-to-production. For deployments where data residency or custom inference configuration matters, vLLM on self-hosted infrastructure is the more appropriate choice.
Install vLLM from PyPI with pip on Python 3.10+.
```bash
pip install vllm
```Yes. vLLM is Apache-2.0 licensed and free to run on your own hardware, including for commercial applications. There are no per-token fees, no seat licenses, and no managed hosting charges when you self-host. Hardware and electricity are the only recurring costs.
vLLM supports NVIDIA CUDA GPUs, AMD ROCm GPUs, Intel Gaudi and XPU accelerators, Google Cloud TPUs, IBM Spyre, Huawei Ascend NPUs, Apple Silicon, and x86/ARM/PowerPC CPUs. NVIDIA CUDA is the most tested and widely used path; other backends work but have varying levels of community support and documentation.
Hugging Face Transformers is designed for model research and single-request inference. vLLM adds PagedAttention for memory management, continuous batching for concurrency, and an HTTP API server, making it suited for serving many concurrent users. For a quick experiment or fine-tuning workflow, Transformers is simpler; for a production server handling concurrent requests, vLLM solves the batching and memory management problems you would otherwise have to address yourself.
For teams with GPU hardware available, vLLM delivers comparable throughput at no per-token cost. The tradeoff is that you manage the server, monitor uptime, and handle model updates yourself. Together AI and Fireworks AI remain better for teams that want fully managed infrastructure or that do not have existing GPU capacity.
Yes. vLLM's HTTP server implements the OpenAI Chat Completions API, so pointing the OpenAI client's base_url at a vLLM instance (for example, http://localhost:8000/v1) requires no changes to request structure or response parsing. An Anthropic Messages API and a gRPC endpoint are also available for applications using those SDKs.
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