Run LLMs
On Your Terms

Private LLM deployments with flexible distributed architecture and tailored inference optimization. Up to 6x lower cost than managed APIs.

Trusted by visionary AI teams worldwide

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Dedicated Deployments
For your mission-critical

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Price-Performance at Scale

  • Lower LLM cost by 6x versus managed APIs on real-world benchmarks with fine-grained performance controls and customizable serving architecture.
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Blazing Fast Autoscaling

  • Spin up inference capacity in seconds across clouds, regions, or data centers to maximize GPU utilization, ensure high availability, and optimize costs.
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Unified Inference Management

  • Deploy, scale, and manage any model—LLMs, embeddings, multi-modal pipelines, or custom architectures with a single, unified platform.
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Private & Compliant by design

  • Private model deployments in your VPC or air-gapped on-prem cluster, built for enterprise-grade security. No data ever leaves your environment.

InferenceOps, Your AI Backbone

Power your teams to ship and maintain AI inference just like they do software—fully automated, reliably scalable, and always secure.

Choose any cloud or on-prem GPU vendor with zero lock-in

Fast autoscaling from 0 to 100s of GPUs on demand

Built-in observability & cost analytics for full visibility

Simplify deployments and eliminate ops overhead

From Models, To AI Systems

Build scalable AI systems with any model, using our Open-Source AI serving framework.

01. Build Inference APIs in Minutes

Instant local testing, interactive debugging, and composable pipelines make it easy to go from dev to prod in minutes, not days.

Llama
RAG
Function Calling
LLM Structured Outputs
ControlNet
@openai_endpoints( model_id=MODEL_ID, default_chat_completion_parameters=dict(stop=["<|eot_id|>"]), ) @bentoml.service( name="bentovllm-llama3.1-405b-instruct-awq-service", traffic={ "timeout": 1200, "concurrency": 256, # Matches the default max_num_seqs in the VLLM engine }, resources={ "gpu": 4, "gpu_type": "nvidia-a100-80gb", }, ) class VLLM: def __init__(self) -> None: from transformers import AutoTokenizer from vllm import AsyncEngineArgs, AsyncLLMEngine ENGINE_ARGS = AsyncEngineArgs( model=MODEL_ID, max_model_len=MAX_TOKENS, enable_prefix_caching=True, tensor_parallel_size=4, ) self.engine = AsyncLLMEngine.from_engine_args(ENGINE_ARGS) tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) self.stop_token_ids = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>"), ] @bentoml.api async def generate( self, prompt: str = "Explain superconductors in plain English", system_prompt: Optional[str] = SYSTEM_PROMPT, max_tokens: Annotated[int, Ge(128), Le(MAX_TOKENS)] = MAX_TOKENS, ) -> AsyncGenerator[str, None]: from vllm import SamplingParams SAMPLING_PARAM = SamplingParams( max_tokens=max_tokens, stop_token_ids=self.stop_token_ids, ) if system_prompt is None: system_prompt = SYSTEM_PROMPT prompt = PROMPT_TEMPLATE.format(user_prompt=prompt, system_prompt=system_prompt) stream = await self.engine.add_request(uuid.uuid4().hex, prompt, SAMPLING_PARAM) cursor = 0 async for request_output in stream: text = request_output.outputs[0].text yield text[cursor:] cursor = len(text)

02. Scale with Confidence

Burst from 1 to 100s of GPUs across regions with Bento’s adaptive autoscaling and ultra-fast cold starts—never over‑provision or miss a traffic spike.

bentoml deploy . 🍱 Built bento "vllm:7ftwkpztah74bdwk" ✅ Pushed Bento "vllm:7ftwkpztah74bdwk" ✅ Created deployment "vllm:7ftwkpztah74bdwk" in cluster "gcp-us-central-1" 💻 View Dashboard: https://ss-org-1.cloud.bentoml.com/deployments/vllm-t1y6

03. Ship Secure AI Endpoints

Auto‑generated REST API, Python Client and OpenAI‑compatible endpoints with built‑in auth, SLA monitoring, and cost analytics.

curl
python
curl -s -X POST \ 'https://bentovllm-llama3-1-405b-instruct-awq-service.mt-guc1.bentoml.ai/generate' \ -H 'Content-Type: application/json' \ -d '{ "max_tokens": 4096, "prompt": "Explain superconductors in plain English", "system_prompt": "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don'"'"'t know the answer to a question, please don'"'"'t share false information." }'

Own Your AI Sovereignty

Self-host the Bento Inference platform in your VPC or on-prem—retain full control over your AI stack with flexible choice of models, clouds, and accelerators.

Deploy anywhere: AWS, Azure, GCP, or On-premise

SOC 2 Type II report, ISO 27001 / HIPAA control mappings

Keep IAM, KMS, and audit logs under your control

No data ever leaves your environment

What our customers say

“BentoML enables our Data Science and Engineering teams to work independently, without the need for constant coordination. This allows us to build and deploy AI services with incredible efficiency while giving the ML Engineering team the flexibility to refactor when needed. What used to take days, now takes just hours. In the first four months alone, we deployed over 40 models, and now run over 150 in production, thanks to BentoML's standardized platform.”

Michael Misiewicz, Director of Data Science, Yext

"BentoML’s infrastructure gave us the platform we needed to launch our initial product and scale it without hiring any infrastructure engineers. As we grew, features like scale-to-zero and BYOC have saved us a considerable amount of money."

Patric Fulop, CTO, Neurolabs

“BentoML is helping us future-proof our machine learning deployment infrastructure at Mission Lane. It is enabling us to rapidly develop and test our model scoring services , and to seamlessly deploy them into our dev, staging, and production Kubernetes clusters.”

Mike Kuhlen, Data Science & Machine Learning Solutions and Strategy, Mission Lane

"BentoML is an excellent tool for saving resources and running ML at scale in production"

Woongkyu Lee, Data and ML Engineer, LINE

"Bento have given us the tools and confidence to build our own Voice AI Agent solution. We are really excited to be working with Bento. They have made our development path to production much easier."

Mark Brooker, CEO, MBit