BentoML
Privacy-first alternative
Why teams pick it
Keep customer data in-house with privacy-focused tooling.
Open-source alternatives to BentoML
Compare community-driven replacements for BentoML in model serving & inference platforms workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.
BentoML
BentoML packages models into reproducible Bentos and deploys scalable APIs/runners (incl. GPUs) with logging, monitoring, and CI/CD integration.Read more
Key stats
- 15Alternatives
- 14Active development
Recent commits in the last 6 months
- 12Permissive licenses
MIT, Apache, and similar licenses
Counts reflect projects currently indexed as alternatives to BentoML.
Start with these picks
These projects match the most common migration paths for teams replacing BentoML.
LoRAX
Fastest to get started
Why teams pick it
Production‑ready tooling: Docker, Helm, Prometheus, OpenTelemetry, OpenAI‑compatible API
All open-source alternatives
SkyPilot
Run, scale, and manage AI workloads on any cloud
Active developmentPermissive licenseIntegration-friendlyPython
Why teams choose it
- Unified YAML/Python API works across 16+ clouds and Kubernetes
- Automatic cheapest-instance selection with spot support and auto-recovery
- Built-in gang scheduling, multi-cluster scaling, and auto-stop for idle resources
Watch for
Requires familiarity with YAML/Python task definitions
Migration highlight
Finetune Llama 2 on a multi-cloud GPU pool
Trains the model in half the time while cutting cloud spend by 60% using spot instances.
vLLM
Fast, scalable LLM inference and serving for any workload
Active developmentPermissive licenseIntegration-friendlyPython
Why teams choose it
- PagedAttention enables efficient memory use for long contexts
- Continuous batching delivers state‑of‑the‑art serving throughput
- Broad hardware support with GPU, CPU, TPU, and accelerator plugins
Watch for
Best performance requires GPU or accelerator hardware
Migration highlight
High‑concurrency chatbot
Serve thousands of simultaneous chat sessions with low latency using continuous batching and streaming outputs.
SGLang
High‑performance serving framework for LLMs and vision‑language models.
Active developmentPermissive licenseIntegration-friendlyPython
Why teams choose it
- RadixAttention prefix caching and speculative decoding for ultra‑low latency
- Zero‑overhead CPU scheduler with continuous batching and expert parallelism
- Broad hardware support: NVIDIA, AMD, Intel, TPU, Ascend, and more
Watch for
Steep learning curve for advanced parallelism features
Migration highlight
Real‑time conversational AI
Provides sub‑100 ms response times for chatbots handling millions of concurrent users.
LoRAX
Serve thousands of fine-tuned LLM adapters on a single GPU
Permissive licenseFast to deployIntegration-friendlyPython
Why teams choose it
- Dynamic per‑request LoRA adapter loading from HF, Predibase, or filesystem
- Heterogeneous continuous batching keeps latency constant across adapters
- Adapter exchange scheduling offloads weights between GPU and CPU
Watch for
Requires Nvidia Ampere‑generation GPU and Linux environment
Migration highlight
Personalized chatbot deployment
Serve a distinct LoRA adapter per customer to adjust tone or knowledge without restarting the server.
GPUStack
Unified GPU cluster manager for scalable AI inference
Active developmentPermissive licenseFast to deployPython
Why teams choose it
- Broad GPU compatibility across major vendors and OSes
- Multi‑version backend support for diverse model runtimes
- Distributed inference on heterogeneous multi‑node clusters
Watch for
Requires Docker and NVIDIA Container Toolkit for NVIDIA GPUs
Migration highlight
Internal chatbot powered by LLMs
Deploys Qwen3 or LLaMA models behind OpenAI‑compatible APIs for secure, low‑latency employee assistance.
LightLLM
Fast, lightweight Python framework for scalable LLM inference
Active developmentPermissive licenseIntegration-friendlyPython
Why teams choose it
- Pure‑Python design with token‑level KC cache for research flexibility
- Integration of high‑performance kernels (FasterTransformer, FlashAttention, vLLM) for fast inference
- Scalable serving on single GPU or multi‑node clusters via easy configuration
Watch for
Primarily optimized for NVIDIA GPUs; limited CPU performance
Migration highlight
Real‑time chat assistant
Delivers sub‑50 ms response latency for LLM‑driven conversational agents on a single H200 GPU.
Triton Inference Server
Unified AI model serving across clouds, edge, and GPUs
Active developmentPermissive licenseIntegration-friendlyPython
Why teams choose it
- Supports multiple deep learning and machine learning frameworks
- Dynamic batching, sequence handling, and model ensembling
- Extensible backend API with Python‑based custom backends
Watch for
Best performance achieved on NVIDIA hardware; CPU fallback may be slower
Migration highlight
Real‑time video analytics
Process live video streams with sub‑second latency using GPU‑accelerated models.
Ray
Scale Python and AI workloads from laptop to cluster effortlessly
Active developmentPermissive licenseAI-powered workflowsPython
Why teams choose it
- Unified core runtime with task, actor, and object abstractions
- Scalable AI libraries for data, training, tuning, RL, and serving
- Flexible deployment on laptops, clusters, cloud, or Kubernetes
Watch for
Steeper learning curve for distributed concepts
Migration highlight
Distributed Hyperparameter Tuning
Find optimal model parameters across hundreds of CPUs in minutes using Ray Tune.
NanoFlow
High‑throughput LLM serving with intra‑device parallelism and asynchronous CPU scheduling
Active developmentFast to deployIntegration-friendlyJupyter Notebook
Why teams choose it
- Intra‑device parallelism with nano‑batching and execution unit scheduling
- Asynchronous CPU scheduling for KV‑cache management and batch formation
- Integration with CUTLASS, FlashInfer, and MSCCL++ kernel libraries
Watch for
Best performance observed on high‑end NVIDIA GPUs (e.g., A100)
Migration highlight
High‑volume chat service
Sustains higher request rates with low per‑token latency for thousands of concurrent users
BentoML
Unified Python framework for building high‑performance AI inference APIs
Active developmentPermissive licensePrivacy-firstPython
Why teams choose it
- Turn any model into a REST API with minimal Python code
- Automatic Docker image generation and reproducible Bento artifacts
- Built‑in performance optimizations: dynamic batching, model parallelism, multi‑model pipelines
Watch for
Requires Python ≥ 3.9, limiting non‑Python environments
Migration highlight
Summarization Service
Generate concise summaries for documents via a simple REST endpoint
FEDML
Unified ML library for scalable training, serving, and federated learning.
Active developmentPermissive licenseIntegration-friendlyPython
Why teams choose it
- Cross‑cloud scheduler automatically matches jobs with the most cost‑effective GPU resources.
- Unified API covers distributed training, model serving, and federated learning in a single codebase.
- Supports on‑prem, hybrid, and multi‑cloud clusters, including edge and smartphone devices.
Watch for
Steep learning curve for advanced distributed configurations.
Migration highlight
Large‑scale LLM fine‑tuning on multi‑cloud GPUs
Accelerated training time and reduced cost by auto‑selecting the cheapest GPU instances across clouds.
TensorRT LLM
Accelerated LLM inference with NVIDIA TensorRT optimizations
Active developmentFast to deployIntegration-friendlyPython
Why teams choose it
- Expert parallelism for multi‑GPU scaling
- Speculative and guided decoding to triple token throughput
- KV‑cache reuse and multiblock attention for long sequences
Watch for
Requires NVIDIA GPU hardware
Migration highlight
High‑throughput chatbot service
Delivers >40,000 tokens/s per GPU, handling millions of user queries daily with sub‑10 ms latency.
Seldon Core 2
Deploy modular, data-centric AI applications at scale on Kubernetes
Active developmentIntegration-friendlyAI-powered workflowsGo
Why teams choose it
- Composable pipelines with Kafka‑based real‑time streaming
- Native and custom autoscaling for models and components
- Multi‑model serving to consolidate inference workloads
Watch for
Requires operational expertise with Kubernetes
Migration highlight
Real‑time fraud detection pipeline
Stream transaction data through Kafka‑linked models to flag anomalies instantly while auto‑scaling under load.
KServe
Unified AI inference platform for generative and predictive workloads on Kubernetes
Active developmentPermissive licenseIntegration-friendlyGo
Why teams choose it
- LLM‑optimized inference with OpenAI‑compatible API
- GPU‑accelerated serving with KV‑cache offloading
- Multi‑framework support and intelligent routing
Watch for
Complexity may increase for small‑scale deployments
Migration highlight
Real‑time LLM chat service
Delivers low‑latency responses with GPU acceleration, KV‑cache offloading, and autoscaling to handle variable traffic.
OpenLLM
Run any LLM locally behind an OpenAI-compatible API
Active developmentPermissive licensePrivacy-firstPython
Why teams choose it
- OpenAI‑compatible API for any supported open‑source LLM
- Single‑command server launch with built‑in chat UI
- Extensive model catalog (Llama, Qwen, Phi, Mistral, etc.)
Watch for
Requires compatible GPU hardware for larger models
Migration highlight
Chatbot prototype
Launch a functional chat API in minutes for internal testing or demos.
Choosing a model serving & inference platforms alternative
Teams replacing BentoML in model serving & inference platforms workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.
- 14 options are actively maintained with recent commits.
Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from BentoML.