FastAgentic vs BentoML for agents
BentoML is the gold standard for shipping classic ML models. But agents aren't models — they're stateful, multi-step, streaming, and protocol-diverse. Here's where the classic ML-serving toolchain stops fitting.
BentoML is great. We’ve shipped classic ML models on it. It’s batteries-included, GPU-aware, has a clean packaging story, and deploys everywhere. But BentoML was designed for models: stateless, request/response, batch-friendly inference. Agents are a different shape, and the impedance mismatch gets painful quickly.
Where BentoML shines
- Packaging: bentos are self-contained artifacts with pinned dependencies.
- GPU inference: optimized runners, batch inference, adaptive batching.
- Model versioning: Yatai model store and registry.
- Framework agnostic for models: PyTorch, TensorFlow, sklearn, ONNX, Transformers.
- Deployment story: Kubernetes, bare metal, cloud runtimes.
If you’re shipping a recommendation model, a classifier, an embedding service, or a diffusion model, BentoML is a great choice.
Where BentoML falls short for agents
Streaming and intermediate state
Agents produce intermediate events — thoughts, tool calls, partial tokens — that clients want to see as they happen. BentoML’s request/response model can stream, but it was designed for batchable inference and nothing about the tooling treats intermediate events as first-class.
Durable resumption
A 40-step LangGraph pipeline that crashes at step 37 should resume from step 38, not rerun from the top. BentoML has no opinion about step-level state. FastAgentic’s StepTracker is designed for this.
MCP and A2A
Model Context Protocol and Agent-to-Agent are protocol surfaces that agents need to advertise to other clients. BentoML has no MCP tool surface. FastAgentic generates MCP tools from the same decorator that generates REST routes.
Cost governance
Token costs per run, per user, per tenant, with hard budget cut-offs — this is a daily concern for agent platforms, not a classical inference concern. BentoML doesn’t model it. FastAgentic treats it as a first-class primitive.
Tool calling
Agents call tools. Those tool calls need to be schema-checked, traced, cost-attributed, and possibly persisted. BentoML’s model is “pass inputs, get outputs” — tool loops sit awkwardly on top.
Authoring ergonomics
BentoML services wrap models. FastAgentic services wrap agents. When your author-time object is an Agent with system prompts, tools, memory, and output types, the service layer needs to understand that shape.
Feature comparison
| Concern | BentoML | FastAgentic |
|---|---|---|
| Classic ML inference | ✅ first-class | ⚠️ possible, not the focus |
| GPU-aware batching | ✅ first-class | ❌ (use BentoML underneath if you need it) |
| Model versioning / registry | ✅ Yatai | ❌ (bring your own) |
| Agent authoring adapters | ❌ | ✅ (PydanticAI, LangGraph, CrewAI, LangChain) |
| MCP protocol | ❌ | ✅ native |
| A2A protocol | ❌ | ✅ native |
| Streaming intermediate events | ⚠️ generic | ✅ first-class |
| Durable step resumption | ❌ | ✅ StepTracker |
| Per-tenant cost caps | ❌ | ✅ |
| Audit trail of tool calls | ❌ | ✅ |
You can absolutely use both
The honest answer is: if you have classic ML models feeding your agents, BentoML is still the right tool for the models, and FastAgentic is the right tool for the agents around them. They compose.
A common pattern we see:
[BentoML service: embedding model]
↑
│ HTTP
│
[FastAgentic service: research agent]
↑
│ MCP / REST
│
[Claude / Cursor / other agents]FastAgentic doesn’t replace your model-serving stack. It replaces the hand-rolled boilerplate between your agents and your users.
Need FastAPI, LangGraph, or agent platform expertise?
Neul Labs — the team behind FastAgentic — takes on a limited number of consulting engagements each quarter. We help teams ship agents to production, fix broken LangGraph pipelines, and design governance for multi-tenant LLM platforms.