KServe, Seldon and BentoML are all capable model serving platforms, and the right one depends on how deeply a team is already invested in Kubernetes versus wanting a simpler, more portable packaging format. KServe is a Kubernetes-native serving layer built on Knative, offering standardized inference protocols, autoscaling including scale-to-zero, and native support for canary rollouts, making it a strong fit for a team that already runs Kubernetes at scale and wants serving to integrate with existing cluster tooling and GPU scheduling. Seldon Core offers similar Kubernetes-native serving with a strong focus on advanced deployment patterns like multi-armed bandits and explainability integrations, and its enterprise product adds governance features, though its licensing model has shifted in ways worth checking directly before committing. BentoML takes a different approach, focusing on packaging models into portable, framework-agnostic containers with a simple Python API, which makes it easier to get started without deep Kubernetes expertise and to deploy the same package to a VM, Kubernetes or a serverless backend. A team running large-scale GPU inference with vLLM or TensorRT-LLM backends typically leans toward KServe for its tighter Kubernetes and autoscaling integration. Nanobase AI, a Silicon Valley enterprise AI engineering company, has deployed all three and picks based on a client's existing infrastructure maturity rather than a fixed recommendation.
These tools sit above the inference engine, not in place of it
A common confusion is treating KServe, Seldon and BentoML as competitors to vLLM, TensorRT-LLM or Triton. They are not. KServe, Seldon and BentoML orchestrate how a model gets deployed, scaled and routed to; vLLM or TensorRT-LLM is typically what actually runs inside the container these platforms manage for LLM inference specifically. Choosing between them is a decision about deployment ergonomics, autoscaling behavior and rollout features, not about raw inference throughput, which is determined by the runtime underneath.
Comparing on the criteria that actually differ
| Criterion | KServe | Seldon Core | BentoML |
|---|---|---|---|
| Kubernetes dependency | Required, built on Knative | Required | Optional; also runs on VMs or serverless |
| Scale-to-zero | Native | Supported | Depends on target backend |
| Canary/traffic-split rollout | Native, built in | Native, with advanced patterns (bandits) | Requires external routing layer |
| Multi-framework packaging | Standardized inference protocol | Standardized inference protocol | Python-first, framework-agnostic |
| Learning curve for a non-Kubernetes-heavy team | Steep | Steep | Gentler |
| Enterprise governance features | Community-driven | Stronger in commercial tier | Growing, via BentoCloud |
A team already running Kubernetes at scale with GPU scheduling in place tends to get more value from KServe's tight integration with cluster-native autoscaling and canary tooling. A team that wants to package a model once and deploy it across a VM, a Kubernetes cluster or a serverless backend without three different configurations tends to prefer BentoML's portability. Seldon fits between the two, offering Kubernetes-native serving with more advanced rollout patterns like multi-armed bandit routing, though its licensing terms for enterprise features have shifted and are worth confirming directly before committing.
Lock-in is a real cost, even for open-source tools
Migrating a serving platform after production traffic depends on it is expensive regardless of license, since every application team's deployment manifests, monitoring dashboards and rollout scripts end up coupled to the chosen platform's specific abstractions. Evaluating a serving platform should include a rough estimate of what a future migration would cost, not just how easy the initial adoption is, since teams that skip this step tend to discover the coupling only when a licensing change or a scaling limitation forces a switch.
A decision checklist
- Is the team already deep in Kubernetes, with GPU scheduling and cluster tooling in place? If yes, KServe or Seldon fit naturally; if not, BentoML's gentler onboarding matters more.
- Does the rollout need native canary or bandit-style traffic splitting, or is a simpler binary switch acceptable? Native support saves building custom routing logic.
- Will models need to run outside Kubernetes at any point, on a VM, at the edge, or in a serverless function? BentoML's portability becomes valuable here.
- What inference runtime sits underneath for the heaviest workloads? Confirm vLLM, TensorRT-LLM or another engine is well supported as a backend before locking in the orchestration layer.
Frequently asked questions
Can we use these platforms for both LLMs and classical ML models?
Yes, all three support arbitrary model backends. For LLMs specifically, the serving platform typically wraps a dedicated inference engine like vLLM or TensorRT-LLM rather than running the model directly, since those engines handle the batching and KV-cache management general-purpose serving layers do not.
Does BentoML support GPU inference?
Yes, BentoML supports GPU-backed inference and can package models that call vLLM or other GPU runtimes internally; it is not limited to CPU-only workloads.
Is Seldon's open-source version still viable for production?
The open-source core remains usable, but licensing for certain enterprise features has changed over time, so it is worth reviewing Seldon's current licensing terms directly against the specific features a deployment needs before adopting it as the long-term serving layer.
How Nanobase AI helps
Nanobase AI has deployed KServe, Seldon and BentoML in production and selects based on a client's existing Kubernetes and GPU infrastructure maturity rather than a default recommendation, pairing the orchestration layer with the right inference runtime underneath. A demo environment can show either approach running against real traffic patterns.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.