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features
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support

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Description

KServe is a robust model inference platform on Kubernetes that emphasizes high scalability and adherence to standards, making it ideal for trusted AI applications. This platform is tailored for scenarios requiring significant scalability and delivers a consistent and efficient inference protocol compatible with various machine learning frameworks. It supports contemporary serverless inference workloads, equipped with autoscaling features that can even scale to zero when utilizing GPU resources. Through the innovative ModelMesh architecture, KServe ensures exceptional scalability, optimized density packing, and smart routing capabilities. Moreover, it offers straightforward and modular deployment options for machine learning in production, encompassing prediction, pre/post-processing, monitoring, and explainability. Advanced deployment strategies, including canary rollouts, experimentation, ensembles, and transformers, can also be implemented. ModelMesh plays a crucial role by dynamically managing the loading and unloading of AI models in memory, achieving a balance between user responsiveness and the computational demands placed on resources. This flexibility allows organizations to adapt their ML serving strategies to meet changing needs efficiently.

Description

Outline your cloud-native infrastructure and manage it as a systematic approach. Create a configuration for your service mesh alongside the deployment of workloads. Implement smart canary strategies and performance profiles while managing the service mesh pattern. Evaluate your service mesh setup based on deployment and operational best practices utilizing Meshery's configuration validator. Check the compliance of your service mesh with the Service Mesh Interface (SMI) standards. Enable dynamic loading and management of custom WebAssembly filters within Envoy-based service meshes. Service mesh adapters are responsible for provisioning, configuration, and management of their associated service meshes. By adhering to these guidelines, you can ensure a robust and efficient service mesh architecture.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Docker Yes 
Kubernetes Yes 
Bloomberg Yes 
Cilium No 
F5 NGINX Gateway Fabric No 
Gojek Yes 
Google Kubernetes Engine (GKE) No 
HashiCorp Consul No 
Helm No 
IBM Cloud Yes 
Istio No 
Kubeflow Yes 
Kuma No 
Linkerd No 
NGINX No 
Network Service Mesh No 
ZenML Yes 
Zillow Yes 
vLLM Yes 

Integrations

Docker Yes 
Kubernetes Yes 
Bloomberg No 
Cilium Yes 
F5 NGINX Gateway Fabric Yes 
Gojek No 
Google Kubernetes Engine (GKE) Yes 
HashiCorp Consul Yes 
Helm Yes 
IBM Cloud No 
Istio Yes 
Kubeflow No 
Kuma Yes 
Linkerd Yes 
NGINX Yes 
Network Service Mesh Yes 
ZenML No 
Zillow No 
vLLM No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

KServe

Website

kserve.github.io/website/latest/

Vendor Details

Company Name

Meshery

Website

meshery.io

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

Alternatives

Alternatives