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Average Ratings 0 Ratings

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features
design
support

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Description

Ambient Mesh is a modern service mesh architecture designed to eliminate the complexity of traditional sidecar-based approaches. It secures, observes, and connects cloud-native workloads with minimal intrusion and resource consumption. Ambient Mesh delivers zero-trust security using workload identity, encryption, and automated certificate management. Teams gain deep visibility into traffic flows through distributed tracing, logs, and performance metrics. Advanced traffic control features support safe deployments, intelligent routing, and seamless failover. The platform improves resilience with circuit breaking, zone-aware load balancing, and retry policies. Ambient Mesh enables organizations to migrate existing sidecar workloads with zero downtime. A free migration tool provides automated analysis and step-by-step guidance. This approach reduces operational risk while maintaining compliance and control. Ambient Mesh simplifies service mesh adoption while lowering infrastructure costs.

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.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Kubernetes Yes 
Amazon EKS Yes 
Amazon Web Services (AWS) Yes 
Azure Kubernetes Service (AKS) Yes 
Bloomberg No 
Cilium Yes 
Docker No 
Envoy Yes 
Gojek No 
Google Cloud Platform Yes 
GraphQL Yes 
IBM Cloud No 
Istio Yes 
Microsoft Azure Yes 
NAVER No 
Red Hat OpenShift Yes 
Splunk APM Yes 
agentgateway Yes 

Integrations

Kubernetes Yes 
Amazon EKS No 
Amazon Web Services (AWS) No 
Azure Kubernetes Service (AKS) No 
Bloomberg Yes 
Cilium No 
Docker Yes 
Envoy No 
Gojek Yes 
Google Cloud Platform No 
GraphQL No 
IBM Cloud Yes 
Istio No 
Microsoft Azure No 
NAVER Yes 
Red Hat OpenShift No 
Splunk APM No 
agentgateway No 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

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

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

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

Types of Training

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

Types of Training

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

Vendor Details

Company Name

Ambient Mesh

Founded

2017

Country

United States

Website

ambientmesh.io

Vendor Details

Company Name

KServe

Website

kserve.github.io/website/latest/

Product Features

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 

Alternatives

Alternatives