Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
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
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
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