AIGR.ID vs Ray/RayServe/Anyscale Ecosystem Comparison

Based on the comparison points, AIGr.id appears to offer several capabilities that are either missing or limited in the Ray/Anyscale/RayServe ecosystem:

Decentralized, Polycentric Network & Governance:
AIGr.id is designed as a global public infrastructure for AI, not owned or controlled by any single entity. It is contributed to and accessed as a digital commons, powered by the 100% open-source and community-driven OpenOS.AI. This contrasts with Ray/Anyscale, where Ray is open source but the platform is managed by Anyscale, especially on public clouds. AIGr.id supports building and coordinating multiple cognitive architectures and composing modular, networked AI systems.
Multi-Cluster Workflow Spanning:
AIGr.id supports AI workflows (vDAGs) where interconnected AI components can span multiple clusters. In contrast, Ray workflows cannot span multiple clusters because Ray clusters themselves are restricted to single Kubernetes clusters.
Deep Programmability via Turing Complete Policies:
AIGr.id enables deep customization through programmable Python-based policies that are Turing complete. These can execute locally as functions, graphs, or jobs and provide control over scheduling, resource allocation, load balancing, auditing, quota management, and AI block/workflow node assignment. Ray/Anyscale supports limited rule definitions via IAM and RBAC, with only partial support for policy-like customization in specific modules like the autoscaler.
Built-in Decentralized Registries:
AIGr.id includes decentralized registries for assets, container images, AI components, and specifications, which are globally discoverable and reusable across the network. These platform-native registries are not present in Ray/Anyscale.
Persistent Database Storage and Management:
AIGr.id includes FrameDB for in-memory and persistent object storage, with TiDB integration and S3-like backup/restore capabilities. Ray supports in-memory sharing via Plasma Store but lacks built-in support for persistent storage and backup management.
Advanced Workflow Composition Features:
AIGr.id supports nested workflows, allowing one vDAG to reference another, and enables sharing of AI blocks across multiple workflows. Ray/Anyscale does not support such nested or shared workflow features.
Specific Customization Points in AI Block Functionality:
AIGr.id provides several block-level customization features, such as:
Implementing custom management commands using the AIOS instance SDK.
Adding sidecar containers as utility components in AI block pods.
Writing fully customizable batching logic beyond fixed parameters.
Native Stream Data and Video Inference Support:
AIGr.id includes native support for stream data ingestion and video/live camera inference. These features are not built-in to the Ray/Anyscale ecosystem and must be manually developed.
Automating Third-Party Service Deployment:
AIGr.id enables automation of third-party service deployment using init containers during AI block creation. This functionality is not available in Ray/Anyscale.
Custom Model Splitting Across Clusters:
AIGr.id supports custom model splitting and distributed inference across clusters. Ray supports multi-node LLM deployment through integrations like vLLM, but lacks native support for custom model splitting across cluster boundaries.

In summary, AIGr.id differentiates itself by focusing on a decentralized architecture, extensive policy-driven programmability for fine-grained control across a multi-cluster network, built-in features for persistent data management and sharing assets, and specific customization points within AI workflows and blocks, as well as native stream/video data support. Ray/Anyscale, conversely, is presented more as a unified framework for scaling traditional ML and Python workloads, with robust MLOps features, dynamic autoscaling leveraging cloud APIs, and fault tolerance for centralized or cloud-based deployments.

Detailed AIGR.ID vs Ray/Anyscale/RayServe Comparision table

1. Platform Architecture and Foundation

This category covers the core structure, underlying principles, network topology, infrastructure requirements, built-in registries, and fundamental data management aspects of the platforms.

Sl no	Comparison	AIGR.ID	AnyScale/RayServe/Ray ecosystem
1	Definition	AIGr.id is a decentralized network of interconnected AI components that coordinate to share data, perform tasks, and compose into higher-level collective intelligence.	Ray is an open-source unified framework for scaling AI and Python applications like machine learning. It provides the compute layer for parallel processing. Anyscale is a platform built on top of Ray to manage deployments on a Ray cluster.
2	Multi-cluster support	Yes. Multiple federated clusters can be part of the AIGr.id network, managed by a management cluster. Clusters can be deployed on heterogeneous clouds, data-centers or homegrown clusters.	Yes, Multiple clouds can be part of the Anyscale configuration. Anyscale schedules Ray workflows on these clusters based on resource availability. Ray cluster, however, cannot spawn across multiple Kubernetes clusters.
3	Can run without kubernetes?	No	Yes, the components of the ecosystem like RayServe and Ray can run without kubernetes.
4	Built-in managed VPC for nodes federation	No, depends on custom VPC, VPN or firewall settings. Allows clusters to use Tailscale, WireGuard or any VPN service under the hood.	Yes, provides a built-in VPC which uses Tailscale under the hood.
5	Persistent Storage Options available	Object storage - ceph (using assets registry APIs), remote. Local file-system volume of the node, FrameDB persistent storage.	Object storage - remote only, Local file system volume of the node, Shared network storage using NFS (network file-system).
6	Built-in registries to store assets, container images, components and specifications for re-use	Yes: Assets registry (files, code, models), Container registry (internal + external), Components registry (AI instance images), Spec store (vDAGs, blocks specs).	No.
7	Built-in Cross language programming	No. User can interact with other languages by packaging them and handling conversions/calling conventions explicitly.	Partial. Java to Python and Python to Java cross programming is supported.
8	In-memory shared database support for storing objects locally and globally	Yes, FrameDB.	Yes, Plasma Store.
9	Persistent database storage support for storing objects in a persistent storage volume locally and globally	Yes, TiDB integration with FrameDB.	No.
10	Backup and restore of in-memory/persistent objects to S3 like object storage	Yes.	No.
11	Sharing of objects across multiple nodes and creation of local copies	Yes.	Yes.
12	In-memory/Persistent object store serialization format	Flexible. Serialization/deserialization handled by application; stores raw bytes.	Apache Arrow serialization and deserialization format.
13	Reference counting and garbage collection of objects with zero reference count	Yes.	Yes.
14	Recovery of lost objects using Lineage reconstruction	No.	Yes.
15	Core communication data format	Protobuf and flexible serialization/deserialization formats using in-memory FrameDB.	Plasma Object (PyArrow format).

2. Resource Management and System Orchestration

This category focuses on how compute resources are allocated, scheduled, and managed, including policy controls, scaling, load balancing, and handling accelerators.

Sl no	Comparison	AIGR.ID	AnyScale/RayServe/Ray ecosystem
1	Nodes federation / Machine pooling support	Yes, nodes can be added to the existing cluster.	Yes, nodes can be added to the existing cluster (Known as customer managed machine pool).
2	Flexible network/cluster governance using programmable policies	Yes, Custom python policies can be deployed to govern addition/removal of clusters, scheduling workloads, executing management clusters at both management cluster and individual worker cluster levels.	No, Limited set of rule definitions are supported based on the IAM and RBAC rules provided by the cloud vendors.
3	Programmable turing complete policies and built-in policies execution system	Yes, AIGR.ID is built with customizability in mind, thus programmable policies are supported across multiple functionalities using a Turing complete python programming language. Provides a built-in system to execute these policies locally within modules or deployed as functions/graphs/jobs.	No. Autoscaler of Ray/Anyscale provides customizable policy-like interface using python but no extensive support for customizability across different functionalities.
4	Supports scaling of individual AI blocks that are part of the workflow	Yes.	Yes.
5	Support for manual scaling of AI blocks	Yes.	Yes.
6	Support for specifying min and max replicas per AI block	Yes.	Yes.
7	Support for autoscaling based on metrics	Yes.	Yes.
8	Autoscaling using programmable policy for flexible decision making	Yes, Autoscaler is completely programmable using the policies system.	Yes, Autoscaler is completely programmable using python and ray library.
9	Support for NVIDIA GPU Accelerators for AI block scheduling	Yes. GPU based metrics collection and scheduling is supported by default.	Yes.
10	Support for Google TPUs, Intel Gaudi, Huawei Ascend for AI block scheduling	No. But there are plans to support these in the future.	Yes. Supported using community contributions.
11	Framework for porting custom accelerators	No.	Yes.
12	Framework for adding custom accelerators for resource allocation	Yes.	Yes.
13	Horizontal Cluster scaling - adding more nodes to the cluster on the fly based on the demand	No. Clusters must be pre-configured. Scaling happens within available resources. New nodes can be added manually.	Yes. Anyscale/Ray can tap into cloud vendor's infrastructure APIs to autoscale by adding more nodes.
14	Customizable AI scheduling (allocation) using programmable policies	Yes. Resource allocation for AI blocks can be customized using a python policy.	No. Provides fixed resource allocation strategies.
15	Concept of Placement groups, i.e bundling of resources and assigning them to tasks readily	No.	Yes. Useful for gang scheduling in deep learning training but also for inference serving.
16	Customizable and programmable load balancing between the replicas of the AI block	Yes. Load balancer logic can be implemented using custom python policy.	No.
17	AI blocks replica health checking	Yes. Periodic health checking of all replicas.	Yes. Periodic health checking of all replicas.
18	Customizable and programmable health anomaly detection	Yes. Programmable python policy can be used to ingest health check data and detect anomaly.	No.
19	Support for deploying the AI block on multiple GPUs	Yes. If supported by the inference framework.	Yes. If supported by the inference framework.
20	Support for deploying multiple AI blocks on same GPU (GPU sharing)	Yes.	Yes.

3. AI/ML Workload Development and Execution

This category focuses on features specifically for building, defining, deploying, and running AI/ML models and workflows, including SDKs, workflow composition, model serving, training, and specialized AI capabilities.

Sl no	Comparison	AIGR.ID	AnyScale/RayServe/Ray ecosystem
1	Support for multi-cluster AI workflows	Yes. The interconnected AI components that form a workflow can spawn across multiple clusters.	No. The interconnected AI components that are part of the ray workflow cannot spawn across multiple clusters as Ray clusters.
2	SDKs to build and deploy AI instances	Yes.	Yes.
3	Base docker images to build the docker images of AI instances	Yes.	Yes.
4	Support for composable AI as workflows (Model composition/vDAGs)	Yes.	Yes.
5	Composable AI specification type	JSON with template based parsing.	Python code using ray library.
6	Support for conditional routing within the workflow	Yes.	Yes.
7	Support for nested workflows - reference an already deployed workflow in the current workflow/vDAG	Yes. Already existing vDAGs can be referenced within the current vDAG by specifying the vDAG URI.	No.
8	Sharing of the AI blocks across multiple workflows	Yes. A block can be shared across multiple workflows by assigning the node of the workflow to it.	No. Sharing the same block (or component) of the workflow is not supported.
9	Built-in model training infrastructure	No.	Yes.
10	Support for side-cars as utility applications connected to the main AI component	Yes. Side-cars can be spinned up as a custom pod connected to the main AI block for extending its functionality.	No.
11	Customizable batching logic	Yes. Developers can write custom batching logic using AIOS instance SDK.	No. Fixed batching parameters can be provided for batching function.
12	AI block selection for inference task submission using a programmable selection logic	Yes. Inference task submission can contain a search query that can be used to select a right AI block for AI inference.	No.
13	Assignment of Workflow DAG nodes on existing blocks using programmable assignment logic	Yes. vDAG spec can contain a programmable selection/assignment python policy for each node, evaluated to select a block.	No.
14	Model Multiplexing	No. But can be achieved by specifying the AI block selection query when submitting the inference task.	Yes. Built-in selection for model multiplexing.
15	Connecting external / third party servers to the AI blocks	Yes.	Yes. The block can contain python functions that can interact with third party external services.
16	Automating the deployment of third party services on the cluster using init containers at the time of AI block creation	Yes.	No.
17	Support for streaming inference	Yes. Data can be supplied as streams.	Yes. Data can be supplied as streams.
18	Support for batch inference	Yes. Data-sets can be stored in in-memory or persistent local databases of Frame-DB for batch inference.	Yes. Data can be stored in the plasma store for batch inference.
19	Out of band communication support using NCCL	Yes, but very limited alpha support.	Yes.
20	Custom communication protocol between blocks of the workflow (Out of band communication)	No.	Yes.
21	Custom pre and post-processor for each node in the AI workflow	Yes.	Yes.
22	Support for multiple inference frameworks and libraries	Yes. Libraries can be imported, used, and packaged with the block.	Yes.
23	Support for deploying and serving LLM models	Yes.	Yes.
24	Support for Composing of AI workflows constituting LLM and non-LLM models	Yes.	Yes.
25	OpenAI compatible API for LLM serving	No. But will be added in the future.	Yes.
26	Multi-node LLM deployment with built-in splitting of LLM models and distribution	No built-in support. Can be deployed using third party vLLM cluster with init container automation.	Yes. Using built-in vLLM integration.
27	Support for custom model splitting and distributed inference across clusters	Yes. But very limited set of model architectures support splitting.	No.
28	Engine agnostic architecture for LLM inference	Yes. Any LLM serving library can be embedded or third party server linked, automated with init containers.	Yes.
29	Multi-LoRA support with shared base models	No.	Yes.
30	Fast model loading with safe tensors and local machine cache	No. But will be added in the future.	Yes.
31	Built-in Ingestion support for stream data	Yes.	No.
32	Video/Live camera inference support	Yes.	No. Can be built in application layer, but no library support exists.
33	Supports non AI workflowns and non-AI computation as blocks	Yes.	Yes.

4. Operational Aspects and Developer Experience

This category includes features related to monitoring, logging, debugging, user interfaces, APIs, configuration management, and general usability and support for developers and administrators.

Sl no	Comparison	AIGR.ID	AnyScale/RayServe/Ray ecosystem
1	Built-in secret management for credentials, API keys storage	No. Secret management is in the roadmap.	Yes. Anyscale can tap into the secret management stores provided by cloud vendors.
2	Built-in integration with CI/CD pipelines	No.	Yes.
3	Metrics storage solution	Yes. Provides both default built-in storage (for policy decisions) and optional long term storage (Prometheus stack not deployed by default).	Yes. Not part of RayServe but metrics storage solution is provided in the AnyScale stack.
4	Support for custom application metrics	Yes.	Yes.
5	Built-in Platform/System metrics	Yes.	Yes.
6	Built-in collection of hardware metrics	Yes. Hardware metrics collected by metrics collector daemonset on every node by default.	Yes. Ray exports hardware metrics as part of built-in platform metrics.
7	Dashboard UI for management	No.	Yes.
8	Built-in dashboards for visualization	No. But can be built according to cluster administrator's requirements using the Grafana deployment which comes included with the metrics stack.	Yes. Also supports custom dashboards creation and alerting.
9	Configurable logging	Yes.	Yes.
10	Updating the configuration of AI components at runtime	Yes. Using management commands.	Yes.
11	In-place code update (Update the code without bringing down the model)	No.	Yes. But code update mechanism will trigger the restart of the whole block and its replicas.
12	Implementation of custom management commands as the part of the AI block	Yes. AIOS instance SDK can support implementation of custom management commands.	No.
13	Dynamic request batching	Yes. Requests can be pooled and processed in batches.	Yes. Requests can be pooled and processed in batches.
14	gRPC inference server for submitting tasks to AI components / AI workflows	Yes.	Yes.
15	FastAPI based REST API server for submitting tasks to AI components/AI workflows	No.	Yes.
16	Customizable quota management in the Inference gateway	Yes. Quota management logic can be implemented using a python policy.	No.
17	Framework for building programmable auditing logic for workflow outputs	Yes. Auditing policies can be built to periodically collect and audit workflow outputs for QA.	No.
18	Built in Jupyter notebook integration and workspaces	No.	Yes.
19	Catching application-level failures	Yes. Users can use application level exception handling and logging to report errors.	Yes.
20	State check-pointing and state restoration upon block restarts	No.	Yes. Actor checkpointing API can be used to programmatically save and restore states.
21	LLM metrics and Custom LLM Metrics	Yes.	Yes.
22	Job schedules - schedule jobs using CRON pattern at specified intervals	No. But will be added in the future.	Yes.
23	Support for local testing of AI block	Yes.	Yes.
24	Support for local testing AI workflows end to end	No.	Yes.

Comparision summary

General Definition & Architecture

Definition: AIGrid is described as a decentralized network of interconnected AI components that coordinate to share data, perform tasks, and compose into higher-level collective intelligence. Ray is an open-source unified framework for scaling AI and Python applications like machine learning, providing the compute layer for parallel processing without requiring expertise in distributed systems. Anyscale is a platform built on top of Ray to manage deployments on a Ray cluster.
Multi-cluster support: AIGrid supports multiple federated clusters managed by a central management cluster, which can be deployed on heterogeneous clouds, data-centers, or homegrown clusters. Anyscale supports multiple clouds, and it schedules Ray workflows on these clusters based on resource availability. However, a single Ray cluster cannot span across multiple Kubernetes clusters.

Infrastructure, Deployment & Node Management

Nodes federation / Machine pooling support: Yes, nodes can be added to an existing cluster in AIGrid. Yes, nodes can be added to an existing cluster (known as customer managed machine pool) in Ray/AnyScale.
Can run without Kubernetes? AIGrid cannot run without Kubernetes. Components of the Ray ecosystem, such as RayServe and Ray, can run without Kubernetes.
Built-in managed VPC for nodes federation: AIGrid does not provide a built-in managed VPC; it depends on custom VPC, VPN, or firewall settings configured between the cluster and a node during federation, allowing clusters to use services like Tailscale, WireGuard, or any VPN. Anyscale provides a built-in VPC which uses Tailscale under the hood.
Horizontal Cluster scaling: AIGrid does not dynamically add new nodes to the cluster based on demand; clusters must be set up with pre-configured nodes, and scaling happens within the available resource pool. New nodes must be added manually. Anyscale/Ray can tap into cloud vendor infrastructure APIs to autoscale clusters by adding more nodes.

Governance, Policies & Security

Flexible network/cluster governance using programmable policies: AIGrid supports custom Python policies to govern the addition/removal of clusters, workload scheduling, and executing management clusters at both management cluster and individual worker cluster levels. Ray/AnyScale has a limited set of rule definitions based on IAM and RBAC rules provided by cloud vendors.
Programmable turing complete policies and built-in policies execution system: Yes, AIGrid is built with customizability in mind, supporting programmable policies across multiple functionalities using Python. It provides a built-in system to execute these policies locally within modules or deployed as functions, graphs, or jobs. Ray/AnyScale's autoscaler provides a customizable policy-like interface using Python, but no extensive support for customizability across different functionalities.
Built-in secret management for credentials, API keys storage: AIGrid does not have built-in secret management currently, but it is in the roadmap. Anyscale can tap into the secret management stores provided by cloud vendors.
Built-in integration with CI/CD pipelines: AIGrid does not have built-in integration with CI/CD pipelines. Anyscale supports built-in integration with CI/CD pipelines.
Customizable quota management in the Inference gateway: AIGrid allows quota management logic to be implemented using a Python policy. Ray/AnyScale does not have customizable quota management in the inference gateway.
AI block selection for inference task submission using a programmable selection logic: AIGrid allows inference task submission to contain a search query that can be used to select the right AI block for inference. Ray/AnyScale does not support this.
Assignment of Workflow DAG nodes on existing blocks using programmable assignment logic: In AIGrid, a vDAG specification can contain a programmable selection/assignment Python policy for each node, evaluated to select a block for that node. Ray/AnyScale does not support this.
Customizable and programmable health anomaly detection: AIGrid allows a programmable Python policy to be used to ingest health check data and detect anomalies. Ray/AnyScale does not support this.
Framework for building programmable auditing logic for workflow outputs: AIGrid supports building auditing policies to periodically collect and audit workflow outputs for QA. Ray/AnyScale does not support this.

Data & Storage

Persistent Storage Options available: AIGrid supports Object storage (Ceph via assets registry APIs), remote. Local file-system volume of the node, and FrameDB persistent storage (TiDB integration). Ray/AnyScale supports Object storage (remote only), local file system volume of the node, and shared network storage using NFS.
In-memory shared database support for storing objects locally and globally: AIGrid provides FrameDB. Ray/AnyScale provides Plasma Store.
Persistent database storage support for storing objects in a persistent storage volume locally and globally: AIGrid supports TiDB integration with FrameDB. Ray/AnyScale does not support this.
Backup and restore of in-memory/persistent objects to S3 like object storage: AIGrid supports this. Ray/AnyScale does not support this.
Sharing of objects across multiple nodes and creation of local copies: AIGrid supports this. Ray/AnyScale supports this.
In-memory/Persistent object store serialization format: AIGrid is flexible; serialization/deserialization is handled by the application, and the store holds raw bytes. Ray/AnyScale uses Apache Arrow serialization/deserialization format.
Reference counting and garbage collection of objects with zero reference count: AIGrid supports this. Ray/AnyScale supports this.
Built-in Ingestion support for stream data: AIGrid has built-in ingestion support for stream data. Ray/AnyScale does not.
Video/Live camera inference support: AIGrid supports video/live camera inference. Ray/AnyScale does not have built-in library support, though it can be built at the application layer.

Registries & Reusability

Built-in registries to store assets, container images, components and specifications for re-use: AIGrid has Assets registry (files, source code, models), Container registry (stores and pulls images locally), Components registry (AI instance images), and Spec store (usable specifications for vDAGs and Blocks). Ray/AnyScale does not have these built-in registries.
Support for nested workflows: AIGrid allows referencing already deployed vDAGs within the current vDAG by specifying the vDAG URI. Ray/AnyScale does not support this.
Sharing of the AI blocks across multiple workflows: AIGrid allows a block to be shared across multiple workflows by assigning the node of the workflow to it. Ray/AnyScale does not support sharing the same block (or component) across multiple workflows.

Workload Definition & Management (Blocks, vDAGs, Specifications)

Support for multi-cluster AI workflows: Yes, interconnected AI components forming a workflow in AIGrid can span across multiple clusters. No, interconnected AI components in a Ray workflow cannot span across multiple clusters.
Support for composable AI as workflows (Model composition/vDAGs): AIGrid supports this. Ray/AnyScale supports this.
Composable AI specification type: AIGrid uses JSON with template-based parsing. Ray/AnyScale uses Python code using the Ray library.
Support for conditional routing within the workflow: AIGrid supports this. Ray/AnyScale supports this.
Support for side-cars as utility applications connected to the main AI component: AIGrid can spin up side-cars as custom pods connected to the main AI block for extending functionality. Ray/AnyScale does not support this.
Updating the configuration of AI components at runtime: AIGrid supports this using management commands. Ray/AnyScale supports this.
Implementation of custom management commands as the part of the AI block: AIGrid's AIOS instance SDK can support the implementation of custom management commands. Ray/AnyScale does not support this.

Resource Management & Scheduling

Supports scaling of individual AI blocks that are part of the workflow: AIGrid supports this. Ray/AnyScale supports this.
Support for manual scaling of AI blocks: AIGrid supports this. Ray/AnyScale supports this.
Support for specifying min and max replicas per AI block: AIGrid supports this. Ray/AnyScale supports this.
Support for autoscaling based on metrics: AIGrid supports this. Ray/AnyScale supports this.
Autoscaling using programmable policy for flexible decision making: In AIGrid, the autoscaler is completely programmable using the policies system. In Ray/AnyScale, the autoscaler is completely programmable using Python and the Ray library.
Support for NVIDIA GPU Accelerators for AI block scheduling: AIGrid supports GPU-based metrics collection and scheduling based on GPU availability by default. Ray/AnyScale supports this.
Support for Google TPUs, Intel Gaudi, Huawei Ascend for AI block scheduling: AIGrid does not currently support these, but there are plans for future support. Ray/AnyScale supports these accelerators via community contributions.
Framework for porting custom accelerators: AIGrid does not have a framework for porting custom accelerators. Ray/AnyScale has a framework for porting custom accelerators.
Framework for adding custom accelerators for resource allocation: AIGrid supports this. Ray/AnyScale supports this.
Customizable AI scheduling (allocation) using programmable policies: AIGrid allows resource allocation for AI blocks to be customized using a Python policy. Ray/AnyScale provides fixed resource allocation strategies.
Concept of Placement groups: AIGrid does not have the concept of placement groups. Ray/AnyScale has placement groups, useful for gang scheduling in deep learning training and inference serving.
Support for deploying the AI block on multiple GPUs: AIGrid supports this if the inference framework allows it. Ray/AnyScale supports this if the inference framework allows it.
Support for deploying multiple AI blocks on same GPU (GPU sharing): AIGrid supports GPU sharing. Ray/AnyScale supports GPU sharing.

Training & Inference Features

Built-in model training infrastructure: AIGrid does not have built-in model training infrastructure. Ray/AnyScale has built-in model training infrastructure.
Model Multiplexing: AIGrid does not have built-in model multiplexing, but it can be achieved by specifying the AI block selection query during inference task submission. Ray/AnyScale has built-in selection for model multiplexing.
Support for streaming inference: AIGrid supports providing data to AI workflows as streams. Ray/AnyScale supports providing data to AI workflows as streams.
Support for batch inference: AIGrid supports batch inference using datasets stored in in-memory or persistent local Frame-DB databases. Ray/AnyScale supports batch inference using data stored in the Plasma store.
Out of band communication support using NCCL: AIGrid has very limited alpha support for NCCL OOB communication. Ray/AnyScale supports NCCL OOB communication.
Custom communication protocol between blocks of the workflow (Out of band communication): AIGrid does not support this. Ray/AnyScale supports this.
Custom pre and post-processor for each node in the AI workflow: AIGrid supports this. Ray/AnyScale supports this.
Support for multiple inference frameworks and libraries: AIGrid allows importing, using, and packaging inference libraries with the AI block. Ray/AnyScale supports multiple inference frameworks and libraries.
Support for deploying and serving LLM models: AIGrid supports this. Ray/AnyScale supports this.
Support for Composing of AI workflows constituting LLM and non-LLM models: AIGrid supports this. Ray/AnyScale supports this.
OpenAI compatible API for LLM serving: AIGrid does not currently have an OpenAI compatible API for LLM serving, but it will be added in the future. Ray/AnyScale has an OpenAI compatible API for LLM serving.
LLM metrics and Custom LLM Metrics: AIGrid supports LLM metrics and custom LLM metrics. Ray/AnyScale supports LLM metrics and custom LLM metrics.
Multi-node LLM deployment with built-in splitting of LLM models and distribution: AIGrid has no built-in support for this, but it can be deployed using third-party vLLM clusters alongside the AI block with init container automation. Ray/AnyScale has built-in vLLM integration.
Support for custom model splitting and distributed inference across clusters: AIGrid supports this for a very limited set of model architectures. Ray/AnyScale does not support this.
Engine agnostic architecture for LLM inference: AIGrid is engine agnostic; any LLM serving library can be embedded, or any third-party inference server can be linked and automated via init containers. Ray/AnyScale is engine agnostic.
Multi-LoRA support with shared base models: AIGrid does not support this. Ray/AnyScale supports multi-LoRA with shared base models.
Fast model loading with safe tensors and local machine cache: AIGrid does not currently support this, but it will be added in the future. Anyscale supports fast model loading with safe tensors and local machine cache.

APIs & Interfaces

SDKs to build and deploy AI instances: AIGrid provides SDKs. Ray/AnyScale provides SDKs.
gRPC inference server for submitting tasks to AI components / AI workflows: AIGrid supports a gRPC inference server. Ray/AnyScale supports a gRPC inference server.
FastAPI based REST API server for submitting tasks to AI components/AI workflows: AIGrid does not support a FastAPI based REST API server. Ray/AnyScale supports a FastAPI based REST API server.

Observability & Monitoring

Metrics storage solution: AIGrid provides both default built-in storage for current metrics needed by policies and supports optional deployment of Prometheus stack for long-term storage. Anyscale provides a metrics storage solution as part of its stack.
Support for custom application metrics: AIGrid supports custom application metrics. Ray supports custom application metrics.
Built-in Platform System metrics: AIGrid has built-in Platform System metrics. Ray has built-in Platform System metrics.
Built-in collection of hardware metrics: AIGrid collects hardware metrics using a metrics collector daemonset deployed on every node by default. Ray exports hardware metrics as part of its built-in platform metrics.
Dashboard UI for management: AIGrid does not have a built-in dashboard UI for management. Anyscale has a dashboard UI for management.
Built-in dashboards for visualization: AIGrid does not have built-in dashboards but supports building them using Grafana (included with the metrics stack). Anyscale has built-in dashboards and supports custom dashboard creation and alerting.
Configurable logging: AIGrid supports configurable logging. Anyscale supports configurable logging.
AI blocks replica health checking: AIGrid supports periodic health checking of all replicas of an AI block. Ray/AnyScale supports periodic health checking of all replicas of an AI block.

Fault Tolerance

AI blocks replica health checking: (Also listed under Observability) AIGrid supports periodic health checking of all replicas of an AI block. Ray/AnyScale supports periodic health checking of all replicas of an AI block.
Catching application-level failures: AIGrid allows users to use application-level exception handling and logging to report errors. Ray supports catching application-level failures.
State check-pointing and state restoration upon block restarts: AIGrid does not support this. Ray supports this using the Actor checkpointing API to programmatically save and restore states.
Recovery of lost objects using Lineage reconstruction: AIGrid does not support recovery using lineage reconstruction. Ray supports recovery of lost objects using lineage reconstruction.

Integration

Built-in integration with CI/CD pipelines: (Also listed under Governance) AIGrid does not have built-in integration with CI/CD pipelines. Anyscale supports built-in integration with CI/CD pipelines.
Connecting external / third party servers to the AI blocks: AIGrid supports this. Ray/AnyScale supports this; the block can contain Python functions that interact with external services.
Automating the deployment of third party services on the cluster using init containers at the time of AI block creation: AIGrid supports this. Ray/AnyScale does not support this.
Built-in Cross language programming: AIGrid does not have built-in cross-language programming, but users can handle interactions with other languages explicitly by packaging them with the AI block. Ray has partial support for cross-language programming (Java to Python and Python to Java).
Built in Jupyter notebook integration and workspaces: AIGrid does not have built-in Jupyter notebook integration and workspaces. Ray/AnyScale supports built-in Jupyter notebook integration and workspaces.

Communication & Data Format

Out of band communication support using NCCL: (Also listed under Training/Inference) AIGrid has very limited alpha support. Ray/AnyScale supports this.
Custom communication protocol between blocks of the workflow (Out of band communication): (Also listed under Training/Inference) AIGrid does not support this. Ray/AnyScale supports this.
Core communication data format: AIGrid uses Protobuf and flexible serialization/deserialization with in-memory FrameDB. Ray/AnyScale uses Plasma Object (PyArrow format).

Development & Testing

Support for local testing of AI block: AIGrid supports local testing of AI blocks. Ray/AnyScale supports local testing of AI blocks.
Support for local testing AI workflows end to end: AIGrid does not support local end-to-end workflow testing. Ray/AnyScale supports local end-to-end workflow testing.

Other Features

Dynamic request batching: AIGrid supports dynamic request batching. Ray/AnyScale supports dynamic request batching.
Customizable batching logic: AIGrid allows developers to write custom batching logic using the AIOS instance SDK. Ray/AnyScale does not have customizable batching logic; fixed batching parameters can be provided.
Job schedules: AIGrid does not currently support scheduling jobs using a CRON pattern, but it will be added in the future. Anyscale supports scheduling jobs using a CRON pattern.
Supports non AI workflowns and non-AI computation as blocks: AIGrid supports non-AI workloads and computation as blocks. Ray/AnyScale supports non-AI workloads and computation.