AI Orchestration Platform

Helps design, control, and scale AI workflows (prompts, APIs, usage) with governance built in. Promptetus

Routing of prompts, integration of APIs, monitoring of usage.
Why it stands out: Focused on the workflow/orchestration side (rather than just infra). Good if you need to coordinate multiple models, tools, and agents.
Potential trade-offs: May be less focused on low-level infrastructure (e.g., GPU scheduling) and more on the orchestration above that.
Use-case fit: If you are building multiple LLM/agent‐based services inside your stack and need governance, routing, usage analytics.

dstack

6

What it is: A platform focused on the infrastructure / compute / orchestration layer for ML/AI teams.
Key features:

• Provisioning and orchestration of GPU clusters across cloud, Kubernetes, on-prem. Dstack
• Single-node & distributed tasks, dev environments, scalable service endpoints.
  Why it stands out: If the major pain is infrastructure orchestration (GPUs, clusters, scaling models) then this is a strong fit.
  Potential trade-offs: Might be less about high-level agents/workflows and more about compute orchestration.
  Use-case fit: If you are building heavy ML/LLM workloads, need to scale training/inference, manage clusters, want to abstract infra.

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3. nanoworks

6

What it is: A platform targeted at generative AI, multi-agent orchestration, and workflow management for enterprises.
Key features:

• Centralise, manage and harmonise all your AI agents from a single platform.
• No-code visual editor for GenAI workflows; full audit, logs, metrics, versioning.
• Deploy on‐prem or private cloud, focusing on data sovereignty, security, enterprise‐grade.
  Why it stands out: Good if your use case involves orchestrating multiple agents (e.g., retrieval agent → reasoning agent → action agent) and you want visibility/governance.
  Potential trade-offs: It may be newer/more niche compared to general ML infra orchestration, so one should check maturity/integrations.
  Use-case fit: If you are building agentic workflows (not just single LLM calls), need governance/observability, and possibly enterprise deployment.

Full-stack AI orchestration platform” can be everything from data ingestion → feature engineering → model training/fine-tuning → pipeline orchestration → deployment → monitoring/governance

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✍️ Comparative Summary

Platform	Strengths	Focus area
Prompteus	Workflow orchestration of prompts/agents/apis	High-level orchestration of LLM/agent workflows
dstack	Compute & infra orchestration for ML/AI	Training/inference scale, cluster management
nanoworks	Multi-agent orchestration + enterprise governance	Agent w

Overview Comparison

Category	AI Gateway	AI Agent-Based Platform	Full-Stack AI Orchestration Platform
Core Purpose	Manage and route AI/LLM traffic, enforce guardrails, monitor and secure inference APIs	Build, deploy, and manage intelligent agents (LLMs + tools + reasoning logic)	Manage entire AI lifecycle: data → model → orchestration → deployment → governance
Primary Focus	Inference management and policy control	Cognitive automation and reasoning orchestration	End-to-end AI lifecycle and multi-layer orchestration
Users	DevOps, API engineers, platform architects	AI developers, product engineers	Enterprise AI/ML teams, MLOps engineers
Deployment Scope	Edge or API layer	Application/logic layer	Data + Model + Application layers
Examples	Kong AI Gateway, Nvidia NIM Router, Cloudflare AI Gateway, BentoML Inference Gateway	LangChain, Langflow, CrewAI, AutoGen, Meta Agentic SDK	Databricks Mosaic AI, NVIDIA NeMo Orchestrator, IBM Watsonx Orchestrate, Predibase, Hugging Face Infinity
Integration Focus	Multi-model routing, API security, governance	Agent composition, reasoning loops, tool use	Data pipelines, model training, serving, monitoring, scaling
Output	Controlled and observable AI responses	Task completion and reasoning-based outcomes	Integrated AI system with full lifecycle visibility

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2. Architectural Layer Breakdown

Layer	AI Gateway	AI Agent Platform	Full-Stack Orchestration Platform
Data Ingestion / ETL	❌ (Handled upstream)	⚙️ Optional (through agents)	✅ Native or integrated
Feature Engineering / Model Training	❌	❌	✅ Core component
Model Registry / Versioning	⚙️ Routes between existing endpoints	⚙️ Sometimes tracks model versions	✅ Native component (MLflow, HuggingFace Hub, etc.)
Inference Serving	✅ Central focus (proxy & scaling)	⚙️ Uses external inference endpoints	✅ Integrated (batch, streaming, real-time)
Agent Composition / Reasoning	⚙️ Limited (prompt routing, policy)	✅ Central component	✅ Included (as one orchestration mode)
Multi-LLM Routing	✅ Core feature	⚙️ Partial (model selection per agent)	✅ Through orchestration
Observability / Governance	✅ Deep focus	⚙️ Limited (logs/traces)	✅ Enterprise-grade observability & auditability
Security / Policy / Guardrails	✅ Core feature	⚙️ Optional	✅ Included (governance + compliance)
Deployment & Scaling	✅ API Gateway scaling	⚙️ Orchestrates tasks	✅ Manages training, serving, agents, APIs
Automation / Workflow Orchestration	⚙️ Policy-based (traffic rules)	✅ Task-graph execution	✅ Full pipeline & multi-agent orchestration

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3. Example Technology Stacks

Stack	AI Gateway	AI Agent Platform	Full-Stack AI Orchestration
Nvidia	NIM Router, Triton Inference Server	NeMo Guardrails	NVIDIA AI Enterprise + NeMo Orchestrator
Open Source	Kong Gateway (AI Gateway 3.11), BentoML, OpenInference	LangChain, CrewAI, AutoGen, Langflow	Kubeflow, Flyte, Airflow + Ray + MLflow
Cloud Providers	Azure AI Gateway, AWS Bedrock Guardrails, GCP Vertex AI Gateway	Anthropic Agent SDK, Amazon Bedrock Agents	Databricks Mosaic AI, Google Vertex AI, IBM Watsonx.ai
Enterprise Vendors	Cloudflare AI Gateway, Kong Konnect	Cognigy.AI, Kore.ai, Inflection Pi	C3.ai, Scale Spellbook, Predibase, DataRobot

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4. Comparative Capabilities Matrix

Capability	AI Gateway	Agent Platform	Full-Stack Orchestration
Multi-model routing	✅	⚙️	✅
Guardrails / policy control	✅	⚙️	✅
Agent reasoning (chain-of-thought, tools)	❌	✅	✅
Visual workflow design	⚙️ (policy maps)	✅	✅
Model training / fine-tuning	❌	❌	✅
Data pipeline management	❌	❌	✅
Retrieval-Augmented Generation (RAG)	⚙️ via upstream	✅	✅
Observability / telemetry	✅	⚙️	✅
Governance & audit	✅	⚙️	✅
Multi-tenant scaling	✅	⚙️	✅
API integration layer	✅	✅	✅
Custom model hosting	⚙️	⚙️	✅
Agent orchestration	⚙️	✅	✅
Lifecycle automation (train-deploy-monitor)	❌	❌	✅

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5. Example Architecture Diagram

a. AI Gateway

[Client Apps] → [AI Gateway Layer] → [Model APIs (OpenAI, Claude, Llama, Custom)]
                   ↳ Security
                   ↳ Rate limiting
                   ↳ Routing / Logging

b. AI Agent Platform

[User Query] → [Agent (LLM + Memory + Tools)]
                   ↳ Calls APIs / Databases / Functions
                   ↳ Uses RAG / Tool Use
                   ↳ Returns task completion

c. Full-Stack AI Orchestration Platform

[Data Sources] → [Ingestion + Feature Store]
     → [Model Training & Registry]
     → [Inference Service / Agent Builder]
     → [Gateway + Monitoring + Governance]

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6. Summary Table

Dimension	AI Gateway	AI Agent Platform	Full-Stack AI Orchestration
Scope	Infrastructure	Application logic	Enterprise lifecycle
Layer	Network/API	Application	Platform
Strength	Security, routing, scalability	Reasoning, task automation	Integration, lifecycle management
Weakness	No reasoning or data pipelines	Limited governance and training	Complex and resource-heavy
Ideal Use Case	API control, traffic management, AI compliance	Build AI copilots, automation workflows	Enterprise AI system for production-scale AI/ML

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7. Putting It All Together

Stack Combination	Description
Langflow + Kong	Agent orchestration + AI Gateway = lightweight orchestrated system
LangChain + Vertex AI	Agent framework + full-stack orchestration
NeMo Orchestrator + NIM Router	True enterprise full-stack orchestration with gateway + agent + data layers
Databricks Mosaic AI	Single unified full-stack orchestration platform with governance, training, inference

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Final Summary

Term	Description in One Line
AI Gateway	“The API layer that routes, secures, and governs AI model calls.”
AI Agent Platform	“The cognitive layer that chains reasoning, memory, and tools to solve tasks.”
Full-Stack AI Orchestration Platform	“The enterprise layer that manages data, models, agents, and deployment end-to-end.”