Top 5 Kong AI Alternatives

Artificial intelligence is reshaping the way organizations develop, deploy, and scale applications, and managing AI models efficiently has become a top priority for developers. 

With the surge of large language models (LLMs) and generative AI, businesses need platforms that not only facilitate access to these models but also ensure reliability, observability, and cost optimization. 

Kong AI has emerged as a notable solution in this space, offering AI-powered API management and monitoring capabilities designed to streamline workflows and enhance the performance of AI applications.

However, no single platform can meet every organization’s unique requirements. Factors such as deployment flexibility, enterprise-grade security, observability, and integration with existing infrastructure can influence the choice of an AI management platform. 

Exploring alternatives to Kong AI allows developers to find tools that better align with their operational needs, technology stack, and growth ambitions. 

In this article, we will dive deep into what Kong AI offers, how it works, and highlight the top five Kong AI alternatives — from open-source gateways to full-stack enterprise AI infrastructure platforms like TrueFoundry — to help you choose the right foundation for your AI ecosystem.

What is Kong AI?

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Kong AI is an enterprise-grade AI gateway built on top of the Kong Gateway, designed to simplify the integration, management, and governance of AI models within organizations. It provides a unified API platform that allows businesses to securely expose, route, and monitor AI services from multiple providers, including OpenAI, Azure AI, AWS Bedrock, and GCP Vertex. 

By centralizing AI traffic management, Kong AI helps organizations accelerate AI initiatives while maintaining control over security, compliance, and operational performance.

The platform is particularly suited for enterprises that need to implement advanced AI workflows, manage multiple language models, and enforce governance policies at scale. Its features are tailored to streamline AI development, reduce operational overhead, and improve cost efficiency, all while ensuring reliable and secure access to AI resources.

Key Features:

Multi-LLM Support: Kong AI enables seamless integration with multiple AI providers, allowing organizations to switch between models efficiently to meet different use cases or maintain high availability.

Automated RAG Pipelines: The platform can automatically build Retrieval-Augmented Generation pipelines, improving the accuracy of AI responses and reducing hallucinations in generated outputs.

PII Sanitization and Prompt Security: Kong AI enforces content safety and compliance by sanitizing sensitive data and implementing prompt-level security rules across all AI interactions.

No-Code AI Integrations: Developers can enrich, transform, or augment API traffic using supported LLMs without writing any code, allowing rapid deployment of AI capabilities across applications.

Advanced Traffic Management: Semantic caching, routing, and load balancing optimize AI traffic, reduce redundant API calls, and control costs, while observability features provide actionable insights on performance and usage.

Kong AI also supports model control protocols (MCP), enabling secure, reliable, and scalable deployment of AI servers. Its analytics and observability tools allow teams to track AI consumption, optimize costs, and monitor performance across all AI workloads. 

How Does Kong AI Work?

Kong AI builds on the core architecture of Kong Gateway and adds AI-specific layers and plugins designed to manage, secure, and optimize traffic to large language models (LLMs). It acts as a middleware that intercepts LLM requests and responses, enforcing policies, routing intelligently, and enabling observability. At a high level, your application sends requests to Kong AI instead of directly to model providers. Kong AI then processes, transforms, and forwards those requests, returning responses while capturing metrics, enforcing governance, and optimizing performance.

Proxy and Plugin Framework

Kong AI uses the same proxy and plugin architecture as Kong Gateway. When an AI request arrives, the request passes through configured AI plugins before reaching the upstream model endpoint. Plugins may modify prompts, enforce guardrails, or route traffic based on type, cost, or performance.

Prompt and Content Policies

Kong AI supports plugins such as Prompt Guard and Semantic Prompt Guard. These plugins inspect the request content, validate or filter prompts, and block or sanitize inputs or responses if they violate policy. This ensures compliance, data safety, and enforcement of content limits.

Automatic RAG Pipeline Injection

One of Kong AI’s unique capabilities is building Retrieval-Augmented Generation (RAG) logic at the gateway layer. Through a dedicated plugin, Kong AI can inject relevant documents or context from vector stores into prompts dynamically. This reduces hallucinations and improves model reliability without requiring developers to code RAG logic in each application.

Traffic Routing, Caching, and Load Balancing

Kong AI implements advanced routing and caching mechanisms that optimize traffic flow. It can balance requests across different LLMs based on latency, cost, or semantic similarity. Its semantic caching allows repeated or similar prompts to be served instantly from cache, reducing redundant model calls and saving token costs.

Observability, Metrics, and Cost Control

As it processes AI traffic, Kong AI collects data such as token usage, request latency, error rates, and model performance. These metrics are aggregated into dashboards to track usage, cost, and system health. The gateway also supports predictive modeling and debugging tools for analyzing AI exposure and optimizing workloads.

Sample workflow with Kong AI

• A client sends a chat prompt to /llm/v1/chat.
• Kong AI’s Prompt Guard plugin checks the content; if acceptable, it passes the request.
• The traffic routing plugin selects a model endpoint (e.g. OpenAI or Bedrock) based on cost, latency, or semantic fit.
• If repeated prompts are provided, the semantic cache returns a cached response.
• If relevant, RAG Injector adds context from a vector database.
• Kong forwards the modified prompt to the selected model.
• Response returns through the gateway; response transformer plugins may decorate or sanitize the output.
• Kong logs metrics and returns the final response to the client.

Because Kong AI combines the mature tooling of Kong Gateway with these AI-specific features, it can operate in environments from self-hosted to enterprise deployments, offering security, observability, and governance for AI traffic alongside routing and policy control.

Why explore Kong AI Alternatives?

While Kong AI Gateway delivers robust features for managing and securing AI traffic, it may not suit every organization’s specific needs. Some teams require deeper integration with agentic frameworks, more flexible observability, or finer control over model orchestration beyond API-level management. Others may prefer open-source platforms that allow greater customization or self-hosting freedom without enterprise licensing constraints.

Additionally, Kong AI’s strength lies in its API governance and enterprise compliance, which can feel complex or excessive for smaller teams focused solely on LLM experimentation or lightweight workloads. 

Cost considerations and deployment overhead may also drive teams to evaluate purpose-built LLMOps or AI infrastructure tools that provide faster setup and broader ecosystem compatibility.

Exploring alternatives ensures that organizations choose the platform best aligned with their AI stack, whether the priority is scalability, developer agility, cost efficiency, or end-to-end model observability.

Top 5 Kong AI Alternatives

While Kong AI offers robust governance and observability, several platforms now provide more advanced AI orchestration and gateway features. These alternatives offer greater flexibility, multi-model routing, and more comprehensive control over AI workloads. Below are the top five platforms leading this evolution.

1. TrueFoundry 

TrueFoundry empowers enterprises to govern, deploy, scale, and observe agentic AI using a unified, end-to-end platform. Unlike point solutions that handle only orchestration or model hosting, TrueFoundry builds a full stack that supports secure, compliant, and high-performance AI at scale. It is Kubernetes-native and designed to serve enterprise needs in hybrid, VPC, on-premises, or air-gapped environments.

Orchestrate with AI Gateway

TrueFoundry’s AI Gateway provides a centralized protocol for agent workflows. It manages memory, tool orchestration, and multi-step reasoning, allowing agents to plan actions, call external tools, and maintain contextual state with full visibility and control.

MCP and Prompt Lifecycle Management

The MCP and Agents Registry maintains a structured library of tools and APIs with schema validation and fine-grained access controls. Combined with Prompt Lifecycle Management, teams can version, test, and monitor prompts to ensure consistent and auditable agent behavior.

Deploy Any Model, Any Framework

Enterprises can host any LLM or embedding model using optimized backends such as vLLM, TGI, or Triton. Fine-tuning is integrated in the workflow to train on proprietary data and deploy updated checkpoints. Agents built on LangGraph, CrewAI, AutoGen, or custom frameworks are fully supported and containerized for production.

Enterprise-Grade Compliance and Observability

TrueFoundry can run in VPC, on-premises, hybrid, or air-gapped environments, ensuring full control over data. It is SOC 2, HIPAA, and GDPR compliant and supports SSO, RBAC, and immutable audit logging. Observability includes full tracing for agents, monitoring GPU and CPU usage, node health, and scaling behavior. Metrics can be integrated with Grafana, Prometheus, or Datadog.

Optimized for Scale and Cost

The platform provides GPU orchestration, fractional GPU support, and real-time autoscaling to maximize utilization and minimize costs. Enterprises like NVIDIA report up to 80 percent improvement in GPU cluster efficiency using TrueFoundry’s automation and agentic workflows.

By unifying orchestration, deployment, compliance, and observability, TrueFoundry delivers a complete enterprise platform for building and scaling agentic AI with confidence.

While Kong AI governs API traffic, TrueFoundry governs the entire AI lifecycle — from model serving to agent monitoring — making it ideal for enterprise-grade deployments.

2. Portkey

Portkey is an open-source AI gateway designed to streamline the integration and management of multiple LLM providers. It provides a unified interface that enables developers to route, monitor, and control AI requests efficiently. Portkey simplifies observability by logging prompts, responses, tokens, and latencies in real time. 

It also supports schema validation and tool orchestration for agentic workflows, making it suitable for both small teams and enterprises. By centralizing multi-model management, Portkey ensures developers maintain visibility and control over AI applications without significant infrastructure overhead. Its open-source nature encourages community contributions and rapid feature evolution.

Key Features:

• Multi-LLM routing across providers with cost and latency optimization
• Real-time request logging and observability dashboards
• Schema validation and tool registry for AI agents
• Lightweight and easy to self-host
• Open-source with active community contributions
  

3. LiteLLM

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LiteLLM is a lightweight platform that enables developers to manage, monitor, and optimize LLM requests in production environments. It focuses on semantic caching and efficient token usage to reduce operational costs. LiteLLM provides detailed observability, including request logging, latency tracking, and performance metrics. 

It supports multiple LLM providers such as OpenAI, Anthropic, and Google Gemini, offering flexibility for multi-model applications. Its container-friendly architecture allows rapid deployment and scaling without heavy infrastructure management. The platform is ideal for teams seeking a simple yet effective way to standardize AI operations.

Key Features:

• Unified API for multiple LLM providers
• Semantic caching to reuse responses and reduce costs
• Request logging and performance metrics tracking
• Easy deployment in containerized environments
• Supports OpenAI, Anthropic, Google Gemini, and others
  

4. AWS Bedrock

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AWS Bedrock is a fully managed AI service designed to help enterprises build applications using foundation models without managing underlying infrastructure. It provides access to multiple models, including Anthropic, AI21, and Amazon Titan. The platform ensures enterprise-grade security and compliance, integrating seamlessly with AWS identity and monitoring tools. Bedrock simplifies multi-model experimentation by providing centralized routing, logging, and cost monitoring. It is optimized for large-scale workloads and production deployments, allowing developers to focus on AI application development rather than infrastructure management.

Key Features:

• Access to foundation models from Anthropic, AI21, Amazon Titan, and more
• Fully managed infrastructure for AI workloads
• Integrated security, compliance, and identity management
• Multi-model experimentation with cost and performance insights
• Easy integration with the AWS ecosystem and analytics tools
  

5. Azure AI Foundry

Azure AI Foundry is Microsoft’s cloud-native platform for deploying, monitoring, and governing AI applications. It provides secure access to Azure OpenAI Service and custom models while offering centralized control over AI workloads. The platform ensures enterprise compliance with SOC 2, HIPAA, and GDPR standards. 

Azure AI Foundry includes observability features like prompt and token tracking, latency monitoring, and usage analytics. Hybrid deployment options allow organizations to run workloads on-premises, in the cloud, or across multiple regions. It is ideal for enterprises that need a secure, scalable, and governance-ready AI infrastructure.

Key Features:

• Supports Azure OpenAI Service and custom LLMs
• Centralized governance and access control for AI workloads
• Observability and telemetry for models and prompts
• Hybrid and multi-cloud deployment support
• Enterprise compliance with SOC 2, HIPAA, and GDPR

Conclusion

Kong AI Gateway is a powerful solution for managing AI traffic, ensuring security, and providing observability across multi-LLM environments. However, organizations have diverse needs, from lightweight multi-model routing to enterprise-grade orchestration and compliance. 

Platforms like TrueFoundry, Portkey, LiteLLM, AWS Bedrock, and Azure AI Foundry offer alternatives that address specific gaps, including agent tracing, prompt lifecycle management, and GPU optimization. Choosing the right platform depends on scale, deployment flexibility, and governance requirements. Exploring these alternatives ensures teams can build reliable, efficient, and secure AI applications tailored to their operational and business objectives.