Similarities
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Unified Inference API.
Both TensorZero and Kong AI Gateway offer a unified API that allows you to access LLMs from multiple model providers with a single integration, with broad support for features like structured generation, tool use, file inputs, and more.
→ TensorZero Gateway Quickstart -
Automatic Fallbacks for Higher Reliability.
Both TensorZero and Kong AI Gateway offer automatic fallbacks and load balancing between model providers to increase reliability.
→ Retries & Fallbacks with TensorZero -
LLM Observability.
Both TensorZero and Kong AI Gateway support OpenTelemetry for exporting traces and metrics.
TensorZero also provides its own LLM-native observability (structured inference + feedback data stored in your database), while Kong provides API-oriented observability (e.g. request latency breakdowns, GenAI OpenTelemetry span attributes).
→ TensorZero Observability Overview -
LLM Controls.
Both TensorZero and Kong AI Gateway offer operational controls for LLM traffic, including rate limiting (by tokens, cost, or request count), usage and spend tracking, credential management, and custom API keys for access control.
→ Enforce custom rate limits
→ Track usage and cost
→ Manage credentials (API keys)
→ Set up auth for TensorZero - Open Source. Both TensorZero and Kong Gateway are open source (Apache 2.0). However, TensorZero is fully open-source, whereas the Kong AI Gateway gates many features behind an enterprise license.
Key Differences
TensorZero
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High Performance.
The TensorZero Gateway was built from the ground up in Rust with performance in mind (<1ms P99 latency at 10,000 QPS).
Kong AI Gateway does not publish comparable LLM-specific overhead benchmarks, and its performance is highly dependent on the plugin chain configured.
→ TensorZero Performance Benchmarks - Built-in LLM-Native Observability. TensorZero collects structured inference and feedback data in your own database (Postgres or ClickHouse), enabling a closed-loop system where production data feeds directly into evaluations, optimization, and experimentation. Kong’s observability is API-platform-native (standard request metrics and OpenTelemetry export) rather than LLM-workflow-native.
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Built-in Evaluations.
TensorZero offers built-in evaluation functionality, including heuristics and LLM judges.
Kong AI Gateway does not offer native evaluation capabilities; evaluation is typically handled by external tools and pipelines.
→ TensorZero Evaluations Overview -
Automated Experimentation (A/B Testing).
TensorZero offers built-in experimentation features, including adaptive A/B testing, to help you identify the best models and prompts for your use cases.
Kong AI Gateway can do weighted routing and load balancing, but metric-driven experiment orchestration is not a built-in feature.
→ Run adaptive A/B tests with TensorZero -
Built-in Inference-Time Optimizations.
TensorZero offers built-in inference-time optimizations (e.g. dynamic in-context learning), allowing you to optimize your inference performance.
Kong AI Gateway does not offer inference-time optimizations in this sense.
→ Inference-Time Optimizations with TensorZero -
Optimization Recipes.
TensorZero offers optimization recipes (e.g. supervised fine-tuning, RLHF, GEPA) that leverage your own data to improve your LLM’s performance.
Kong AI Gateway does not offer any features like this.
→ LLM Optimization with TensorZero -
Schemas, Templates, GitOps.
TensorZero enables a schema-first approach to building LLM applications, allowing you to separate your application logic from LLM implementation details.
This approach allows you to more easily manage complex LLM applications, benefit from GitOps for prompt and configuration management, counterfactually improve data for optimization, and more.
Kong AI Gateway does not offer a comparable schema-first approach for LLM applications.
→ Prompt Templates & Schemas with TensorZero -
Inference Caching.
TensorZero offers open-source inference caching features, allowing you to cache requests to improve latency and reduce costs.
Kong offers a semantic cache plugin, but it is only available as part of AI Gateway Enterprise.
→ Inference Caching with TensorZero
Kong AI Gateway
- General-Purpose API Gateway Platform. Kong AI Gateway is built on top of Kong Gateway, a mature API gateway and reverse proxy. This means it inherits a rich set of platform capabilities that go far beyond LLM traffic. TensorZero is purpose-built for LLM inference and LLMOps workflows, not general API management.
- Enterprise Governance. Kong AI Gateway offers dedicated AI governance plugins (e.g. PII sanitization) that can enforce policies at the gateway layer. TensorZero does not offer built-in guardrails or content safety plugins; you’ll need to integrate with other tools.
- Enterprise API Security Features. Kong offers RBAC with roles/permissions, vault-backed secret stores, and other security features on the enterprise plan. TensorZero offers API key authentication, credential management, and custom rate limiting, but you’ll need to layer specialized security and/or networking software for additional general-purpose API controls.
- Plugin Ecosystem. Kong offers a large plugin marketplace with categories spanning authentication, security, traffic control, logging, and more, plus a Plugin Development Kit (PDK) for custom plugins. TensorZero supports industry standards (e.g. OpenTelemetry, OpenInference) and offers configuration-driven extensibility, but does not have a built-in plugin marketplace.
- Managed Service. Kong offers Konnect, a managed SaaS/hybrid platform for managing Kong Gateway deployments. TensorZero is fully open-source and self-hosted.