Similarities
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Unified Inference API.
Both TensorZero and Portkey offer a unified inference API that allows you to access LLMs from most major model providers with a single integration, with support for structured outputs, batch inference, tool use, streaming, and more.
→ TensorZero Gateway Quickstart -
Automatic Fallbacks, Retries, & Load Balancing for Higher Reliability.
Both TensorZero and Portkey offer automatic fallbacks, retries, and load balancing features to increase reliability.
→ Retries & Fallbacks with TensorZero -
Experimentation (A/B Testing or Canary Testing).
Both TensorZero and Portkey offer experimentation features to help you test your prompts and models.
→ Experimentation (A/B Testing) with TensorZero -
Schemas, Templates.
Both TensorZero and Portkey offer schema and template features to help you manage your LLM applications.
→ Prompt Templates & Schemas with TensorZero -
Multimodal Inference.
Both TensorZero and Portkey support multimodal inference.
→ Multimodal Inference with TensorZero
Key Differences
TensorZero
- Open-Source Observability. TensorZero offers built-in open-source observability features, collecting inference and feedback data in your own database. Portkey also offers observability features, but they are limited to their commercial (hosted) offering.
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Built-in Evaluations.
TensorZero offers built-in evaluation functionality, including heuristics and LLM judges.
Portkey doesn’t offer any evaluation features.
→ TensorZero Evaluations Overview -
Open-Source Inference Caching.
TensorZero offers open-source inference caching features, allowing you to cache requests to improve latency and reduce costs.
Portkey also offers inference caching features, but they are limited to their commercial (hosted) offering.
→ Inference Caching with TensorZero -
Open-Source Fine-Tuning Workflows.
TensorZero offers open-source built-in fine-tuning workflows, allowing you to create custom models using your own data.
Portkey also offers fine-tuning features, but they are limited to their enterprise ($$$) offering.
→ Fine-Tuning Recipes with TensorZero -
Advanced Fine-Tuning Workflows.
TensorZero offers advanced fine-tuning workflows, including the ability to curate datasets using feedback signals (e.g. production metrics) and the ability to use RLHF for reinforcement learning.
Portkey doesn’t offer similar features.
→ Fine-Tuning Recipes with TensorZero -
Inference-Time Optimizations.
TensorZero offers built-in inference-time optimizations (e.g. dynamic in-context learning), allowing you to optimize your inference performance.
Portkey doesn’t offer any inference-time optimizations.
→ Inference-Time Optimizations with TensorZero - Programmatic & GitOps-Friendly Orchestration. TensorZero can be fully orchestrated programmatically in a GitOps-friendly way. Portkey can manage some of its features programmatically, but certain features depend on its external commercial hosted service.
Portkey
- Access Control. Portkey offers access control features, including virtual keys and budgets; that said, these features are only available on their commercial (hosted) offering. TensorZero doesn’t offer built-in access control features, and instead requires you to manage it externally (e.g using Nginx).
- Prompt Playground. Portkey offers a prompt playground in its commercial (hosted) offering, allowing you to test your prompts and models in a graphical interface. TensorZero doesn’t offer a prompt playground today (coming soon!).
- Guardrails. Portkey offers guardrails features, including integrations with third-party guardrails providers and the ability to use custom guardrails using webhooks. For now, TensorZero doesn’t offer built-in guardrails, and instead requires you to manage integrations yourself.
- Managed Service. Portkey offers a paid managed (hosted) service in addition to the open-source version. TensorZero is fully open-source and self-hosted.