Best Portkey Alternatives in 2026: Top 3 Open-Source AI Gateways
Portkey became one of the best-known AI gateways by giving teams a single control plane for routing, observability, and guardrails across LLM providers. In 2026 it changed hands. Palo Alto Networks announced its intent to acquire Portkey on April 30, 2026, and the deal closed at the end of May. Portkey will now serve as the AI gateway for Palo Alto Networks' Prisma AIRS platform. Palo Alto has…
In 2026, the AI gateway Portkey was acquired by Palo Alto Networks, leading many engineering teams to seek alternative solutions. The top three open-source alternatives are GoModel, LiteLLM, and TensorZero. These options offer distinct features for teams looking for control over their AI gateway.
GoModel stands out as the best overall alternative due to its architecture and transparency. Built in Go as a single static binary, it avoids supply-chain vulnerabilities associated with Python runtimes. The MIT-licensed gateway provides an OpenAI-compatible API, multi-provider routing, cost controls, observability, and guardrails. Its transparent open-core model allows teams to inspect what they're paying for, making it an attractive choice for regulated industries and on-premises deployments.
LiteLLM, the most widely adopted open-source LLM proxy, supports a vast number of providers and offers a Python SDK. While it has a larger dependency tree and heavier runtime than GoModel, its extensive provider coverage and community support make it a strong choice for Python-centric teams. However, its built-on Python increases the risk of supply-chain compromises and requires more instances and tuning for high throughput.
TensorZero offers a different approach by unifying an LLM gateway with observability, optimization, evaluation, and experimentation. Written in Rust, it boasts sub-1 ms latency overhead and built-in optimization tooling for prompts, models, and inference strategies. The Rust-based gateway provides under 1 ms of latency overhead at 10k QPS, making it an attractive option for performance-focused teams.
However, TensorZero's opinionated configuration model and feedback workflow may be less suitable for those seeking a more drop-in alternative.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
