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Portkey vs Together AI

PortkeyTogether AI

Bottom line: Portkey for teams routing production traffic across many LLMs; Together AI for cost-conscious teams on open models.

	Portkey Open-source AI gateway and control plane for production Gen AI. Visit	Together AI Inference, fine-tuning, and GPU clusters for open models. Visit
Votes	0	0
Pricing	Freemium	Freemium
Category	Coding	Coding
Tags	ai-gatewayllmopsopen-sourceobservabilitygovernance	inferencefine-tuninggpu-cloudopen-sourcellm-api
Best for	Teams routing production traffic across many LLMs Organizations needing governance and reliability tooling Developers wanting an open-source, self-hostable gateway	Cost-conscious teams on open models ML teams that fine-tune Startups scaling inference volume
Pros	Fully open-source, self-hostable gateway for data control and no lock-in Access to more than 1,600 models through one API Broad feature set spanning observability, guardrails, routing, prompts, and cost control Transparent, relatively cheap entry with free and ~$49 tiers Battle-tested at very large scale	Large catalog of open and open-weight models Competitive per-token pricing Fine-tuning with weight ownership Dedicated GPU clusters for scale OpenAI-compatible API
Cons	Adds a network hop and dependency in the critical request path Log-volume-based pricing can get complex or expensive at scale Free-tier logging caps and short retention are tight for real debugging Self-hosting the open-source gateway shifts ops burden to you It is a gateway and ops layer, not a full build-and-eval IDE	Broad pricing surface across several product lines You own quality and safety evaluation of open models Dedicated clusters require commitment and planning Less turnkey than closed frontier APIs Model catalog and prices change over time

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