Build and scale GenAI apps on Azure | Portkey

Run GenAI at scale, fully inside Azure

Portkey plugs into your Azure AI stack - deploy via Marketplace, connect Entra SSO/SCIM, apply Content Safety guardrails, and run Azure OpenAI or AI Foundry models seamlessly.

Build a secure and scalable Gen AI platform on Azure

Portkey gives Azure AI teams the control, visibility, and infrastructure they need to ship GenAI applications faster.

Know what’s driving your AI bill

Break down LLM usage by app, team, or user and see exactly which use cases drive value, and which need optimization or retirement.

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Track everything, down to each prompt

Monitor latency, token usage, and model performance. Trace every agent action and tool call to debug and optimize complex autonomous workflows.

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Mirror your internal org structure

Integrate with Microsoft Entra to sync org-wide user access, set budgets and rate limits, so teams can build freely without overrunning costs.

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Built-in governance from day one

Stay audit-ready with org-wide audit logs, usage history and feedback data for every call.

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Enterprise-ready LLMOps platform

Highest standards for security & compliance

Portkey complies with stringent data privacy and security standards so that you can focus on innovation without worrying about data security.

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Audited every quarter

100% secure on-prem deployment with full control

Deploy Portkey in your private cloud for enhanced security, control, and 100% data ownership.

Trusted by Fortune 500s & Startups

Portkey is easy to set up, and the ability for developers to integrate with LLMs is great. Overall, it has significantly sped up our development process.

Patrick L,

Founder and CPO, QA.tech

With 30 million policies a month, managing over 25 GenAI use cases became a pain. Portkey helped with prompt management, tracking costs per use case, and ensuring our keys were used correctly. It gave us the visibility we needed into our AI operations.

Prateek Jogani,

CTO, Qoala

Portkey stood out among AI Gateways we evaluated for several reasons: excellent, dedicated support even during the proof of concept phase, easy-to-use APIs that reduce time spent adapting code for different models, and detailed observability features that give deep insights into traces, errors, and caching.

AI Leader,

Fortune 500 Pharma Company