Can I customize the redaction patterns?

Currently, redaction patterns are standardized and not customizable.

PortkeyUnified control plane to build, manage, and monitor AI applications

4.4 (5)

Reviewed by Daniel Nikulshyn·Updated July 2026

AI Gateway Observability LLMOps Prompt Management Analytics Enterprise API

Overview

Portkey is a unified control plane for building, managing, and monitoring AI applications. It provides a comprehensive platform for AI teams to go to production, offering features such as AI Gateway, Observability, Guardrails, Governance, and Prompt Management. The platform allows users to access over 1,600 LLMs via a unified API, streamlining the process of integrating models and enabling teams to focus on building rather than managing. Portkey also offers real-time observability, allowing users to monitor LLM behavior, catch anomalies early, and manage usage proactively. Additionally, the platform provides caching capabilities, which have been shown to save users thousands of dollars by reducing redundant tests. Portkey is designed for AI teams and supports a wide range of LLMs, with over 3,000 GenAI teams and a large number of tokens processed daily.

Key features

AI gateway with multi-provider routing
Prompt management and versioning
Request logs, traces, and analytics
Semantic caching and retries
Guardrails for input and output validation
Usage and cost monitoring dashboards

Pricing

Model: Free
Category: Observability
Rating: 4.4 / 5 (5)

Use cases

Multi-Provider LLM Routing

Route requests across OpenAI, Anthropic, and open-source models through a single unified API, with automatic fallbacks to keep applications reliable when a provider fails.

LLM Cost and Usage Monitoring

Track spend, latency, and token usage across providers and environments using dashboards to identify expensive prompts and optimize AI workload economics.

Prompt Versioning for Teams

Centrally manage and version prompts so product and engineering teams can iterate, test, and roll back changes without redeploying application code.

Guardrails and Policy Enforcement

Validate inputs and outputs with guardrails to enforce content policies, compliance rules, and quality checks across production AI applications.

Pros & Cons

Pros

Single API across 200+ LLM providers
Built-in observability and cost tracking
Guardrails and policy enforcement
Caching and fallback for reliability

Cons

Adds an extra layer to the stack
Advanced features require paid plans
Learning curve for teams new to gateways

Battle record

Across 2 battles in the Pantheon.

1st

2nd

3rd

Last 2 battles

Reviews

4.4

Average from 5 ratings.

Yuki Mori

May 12, 2026

Solid for our team

We rolled this out across the team last quarter and built-in observability and cost tracking. Guardrails for input and output validation fits neatly into how we already work, and semantic caching and retries removed a step we used to do by hand. but it has held up under daily use.

Aisha Khan

Apr 1, 2026

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on usage and cost monitoring dashboards, and caching and fallback for reliability caught me off guard. Adds an extra layer to the stack is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Devin Walker

Mar 28, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is guardrails for input and output validation — handled better than most — and caching and fallback for reliability. Adds an extra layer to the stack is my one real gripe. Worth the time if this is your use case.

Margaret Whitfield

Mar 23, 2026

Use it every day

Honestly didn't expect to like it this much. Usage and cost monitoring dashboards is exactly what I needed, and built-in observability and cost tracking. I do wish adds an extra layer to the stack, but I reach for it almost every day now and it just clicks.

Carlos Mendoza

Sep 14, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: prompt management and versioning and guardrails and policy enforcement. On balance the feature set — especially semantic caching and retries — justifies the 5 stars for our use case.

Q&A

Can I combine multiple PII redaction methods?

Yes, you can use multiple guardrails in the same config to combine pre-built PII detection with custom regex patterns.

Asked by Katarzyna Zielinska · Jun 1, 2026

Does this feature work on both input and output?

Yes, you can configure the guardrail to scan both requests and responses.

Asked by Fernando Rojas · May 26, 2026

Can I use this feature with any LLM?

Yes, the feature works with any LLM supported by Portkey.

Asked by Joanna Kowalski · Mar 28, 2026

Does this feature impact request latency?

Impact varies by guardrail provider and request complexity. Custom regex patterns with complex expressions may add minimal latency.

Asked by Nikolai Petrenko · Mar 22, 2026

How does the system handle multiple instances of the same type of PII?

Each instance receives a numbered identifier (e.g., {{EMAIL_ADDRESS_1}}, {{EMAIL_ADDRESS_2}}, etc.).

Asked by Victor Nguyen · Mar 9, 2026

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