
Datadog
Langfuse
Sentry.io
AgentShield.one
CtrlAI
SignalVault.io
Monitor every API call your AI Agent makes. Guardrly provides real-time alerts, visual audit logs, and PII scrubbing to prevent rogue actions from banning your Meta Ads or Shopify accounts. Know what your agent did before it's too late.
Application and Data, Libraries, and JavaScript Framework Components

Which is more popular?
Based on our record, Ember-cli seems to be more popular. It has been mentioned 1 time since March 2021.
Website, pricing, platforms and company facts side by side.
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Ember-cli
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| Website | guardrly.com | cli.emberjs.com |
| Pricing | — | |
| Listed in |
In their own words, as submitted to SaaSHub.

The Safety Net for Your Autonomous AI Agents As AI Agents (like Claude Code, Cursor, or custom-built solutions) become more capable, developers are giving them access to critical external APIs—like Shopify, Meta Ads, and Stripe. But what happens when an agent hallucinates? A rogue AI can easily...
No description of Ember-cli yet.
What each product offers, as listed by its team.

No features have been listed yet.
Possible disadvantages
An editorial look at what each product does well and who it suits.

Overall verdict
Why this product is good
Recommended for
Overall verdict
Why this product is good
Recommended for
How often each product is chosen within a category, 0–100% relative to the other.

Share your experience with using Guardrly and Ember-cli. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.

Tracking Guardrly since Apr 2026.
The webpage is LinkedIn.com. While this isn’t a framework, I know that are using https://cli.emberjs.com/release/. Source: about 5 years ago
When comparing Guardrly and Ember-cli, you can also consider the following products.

See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.
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Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.
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From error tracking to performance monitoring, developers can see what actually matters, solve quicker, and learn continuously about their applications - from the frontend to the backend.
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Cost observability for AI agents in production
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Transparent proxy that secures AI agents with guardrails
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The trust layer for AI applications.
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