AI quality monitoring

Synthetic monitoring for AI features

Catch silent AI regressions before your customers do.

PromptCanary runs scheduled checks against your real AI endpoint, compares the last good response to the latest bad one, alerts you with enough context to act quickly, and can block risky prompt or model changes in CI before they ship.

Create your first monitor See CI guideStart free. No SDK. No code changes. Add a deployment gate when the workflow matters.

Example failure

Support bot stopped returning structured JSON

Unhealthy

The endpoint stayed healthy. The output regressed. PromptCanary caught the change on the next scheduled run.

> monitor: support-bot-json

> schedule: hourly

> result: failed schema assertion

> alert: email sent

last passing { "intent": "refund_request", "priority": "high", "next_step": "refund_policy" }

latest failing I can help with that. Here are a few next steps you can try depending on your situation...

Check whether the response still does the job, not just whether the endpoint stayed up.

No SDK, proxy, or code changes
Works with OpenAI, Anthropic, or any HTTPS endpoint
4 one-click monitor templates
Can run as a GitHub Actions quality gate
Embeddable public status badge
Weekly digest email summaries
Start free, 2 monitors included

How it works

From zero to a working canary in under five minutes.

No infrastructure to manage. The hosted product handles the scheduler, storage, and alerting, and the first monitor can start from an opinionated one-click template instead of a blank form.

Step 1

Point it at your endpoint

Paste the URL of your AI feature — any HTTP endpoint that returns text or JSON. No SDK, no proxy, no code changes required.

Step 2

Write one objective assertion

Start with valid JSON, a schema rule, a required keyword, or a latency limit. One clear assertion is enough for the first canary.

Step 3

Get alerted or fail a rollout

PromptCanary runs on a schedule, shows the last passing response next to the latest failure, and can also fail a CI job before a risky prompt or model change ships.

Create your first monitor See pricing Read the setup guide

One-click templates

Start with a monitor shape that already matches the job.

The first monitor should feel opinionated, not empty. PromptCanary now ships four conversion-oriented templates that pre-fill prompts, endpoint hints, and assertions for common AI workflows.

OpenAI JSON Classifier

Pre-fills an OpenAI-compatible request plus `valid_json` and `json_schema` checks for structured routing flows.

Support Bot Keyword Check

Starts with required keywords, forbidden phrases, and latency so support workflows do not quietly drift.

Summariser Length Guard

Combines word-count limits with key-term checks to keep summaries concise without dropping the facts that matter.

Structured Data Extractor

Pre-fills JSON validity and required-field schema checks for extraction flows that feed downstream systems.

Try the templates See setup guide

Features

Everything you need to trust your AI endpoints.

Clear checks, clear regressions, and clear alerts. Everything on this page is there to help an operator act faster.

⏱

Scheduled Checks

Run monitors on a daily, hourly, or tighter cadence so regressions surface before a support queue fills up.

⚖

Pass / Fail Diffs

See the last passing response next to the latest failure so you can spot what changed without digging through logs.

✔

Assertion Types

Start with JSON, schema, keywords, regex, and latency. Add similarity scoring or an LLM judge rubric when you need a higher bar.

✉

Slack + Email Alerts

Route failures and recoveries to the inbox or channel your team already watches, with the failing diff included inline for faster triage.

🔗

CI Quality Gates

Trigger a monitor from GitHub Actions or another CI runner and fail the job when a prompt or model change breaks the behavior you expect.

🏷

Embeddable Status Badge

Turn a monitor into a public trust signal with an opt-in SVG badge you can drop into docs, changelogs, or customer-facing status sections.

🗓

Weekly Digest Email

Send a compact weekly summary of runs, incidents, and recoveries so the product keeps reminding teams that quality is being watched.

🧩

Webhooks & API

Invite teammates, create scoped API keys, trigger runs programmatically, and route failures to Slack, webhooks, or PagerDuty.

What Teams Actually See

Signal that reaches humans, not just dashboards.

When something drifts, the useful part is the artifact your team already lives in: the Slack message with the diff, the badge in docs, and the weekly summary that keeps trust visible even when nothing is on fire.

Slack alert

#ai-incidents

Live diff

PromptCanary10:42 AM

⚠ Support bot checkout classifier

Regression detected — 1 of 2 test cases failed quorum.

Endpoint: https://api.example.com/support-bot

Response diff (last passing → latest failing)

- "intent": "refund_request"
- "intent": "general_support"
- "priority": null

View monitor in PromptCanary

Embeddable badge

Drop into docs or a status page

Public

Replace `your-workspace` and `your-monitor` with the slugs from a monitor that has public badge sharing enabled.

Weekly digest

Inbox-ready summary

Retention loop

Weekly PromptCanary digest — Support bot checkout classifier

Current status: Healthy

Runs this week: 28

Passed runs: 27

Failed runs: 1

Failure alerts sent: 1

Recovery alerts sent: 1

Latest failing run: Apr 7, 09:14 UTC (1 failed test case)

CI quality gates

Block risky prompt and model changes before deploy.

Use PromptCanary as a release gate when the behavior matters enough that a bad rollout should stop the job, not wait for the next scheduled alert.

name: PromptCanary gate on: pull_request: jobs: quality-gate: runs-on: ubuntu-latest steps: - uses: promptcanary/action@v1 with: monitor: support-bot api-key: ${{ secrets.PROMPTCANARY_KEY }}

Read the CI guide See Team plan

Social proof

Early users on what made the difference.

The first win tends to be small and specific: one production risk, one monitor, one fast answer when output drifts.

We caught a broken refund classifier before support volume spiked. The diff made the regression obvious in under a minute.

Alex R.Staff engineer, commerce AI

Our uptime checks stayed green while the model output quietly drifted. PromptCanary was the first thing that showed us the real failure.

Priya M.Product lead, internal copilots

The first useful win was one JSON canary on a critical flow. After that, adding the rest of the team felt straightforward.

Marcus T.Platform engineer, support automation

Comparison

Built for AI monitoring, not adapted from uptime tools.

PromptCanary sits between generic uptime checks and offline eval suites: close enough to production to catch drift, simple enough to operate daily.

Capability	PromptCanary	Uptime monitors	LangSmith / evals
Checks AI output quality (not just uptime)	Yes	No	Partial
Runs on a schedule against a live endpoint	Yes	Yes	Partial
Shows last pass vs latest fail diff	Yes	No	Partial
Operator-friendly alerts with recovery status	Yes	Partial	No
Blocks risky prompt or model changes in CI	Yes	No	Partial

Common first monitors

Where PromptCanary is easiest to prove.

Start with one workflow where bad AI output would create real user pain, broken automation, or team confusion.

Structured JSON outputs

Catch schema drift before it breaks downstream tools, automations, or handoffs.

Support and triage bots

Make sure the model still classifies, routes, or escalates requests the way your workflow expects.

Summaries and extractions

Detect when responses become vague, malformed, too long, or no longer preserve the key facts.

Internal AI workflows

Monitor the prompts that matter to revenue, operations, and customer support before users notice drift.

Ready for teams

Simple to start, still usable when more people and releases depend on it.

The first win is usually one canary. After that, the product already has the pieces teams need to operationalize it across alerting, ownership, and release workflows.

Shared workspaces

Invite teammates, assign roles, and keep everyone looking at the same monitor history instead of passing screenshots around.

Scoped API access

List monitors, create monitors, trigger runs, and pull run history from your own tooling using scoped workspace keys.

Deployment gates

Use a monitor id or slug as a CI quality gate in GitHub Actions so prompt or model regressions stop the job before rollout.

Incident routing

Send failures and recoveries to email, Slack, signed webhooks, or PagerDuty depending on how your team already works.

Start free CI quality gates See pricing

Your AI feature is running. Is it still doing its job?

PromptCanary runs scheduled checks, alerts you when something changes, and can act as a CI quality gate before a risky prompt or model change ships. Start free with two monitors, no credit card required.

Start monitoring free Read the CI guide