Use case
Schedule AI jobs and recurring LLM calls
Run AI agents on a cron without managing a background worker. Crontap triggers OpenAI, Anthropic, Replicate, Hugging Face or your own model on a schedule, retries failures and logs every run, so your nightly summarizer, weekly brand monitor or hourly embedding job just keeps going.
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The problem
Why this is painful without the right tool
- LLM APIs are rate-limited and flaky; a single cold minute shouldn't wipe out a whole day of data collection.
- Running cron jobs inside your own app means keeping a dyno or container alive 24/7 just for a task that takes 30 seconds.
- Serverless cron (Vercel, Lambda) caps execution time and makes it awkward to retry long LLM calls.
- Most schedulers have no good way to capture structured output, log token usage or alert you when a prompt stops working.
The fix
How Crontap solves it
With Crontap you point a schedule at your AI endpoint (Replicate, an OpenAI-compatible server, a Hugging Face Inference API, or your own wrapper) and pick a cadence. Crontap sends the request with your custom headers and JSON payload, retries on failure and stores every response in the history so you can audit outputs and token costs later.
0 */4 * * *A typical setup looks like this: build a small endpoint in your app (or a Replicate model) that takes a payload, runs the model and writes the result somewhere. Crontap hits it on a cadence, sends along auth headers and any prompt variables, and fires a Slack, Discord or email notification if the run fails a few times in a row. No queue, no worker, no cron file on a server you forgot about.
Full walkthrough with Replicate and Llama 3.1 in our 24/7 AI brand monitor guide.
One concrete pattern from the dataset, archetyped: a feedback SaaS running OpenAI on a classic ASP backend, firing a sentiment-classification call every 4 minutes against /api/openai/sentiment. The schedule is the alarm clock; the ASP backend reads the queue, calls OpenAI, persists the classification, and returns 200. Crontap captures the response body for every run, so a sudden shift in the model's outputs (a prompt that started returning empty, a model that started rejecting the input) is visible in the run history before the customer-facing dashboard ages out.
FAQ
Common questions
- Can Crontap call the OpenAI or Anthropic API directly?
- Yes. Any HTTP endpoint works. Point the schedule at https://api.openai.com/v1/chat/completions (or any other model provider), set the Authorization header to your API key and put the prompt in the JSON payload. Crontap will call it on the schedule you configure.
- What happens if the model is slow or times out?
- Crontap enforces a per-request timeout to protect you from runaway calls. Failed runs are logged with status code, duration and response body, and you can wire up retries plus Slack/Discord/email alerts via the Integrations panel per schedule.
- How do I handle rate limits from model providers?
- Stagger your schedules (e.g. every 10 minutes instead of every minute), keep an eye on logs, and use the failure integrations to get paged if you start hitting 429s. For heavier workloads, wrap the provider in your own endpoint that adds jitter and backoff, then have Crontap trigger it so your endpoint handles the burst.
Ready to fix it?
Point Crontap at any URL. Pick any cron. Done.
WordPress, Shopify, Railway, Cloud Run, Vercel, HubSpot, Ghost, your own box. If it answers HTTP, Crontap can drive it on a clock you can read, in the timezone that actually matters, and page you when something breaks.
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/wp-cron.php?doing_wp_cron=1
Schedule
"every 5 minutes"
Next
in 23s