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 automatically with exponential backoff, and logs every run.
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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 failed attempts with exponential backoff, and records each attempt's status, code, duration, size, and error summary. Your application persists model outputs and token costs.
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 final attempt fails. 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 token cost, and returns 200. Crontap records run metadata and any retry attempts; the application's own data store remains the source of truth for model output.
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 attempts record the status code, duration, size, and error summary. Automatic retries with exponential backoff run before Slack, Discord, or email alerts fire.
- 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.
Related guides
- Introducing AI Integrations: transform any response, forward it anywhere
- Turn a raw API response into a plain-English Slack digest
- Build a 24/7 AI brand monitor with Crontap + Replicate Llama
- Schedule n8n workflows externally via webhook
- Is OpenAI down? Live status check
- Is Anthropic down? Live status check
Adjacent use cases
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