Monitoring use cases
Monitor the work behind your product.
Telesis helps small engineering teams connect a failing service, the evidence behind it, and the people who need to know. These example workflows show where to start.
Keep a SaaS product reachable
For a technical founder, the important question is whether customers can reach the product and complete its essential actions. Start with the public site and API; use response assertions and timing breakdowns to distinguish a failed request from a slow one.
Add a Playwright journey for a critical browser flow, such as signing in with a dedicated test account. Cloud execution needs a configured runner. Review recording settings before testing private pages.
Route a failed check to an alert, investigate its stored result, and publish an incident update on the services selected for your public status page.
Catch jobs that stop running
A daily export or scheduled synchronization can fail while the website stays online. Register the existing job as a cron monitor, set its schedule and grace period, and report completion to its private check-in URL.
Telesis tracks expected check-ins. Add start and fail events when you need hung-run detection or an explicit failure signal. Your job keeps running in your own environment; the check-in reports its lifecycle.
Run history gives the developer responsible for the job a place to see what happened and configure the notification that should follow.
Set up cron monitoringReview endpoint changes alongside uptime
For a team releasing a web service, availability and endpoint security are related operational questions. A public security scan collects TLS, certificate, header, DNS, and exposed technology evidence, then organizes findings for investigation.
Recurring checks make it possible to review findings over time and alert on configured security conditions. Treat a finding as evidence to verify and remediate. Endpoint scanning does not inspect all of your cloud permissions or establish compliance.
Explore endpoint security monitoringTest an AI provider with a real request
For a product that depends on an LLM API, a provider status page cannot tell you whether your configured model accepts a request. An inference check sends a prompt to the selected provider and evaluates the response, with latency and token-usage evidence.
Choose a small non-sensitive test prompt, configure the provider credentials and model, and set a cadence that matches the cost you accept. These requests consume provider credits. Public quick checks do not execute credentialed inference.
Explore LLM inference monitoringKeep monitoring in your engineering workflow
Use the console for investigation, the CLI for a one-off check, the API for integrations, and Terraform for repeatable configuration. MCP tools let an assistant work with supported Telesis operations using the access you configure.
Select the right organization and review execution capabilities before creating monitors or running checks. Feature availability depends on the plan, credentials, and runtime configuration.