Monitor AI agents
like production systems.

AgentPing shows what your AI agents cost, whether they ran, and when output starts getting worse, before mystery bills, silent failures or bad answers become customer problems.

Track your first AI workflow

Free plan, no card required Works with any AI workflow Live in minutes

app.agentping.io/overview EU · live

$5,382

spend, mtd

$0.094

cost / good run

11/12

agents healthy

3.8/5

avg quality

open incidents

Spend · cost by agent↑ 8% wow

content-writer$2,189

research-agent$1,474

support-triage$685

email-classifier$262

Spend · by customermtd

acme-corp $2,427
zenith-labs $1,332
globex $861
unattributed $767

Pulse · support-triagelive

missed its 14:00 run paged on-call · 247 runs clean before

Verify · summariser↓ 4.2 to 3.8

cites a source pass
stays on policy fail

Recent runslast 15 min

Agent	Status	Cost	Quality	When
content-writer	ok	$0.061	4.4	12s ago
support-triage	missed	—	—	1m ago
research-agent	ok	$0.214	3.9	2m ago
email-classifier	ok	$0.003	4.7	3m ago

Guardarmed

$50 daily cap · 40 step ceiling. Blocked 1 runaway loop today.

Alerts↑ 3.8×

content-writer cost 3.8× its normal. Paged to #ai-ops 4m ago.

AI agents have moved from demos to production.

AI agents now serve customers, enrich data, write summaries, trigger actions, run scheduled workflows and power product features. That means every agent run is no longer just a technical event. It is a cost, a reliability risk and sometimes a customer experience. The tools you already have were not built to connect agent runs to cost, missed runs, retries, loops, customers, features and output quality.

Can you answer these questions?

Which AI agent cost the most this week?
Which customer or feature is driving token spend?
Did every scheduled AI workflow run today?
Did any agent fail, retry, loop or go silent?
Did a prompt change increase cost per run?
Are successful runs still producing good output?
Would you know if one workflow stopped working tomorrow?

If not, you are running production AI without production visibility.

The dangerous part is not the bill. It is not knowing what caused it.

Your AI provider bill increases by 42%. You can see total spend in OpenAI or Anthropic, but not which agent, customer, feature, model, prompt change or retry loop caused it. AgentPing connects spend back to the production run that created it.

Explore Spend →

Monitor the three ways AI agents create production risk.

Spend

Know which agent caused the bill.

→Track cost by agent, customer, feature, provider, model and run. Spot cost spikes before they become margin problems.

Explore Spend →

Pulse

Know whether your agents actually ran.

→Get alerted when scheduled workflows fail, stall, miss a run or go silent.

Explore Pulse →

Verify

Know when successful runs are no longer good enough.

→Track quality signals, evals and drift so "200 OK" does not hide bad answers.

Explore Verify →

cost of monitoring

Less than the variance on your LLM bill.

AgentPing typically costs 1 to 3% of what customers are already paying OpenAI and Anthropic.

001Starter

~3.3%

$99/mo, for teams typically spending around $3,000/mo on LLM tokens.

002Team

~1.3%

$199/mo, for teams typically spending around $15,000/mo on LLM tokens.

003Business

~1.1%

$399/mo, for teams typically spending around $37,500/mo on LLM tokens.

The first cost spike AgentPing catches pays for the next year of subscription. Often the next several.

Calculate it for your team →

Your current tools only show part of the picture.

Current tool	What it shows	What is missing
OpenAI or Anthropic dashboards	Total usage and spend	Cost by agent, customer, feature or run
Logs and Sentry	Errors and exceptions	Missed runs, AI cost context and quality signals
Cron monitors	Whether a scheduled job fired	What the AI run cost, produced or retried
Spreadsheets	Manual cost tracking	Real time alerts and run level visibility

AgentPing sits beside your existing stack and gives production AI workflows the context traditional tools were not built for.

See every AI run in context.

AgentPing captures the operational details around each AI workflow: agent, customer, feature, model, tokens, cost, status, latency, alerts and optional quality signals.

run · nightly-report-agent success

Workflow	weekly-digest
Customer	Northwind
Feature	exec-summary
Provider	anthropic
Model	claude-opus-4-8
Tokens	21,904
Cost	$0.21
Latency	38.6s
Retries	0
Alert state	none
Quality signal	4.6 / 5

Start with one agent.

No platform rollout. Track one workflow, see what it cost and whether it behaved, and decide from there.

Create a project

One account, your first project, in under a minute.

Add one agent

Name the agent or workflow you want to watch first.

Send a run event

By SDK or a single webhook. Two lines of code or one HTTP request.

See the run

Cost, status, model and latency, live in the dashboard.

Add an alert

For cost spikes or missed runs, to a channel you watch.

Track your first AI workflow →

Built for teams putting AI agents into production.

[I]

SaaS teams

Track AI cost by customer, feature and run before margins disappear.

AgentPing for SaaS teams →

[II]

Automation agencies

Monitor client AI workflows after handover and know when they fail silently.

AgentPing for agencies →

[III]

Product teams

Track quality signals so successful runs do not hide worse answers.

AgentPing for product teams →

By industry Customer support Sales & RevOps E-commerce & retail Legal & professional services Platform engineering

Works with the stack your agents already use.

Send events from LangChain, the OpenAI Agents SDK, the Vercel AI SDK, n8n, Laravel, GitHub Actions, scheduled jobs or any workflow that can call a webhook.

View integrations →

Monitor production AI without exposing more than you need to.

Start with metadata such as agent name, run status, model, token usage, cost, latency and your own customer or feature identifiers. Sensitive prompts and outputs are optional, configurable and clearly explained, so you can get cost and reliability visibility without sending content you would rather keep.

Read about data and security → Your data →

Frequently asked questions

What is AI agent observability?

It is monitoring AI agents the way you monitor production systems: what each agent run costs, whether scheduled runs actually fired, and whether output quality is holding up. AgentPing puts cost attribution, uptime and quality scoring on one event, the agent run, rather than scattering them across separate tools.

How is AgentPing different from Sentry or Datadog?

Sentry and Datadog watch infrastructure and exceptions; they do not know what an agent run cost, whether a scheduled agent missed its window, or whether a 200 OK hid a bad answer. AgentPing is built around the agent run, so cost per customer, schedule freshness and output quality roll up to the agent, not the host.

How does the price compare to my LLM bill?

Most teams find AgentPing costs 1 to 3% of their monthly LLM spend. A team spending $15,000 a month typically lands on the Team plan at $199, about 1.3% of the bill. The first cost spike it catches usually pays for the next year of subscription.

Will it slow my agents down?

No. The SDK never blocks your agent. Telemetry runs on a separate thread with a hard 2-second timeout, a bounded local queue, and graceful degradation when our service is unreachable. If we go down, your agents run as if we weren't installed.

What's the deployment model?

Fully hosted SaaS. You pick the ingestion region: US or EU.

Can I export my data?

Yes. Full event export to JSON or Parquet. API access on every tier. We don't lock your data in.

Start monitoring your first production AI workflow.

Pick one agent, send a run, and see what it cost and whether it behaved normally. Free to start, live in minutes.

Track your first AI workflow See all features

Free to start. No card. 14-day trial on paid plans. The SDK never blocks your agents, and never crashes them.

Monitor AI agents like production systems.