Why We Built Traxo: Monitoring for AI-Powered Applications

If you are building with AI right now, chances are you have a billing page open in one tab, a provider dashboard in another, and a growing sense of unease about what your LLM spend will look like at the end of the month. You are not alone. We talked to dozens of engineering teams before writing a single line of code, and the story was remarkably consistent: they were spending thousands of dollars on AI APIs with almost no visibility into where that money was going.

That is why we built Traxo -- a monitoring platform designed from the ground up for applications that run on both traditional infrastructure and AI models.

The blind spot in modern monitoring

Traditional uptime monitoring tools are excellent at answering a narrow question: is this HTTP endpoint returning a 200? They can check your SSL certificates, verify DNS resolution, and ping your servers from multiple locations. What they cannot do is tell you that your OpenAI spend tripled because someone changed a system prompt last Thursday. They cannot detect that 40% of your Anthropic calls are duplicate prompts hitting the same cache-miss path. They have no concept of token budgets, model routing, or prompt regression.

AI workloads behave differently from traditional infrastructure. Costs scale with input complexity, not just traffic volume. A single prompt change can double your bill without triggering a single alert in your existing monitoring stack. Model providers deprecate endpoints, change rate limits, and shift pricing -- often with minimal notice. The tools that worked for stateless HTTP servers do not cover this.

What Traxo actually does

Traxo combines AI/LLM monitoring and traditional uptime monitoring in a single platform. On the AI side, our @traxodev/ai SDK wraps calls to OpenAI, Anthropic, Google Gemini, Mistral, Cohere, and five other providers with zero dependencies. It captures token usage, latency, cost, model metadata, and error rates, then forwards everything to your Traxo dashboard in real time. No code rewrites -- you swap one import and your existing calls are instrumented.

On top of raw telemetry, our anti-pattern detection engine runs hourly against your AI traffic. It flags six categories of problems automatically: context stuffing (prompts growing unbounded), model overspend (using GPT-4 for tasks Haiku handles fine), excessive retries, prompt regression, latency degradation, and token waste from duplicate requests. Each finding includes severity, affected endpoints, and enough context to act on immediately.

On the infrastructure side, Traxo monitors HTTP endpoints, TCP ports, SSL certificates, DNS records, heartbeat cron jobs, and full browser page loads across eight global regions -- US East, US West, EU West, EU Central, Asia Southeast, Asia Northeast, South America East, and Asia South. Multi-region consensus means a network blip in one region does not page you at 3 AM. All regions must agree a service is down before an incident fires.

One dashboard, not six

Before Traxo, the teams we talked to were stitching together four to six tools: a provider cost dashboard, an APM for latency, a custom script for token tracking, an uptime checker for endpoints, and a spreadsheet for budget projections. Each tool had its own alert threshold, its own retention window, and its own login. When something went wrong, the first 10 minutes were spent figuring out which dashboard to check.

Traxo consolidates everything into a single real-time dashboard powered by Server-Sent Events. Monitor status changes, cost spikes, and anti-pattern detections all surface in the same view. Alerts route through the same pipeline -- Email, Slack, Webhooks, PagerDuty, or SMS -- so your on-call workflow does not change. You set cost budget thresholds that fire every five minutes, not every billing cycle. And the whole team shares one source of truth with role-based access and shared incident timelines.

What makes Traxo different

There are other monitoring tools, and some of them are very good at their niche. Here is where Traxo stands apart:

• AI-native, not bolt-on -- AI monitoring is not a plugin or add-on. It is built into the core data model, the alerting pipeline, and the analysis engine.
• 10+ LLM providers -- OpenAI, Anthropic, Google, Mistral, Cohere, and more. One SDK covers them all.
• 6 anti-pattern detectors -- Automated hourly analysis catches cost and performance issues before they compound.
• 8 global monitoring regions -- Multi-region consensus eliminates false positives for traditional uptime checks.
• Zero-dependency SDK -- The @traxodev/ai package adds no transitive dependencies to your project. It wraps your existing provider clients with minimal overhead.
• Real-time by default -- SSE-powered dashboard updates arrive within milliseconds, not on a polling interval.

Free to start, built to scale

The free tier includes five monitors, 1,000 AI events per day, email alerts, and seven days of data retention. No credit card required. It is enough to instrument your first AI feature and get cost visibility within minutes of signing up.

When you need more, Pro starts at $29/month with 50 monitors, 25K AI events per day, 30-day retention, and three monitoring regions. Business and Enterprise tiers scale to hundreds of monitors, 250K+ daily AI events, all eight regions, and up to 365 days of retention.

Where we are headed

Our goal is straightforward: Traxo should be the default monitoring layer for AI-powered applications. Not a replacement for your APM or your log aggregator -- a purpose-built layer that understands both the AI and infrastructure sides of your stack and gives you a single place to watch both.

We ship features based on what our users actually need. The support page and the API docs are good places to start if you want to dig deeper. And if you have five minutes, the fastest way to see what Traxo can do is to create a free account and instrument your first AI call.