Now in public beta

You shipped AI.
Now you're flying blind.

In production, AI doesn't fail loudly. It fails expensively โ€” through silent loops, prompt changes, and features that look fine but quietly burn margin.

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Efficiency Score
90Excellent
Reliability
93%
Speed
2.0s
Efficiency
100%
Scaling Health
Efficient
+275%
Usage Growth
-50.9%
Cost Change
+326%
Net Efficiency
$0.0031
Avg Cost/Request
2.4M
Total Tokens

The moment every AI team hits

The Problem
#alerts
!
OpenAI Alert9:14 AM

Usage spike detected: $2,847 in the last 24h (+312%)

๐Ÿ’ฐ
Sarah (Finance)9:22 AM

Why is our AI bill 3x higher this month? We need answers before the board meeting.

๐Ÿ‘จโ€๐Ÿ’ป
Mike (Engineering)9:31 AM

Checked the logs. We made 847k API calls yesterday, mostly to gpt-4o. But the logs don't tie back to features โ€” I'd have to dig through code paths manually.

๐Ÿ“Š
Priya (Product)9:45 AM

Was it the new summarization feature? The chat widget? A looping workflow? I genuinely don't know.

No one has the answer...
The Reality โ€” Agentic AI Workflow
Agent workflow running...
Customer:Acme Corp
User Request
"Analyze Q4 Report"
doc-parser
gpt-4o1 call
$0.12
!
deep-analysis
claude-3.54 calls
$1.35
3 retries$0.90 wasted
summarizer
gpt-4o-mini1 call
$0.08
Task Cost
$1.55$0.65
138% over budget
Bill to Customer
$???
No task attribution

Orbit ties every call to a task. Every task to a customer.

So you always have the answer.

Here's how we solve it

Feature-Level Analytics

Stop losing money
in the dark

Vendor dashboards show totals. Logs show requests. Neither tells you which feature is burning margin or why.

Orbit ties every LLM call to a product feature โ€” and soon, to entire workflows โ€” so you can see what is expensive, slow, or failing in production.

Per-feature cost โ€” Know exactly what each AI feature costs
Per-feature latency โ€” Track response times for every feature
Per-feature errors โ€” Success rates and error patterns broken down
Features
Feature-level analytics
Last 30 days
Total Features
7
Total Cost
$0.0074
Total Requests
34
Avg Latency
1254ms
Features
7 features tracked
code-generator
โ†— 100%
$0.0037
8Req
12.5%Err
1067ms
chat-assistant
โ†— 100%
$0.0037
8Req
12.5%Err
1849ms
content-writer
โ†— 100%
$0.0000
2Req
0.0%Err
1122ms
document-summarizer
โ†— 100%
$0.0000
2Req
0.0%Err
1121ms
error-test-model
โ†— 100%
$0.0000
6Req
50.0%Err
1163ms
error-test-valid
โ†— 100%
$0.0000
6Req
50.0%Err
1162ms
Cost TrendLast 30 days
Dec 10Dec 25Jan 10
Scaling Health
Efficient
+275%
Usage Growth
-50.9%
Cost Change
Net Efficiency
+325.9%
Avg Cost / Request
$0.0002
Total Tokens
1.2k
Scaling Health

Catch margin collapse
before it happens

AI spend rarely grows linearly. One prompt tweak or model swap can turn a healthy feature into a loss overnight โ€” and as systems become more workflow- and agent-driven, cost can explode in minutes.

Orbit tracks traffic vs. spend in real time and warns you the moment your unit economics break.

Usage vs. cost correlation โ€” Is growth actually profitable?
Net efficiency โ€” One number that tells you if AI is scaling safely
Cost trend analysis โ€” See spend drift before finance does
Error visibility

Debug failures
before users notice

See error rates by feature and model. Understand which parts of your product are breaking and why โ€” with detailed error logs and failure reasons.

Track error trends over time. Catch regressions early. Know exactly which model and feature combination is causing issues.

Error rate by feature โ€” See which features are failing
Error type breakdown โ€” Invalid models, rate limits, timeouts
Recent error logs โ€” Full context for debugging
Total Errors
47
Error Rate
2.8%
Success Rate
97.2%
Affected
3 features
By Type
model_not_found24
rate_limit_exceeded15
invalid_request8
Recent Error
model_not_found
Feature: code-generator
Model 'gpt-5' does not exist
Privacy & Security

Built for correctness

Orbit shows you what's actually happening in your application โ€” without proxies, scraping, or hidden assumptions.

SDK-based collection

Metrics are captured directly from your application runtime. No external monitoring or traffic interception.

No request interception

Orbit never sits between your app and your AI provider. Your requests go directly to OpenAI, Anthropic, etc.

Deterministic metrics

Cost, latency, and error rates are calculated from real request data โ€” not estimates or statistical sampling.

No API key access

Your provider API keys stay in your application. Orbit only receives usage metadata, never credentials.

Why Orbit

Vendor dashboards show API usage.
Orbit shows how your product uses AI.

Provider dashboards show total API calls. They don't show which feature failed, why, or what it cost you.

Capability
Providers
Orbit
View AI usage by model
View total spend
Feature-level attribution
โ€”
Feature-level latency & errors
โ€”
Multi-provider unified view
โ€”
Track multi-step AI workflows
โ€”
Unit economics (traffic vs. spend)
โ€”
Account-level efficiency score
โ€”
SDK-based runtime data
โ€”
Integration

Get started in minutes

One npm package. Wrap your AI client. See your data instantly.

Supported: OpenAI, Anthropic, Gemini|Coming soon: Mistral, Groq, DeepSeek

01

Install the SDK

npm install @with-orbit/sdk

02

Wrap your client

One line to instrument OpenAI

03

See your data

Real-time metrics in your dashboard

app.ts
import { Orbit } from '@with-orbit/sdk';
import OpenAI from 'openai';

// Initialize Orbit
const orbit = new Orbit({
  apiKey: process.env.ORBIT_API_KEY
})

// Wrap your OpenAI client
const openai = orbit.wrapOpenAI(new OpenAI(), {
  feature: 'chat-assistant'
})

Don't wait for your first
AI bill shock.

See what your AI is really costing you in production โ€” feature by feature, workflow by workflow.

Expose Hidden AI Spend