Unified LLM telemetry
Capture every request across OpenAI, Anthropic, local models, and tooling.
Trace prompts, responses, tokens, latency, and status without rewiring your app.
Neural observability for AI systems
See what your LLMs are doing
in production. In real time.
Quaneuron is a neural observability platform for AI applications. Ingest every request, track every token, and surface issues before your users do with live metrics, traces, and alerts.
Built for engineers shipping AI features in the real world.
No more blind spots.
Live traffic · 3 providers
Edge ingest · Supabase
Requests / min
1,284
+23% vs last hour
Average latency
862 ms
p95: 1.4 s · green
Token spend (today)
$214.37
-12% with routing
Hallucination alerts
3 open
Slack · #ai-incidents
Live traces
Streaming
search_assistant / user: 38492
742 ms
report_generator / batch-job
1.2 s
policy_check · hallucination flag
Alert
tool_call / vector-search
418 ms
Why Quaneuron
Everything you need to keep AI features stable, fast, and sane.
Quaneuron plugs into your existing stack with a light SDK and edge functions.
It turns raw model calls into structured telemetry so you can see performance,
cost, and failures in one place.
Real-time metrics and dashboards
Watch traffic, error rates, token spend, and latency in real time.
Slice by provider, model, route, team, or environment with one click.
Alerts that actually matter
Define thresholds for cost spikes, error bursts, or hallucination flags.
Pipe alerts into Slack, email, or incident channels so the right people see them fast.
Production-safe tracing
Drill from high-level metrics into a single problematic request.
Inspect prompt, context, tools, and response side by side to debug issues in minutes.
Cost and provider optimization
Compare providers on real workloads, not benchmarks.
See which models give the best results per dollar and ship routing rules with confidence.
Drop-in SDKs and webhooks
Start with a 3–5 line integration. Use JS or Python SDKs, or push logs from your
own middleware using signed webhooks and simple JSON.
How it works
Instrument once. See everything.
Quaneuron wraps your existing LLM calls instead of forcing you into a new client.
All events stream into a Supabase-backed data plane with edge functions for ingest,
metrics, and alert dispatch.
1
Drop in the SDK
Add a small wrapper around your LLM client or middleware. Configure your project key
and environment. Quaneuron starts recording structured events instantly.
2
Stream to Quaneuron ingest
Events hit a secure edge endpoint where they are validated, normalized, and written
to a Postgres store. Tokens, timing, cost, and tags are recorded for analysis.
3
Metrics, traces, and alerts in one console
The Quaneuron dashboard shows live traffic and lets you zoom into traces.
Background jobs compute rolling windows, budgets, and incident triggers and
push alerts out to your team tools.
JavaScript · quick start
// install
// npm install @quaneuron/js
import { withQuaneuron } from "@quaneuron/js";
import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.OPENAI_KEY });
const wrapped = withQuaneuron({
client,
projectKey: process.env.QUANEURON_PROJECT_KEY,
environment: "production",
});
const result = await wrapped.chat.completions.create({
model: "gpt-4.1-mini",
messages: [{ role: "user", content: "Summarize this ticket." }],
metadata: {
route: "support_summarizer",
userId: "u_38492",
},
});
// Quaneuron captures:
// - model, tokens, cost
// - latency, status, retries
// - route, user, environment
// - response quality flags
Use cases
Built for teams shipping AI features, not just research demos.
Watch AI features like any other critical service.
See when a release silently doubles latency, breaks prompts, or explodes your spend.
Tie incidents back to routes and versions so you can roll forward safely.
Run multi-provider, multi-model setups without guesswork.
Compare providers on real workloads, load-balance intelligently, and keep an eye
on budgets across teams and environments.
Track quality signals and risk over time.
Flag hallucinations, policy violations, and high-risk outputs and follow them
back to specific prompts, contexts, and tools.
Pricing
Start free, grow with your traffic.
Quaneuron launches with a generous free tier for builders and early teams,
with simple usage-based pricing once you graduate from prototype to production.
No seat tax, no long-term contracts.
Planned tiers include:
Starter · for solo builders
Team · shared dashboards & alerts
Scale · SSO, audit logs, custom retention
Enterprise · dedicated region & support
FAQ
Questions you might already be asking.
What kinds of AI systems can Quaneuron monitor?
Quaneuron is designed for LLM-powered applications: chat interfaces, copilots,
RAG systems, agents, and background jobs. If you are sending prompts to models
and care about reliability, Quaneuron can help.
Which providers do you support?
The first release focuses on OpenAI, Anthropic, and common open-source model
gateways, with room to add more based on demand. You can also send custom events
from your own middleware via HTTP.
Do I need to replace my existing LLM client?
No. The SDK wraps your existing client or sits in your middleware layer.
If you would rather not use the SDK at all, you can push events directly to
the ingest endpoint.
How is my data stored and secured?
Events are stored in a Postgres database with row-level security and strict
access control. You can choose retention windows and limit which fields are
persisted so that sensitive content never leaves your stack.
Get on the early access list for Quaneuron.
If you are shipping or maintaining AI features and want better visibility
into how they behave in the wild, Quaneuron is being built for you.
Share a bit about your stack and we will reach out as we open the first
wave of projects.