SDKs
Express, Python, Go, and Spring services emit spans, metrics, logs, and app identity.
AI-assisted operations for production systems
Telemetry review for teams operating real software.
Upblit unifies logs, traces, metrics, API keys, and runbook intelligence into a dark-first workspace designed for incident response and production clarity.
p95 latency
128ms-18%
error rate
0.12%stable
trace volume
2.4M+31%
Built from the backend upSDK-first telemetryScoped API keysTenant-aware AI docs
Incident cockpit
acme-production / checkout
streaming
Signal health15m
Logs86%
Traces72%
Metrics94%
Docs61%
AI runbook match
Payment timeout resembles incident INC-042. Check queue depth, gateway retry volume, and worker pool saturation.
Trace timelineERROR spike
12:04:11.204checkout-api
POST /orders accepted
12:04:11.391payment-worker
authorization latency p95 crossed
12:04:11.883gateway
retry policy activated
12:04:12.102ai-docs
runbook matched: queue pressure
12:04:12.560checkout-api
payment timeout threshold exceeded
Product architecture
A clean signal path from runtime to review.
Upblit is organized around the production hierarchy your team already uses: organizations, projects, applications, API keys, ingest, and incident review.
API keys
Scoped application keys keep ingest boundaries clean across environments.
Ingest pipeline
Logs and telemetry are normalized with project, org, trace, and application context.
Review cockpit
Engineers inspect timelines, runbooks, and correlated signals from one surface.
Feature system
Composed surfaces, not a wall of cards.
Every section is arranged around the actual operational objects in Upblit: logs, traces, metrics, documents, applications, and incident review.
Logs
Searchable production logs with trace context.
Log review stays connected to the service, project, severity, and request path that produced it. The interface is intentionally dense so responders can scan quickly under pressure.
Level-aware filtering
Trace ID correlation
Auto-refresh ready
Structured payloads
logs surface
livetrace_00a9491ms
trace_01a9182ms
trace_02a9491ms
trace_03a9182ms
trace_04a9491ms
Traces
Request waterfalls that explain latency.
Telemetry rows expand into span timelines with parent-child relationships, response status, method, URL, and duration.
Span hierarchy
Duration hotspots
Status mapping
Incident timeline
traces surface
livetrace_10a9491ms
trace_11a9182ms
trace_12a9491ms
trace_13a9182ms
trace_14a9491ms
AI Docs
Runbooks surfaced beside the incident.
Upload operational docs and keep retrieval scoped to the tenant. Upblit AI becomes a focused assistant for engineering context, not a disconnected chatbot.
PDF/DOCX/TXT
Tenant-aware search
Runbook summaries
Document deletion
ai docs surface
livetrace_20a9491ms
trace_21a9182ms
trace_22a9491ms
trace_23a9182ms
trace_24a9491ms
Dashboard preview
A production cockpit with real information density.
Review service health, trace waterfalls, matching docs, and incident context from the same visual plane. It is built to be scanned, not admired from a distance.
Incident cockpit
acme-production / checkout
streaming
Signal health15m
Logs86%
Traces72%
Metrics94%
Docs61%
AI runbook match
Payment timeout resembles incident INC-042. Check queue depth, gateway retry volume, and worker pool saturation.
Trace timelineERROR spike
12:04:11.204checkout-api
POST /orders accepted
12:04:11.391payment-worker
authorization latency p95 crossed
12:04:11.883gateway
retry policy activated
12:04:12.102ai-docs
runbook matched: queue pressure
12:04:12.560checkout-api
payment timeout threshold exceeded
sdk-ingest.ts
1import { upblit } from '@upblit/sdk'
2const span = upblit.trace('checkout.create')
3logger.warn('payment latency high', { traceId })
4await upblit.metric('queue.depth', depth)
5await span.end({ status: 'retry_scheduled' })
Developer experience
Simple ingest. Clear ownership. Useful defaults.
SDKs and API routes map directly to teams, projects, applications, and telemetry. The product avoids mystery abstractions so production context remains inspectable.
Typed client helpers and clear API boundaries.
Organization, project, and application hierarchy.
Auto-refresh patterns for high-pressure incident review.
Docs and runbooks linked directly into telemetry analysis.
Engineering controls
Practical boundaries for a student-built observability system.
Upblit focuses on the controls that matter while the product is still engineering-led: auth, scoped keys, tenant boundaries, explicit deletion, and telemetry shapes that are easy to inspect.
GitHub OAuth sign-in
Scoped application API keys
Organization/project boundaries
Trace-aware log context
Document tenant separation
Explicit delete paths
Backend-owned token refresh
Audit-friendly event shape
Built for engineers first
A serious observability workbench for people who read logs.
Start with GitHub OAuth, then move into projects, applications, API keys, telemetry, logs, and AI-assisted incident context. The product should feel useful before it tries to sound important.