The verdict: can you vibe code Honeycomb?
Build a simple log-search dashboard instead, because replicating Honeycomb's custom columnar storage engine and distributed trace analytics pipeline is a massive systems engineering feat.
Honeycomb's core value is not its web UI—it is a specialized, ultra-fast distributed columnar database built from scratch to query billions of wide, schema-less JSON log lines and trace spans in milliseconds without pre-aggregation. An AI coding agent can scaffold a Next.js frontend with Tailwind and a basic Postgres or SQLite table for logs, but the moment you feed it real-world OTel workloads with high-cardinality custom dimensions, standard SQL databases will buckle instantly. If you need observability, use existing open-source tools like ClickHouse or Grafana; if you want to vibecode, build a much simpler SaaS.
Estimated effort: 6+ months of full-time systems engineering work
What you can't replicate
- Proprietary high-cardinality columnar storage architecture
- Sub-10 second distributed trace query slicing over billions of events
- Enterprise-grade sampling proxy (Refinery) for telemetry traffic management
Founded
2016
Raised
$150M
Team
186-300
Cheapest paid tier
$150/mo
What Honeycomb does
Cloud-based observability and APM platform designed for distributed tracing, high-cardinality log analytics, and AI-driven debugging.
Core features
- OpenTelemetry ingestion endpoint (OTLP gRPC/HTTP)
- High-cardinality columnar query engine
- Distributed trace visualization and waterfall graphs
- Time-series metrics aggregation
- BubbleUp anomaly comparison tool
- Service Level Objectives (SLOs) tracking
- AI Copilot / MCP query assistant
The business
Pricing
- FreeFree
- Pro$150/mo
- EnterpriseCustom
Funding
$150M from Insight Partners, Scale Venture Partners, Headline, Storm Ventures, Industry Ventures, NextWorld Capital, Uncorrelated Ventures
Pay vs build, cumulative
Break-even at month 1 — after that, every month is money kept.
The hard parts of vibe coding Honeycomb
- Building a custom high-cardinality columnar storage engine capable of sub-second arbitrary slicing across millions of unindexed JSON fields
- Handling erratic telemetry traffic spikes without dropping incoming spans or choking ingestion nodes
- Rendering fluid heatmaps and interactive trace graphs with thousands of nodes smoothly in the browser
- Implementing OpenTelemetry gRPC and HTTP protocol parsers for zero-friction tracing collection
How to vibecode Honeycomb
Prerequisites
Node.jsfree
Required for running the Next.js web application frontend and local development server.
GitHubfree
Source control and integration with deployment platforms.
AI coding tools
Recommended stack
| Frontend | Next.js with Tailwind CSS and shadcn/ui |
|---|---|
| Backend | Next.js API Routes / Node.js OTLP receiver |
| Database | ClickHouse Cloud / SQLite for local personal subset |
| Auth | better-auth |
| Payments | Stripe |
| Other | OpenTelemetry JS SDK, Recharts for visualization |
Build guide
01Project Scaffolding & Database Schema
Initialize the Next.js project with Tailwind CSS, shadcn/ui, and set up a ClickHouse or SQLite schema optimized for storing wide event rows containing timestamp, trace_id, span_id, name, duration_ms, and a flexible JSON column for custom attributes.
Create a new Next.js project with TypeScript, Tailwind CSS, and App Router. Set up a database schema using ClickHouse client or SQLite to store distributed tracing spans. Define tables for 'events' with columns: event_id (UUID), trace_id (String), span_id (String), parent_span_id (String), name (String), duration_ms (Float64), timestamp (DateTime), and attributes (JSON/String map for high-cardinality custom fields). Create connection utilities and basic migration scripts.02OpenTelemetry Ingestion Endpoint
Build a high-performance HTTP/gRPC ingestion route in Next.js or a standalone Node.js service that accepts standard OpenTelemetry protocol (OTLP) JSON payloads, extracts spans and metrics, and bulk-inserts them into the storage layer.
Build an API endpoint at `/api/v1/traces` that accepts OpenTelemetry (OTLP) JSON payloads over HTTP. Parse the incoming resource spans, scope spans, and individual span attributes. Flatten the nested OTLP structure into wide event records matching our database schema and batch insert them efficiently. Handle malformed payloads gracefully with appropriate HTTP error codes and validation.03Query Engine & Builder UI
Implement a query builder interface allowing users to filter events by custom fields, group by high-cardinality dimensions, calculate aggregates (p99, count, error rate), and execute fast analytical queries against the telemetry store.
Create a query builder UI component in Next.js with condition rows (field, operator, value), group-by selectors, and calculation metric selectors (COUNT, P95(duration_ms), AVG). Implement the backend query executor that translates these filters into parameterized SQL queries against our telemetry database, returning aggregated results and time-series buckets for visualization.04Trace Waterfall & Visualization
Build an interactive trace waterfall visualization component that reconstructs parent-child relationships from trace_id and span_id fields, rendering nested execution timelines with color-coded durations and attribute inspectors.
Build an interactive trace detail view component in React. Given a `trace_id`, fetch all associated spans and reconstruct their tree hierarchy using parent_span_id relationships. Render a Gantt-style waterfall timeline where bar widths represent span durations, indented hierarchically. Add a side drawer that displays all key-value attributes and logs for any selected span.05BubbleUp Anomaly Detection
Implement a simplified BubbleUp statistical comparison feature that lets users select a subset of outlier traces (e.g., high duration or errors) and compares their attribute distributions against baseline traffic to highlight anomalous tags.
Implement a BubbleUp anomaly analysis feature. Create a UI view where users can highlight a subset of outlier data points on a scatter plot or list. When submitted, the backend compares the frequency distribution of all JSON attribute keys and values within the selected outlier group against the baseline group, calculating statistical divergence and ranking attributes by how strongly they correlate with the anomaly.06Authentication & Polish
Integrate better-auth for user accounts and organization management, polish dashboard navigation, add empty states, and write integration tests for the ingestion and query pipeline.
Integrate better-auth into the Next.js app supporting email/password authentication and multi-tenant organization teams. Protect all dashboard and ingestion API routes behind auth checks (or ingest token headers for OTLP). Add clean loading skeletons, error boundaries, and empty states when no telemetry data has been received yet.
Cost vs paying for Honeycomb
What will you build it with?
Starting total with Claude Code$0 one-time
Starting costs (one-time)
- AI Coding Assistant (Claude Pro / Cursor)$40
Total$40 one-time
Ongoing costs (monthly)
- Railway Hosting & ClickHouse Storage~$10/mo
Total~$10/mo
Paying for Honeycomb
$150/mo (Pro Plan)
Your time to build
120-160 hours
AI tool credits
$40
Break-even
Not viable (building a custom APM store is a systems project, buy the SaaS)
Vibe code Honeycomb: FAQ
- Can you vibe code Honeycomb yourself?
- Don't bother — 15/100 vibecodeable. Build a simple log-search dashboard instead, because replicating Honeycomb's custom columnar storage engine and distributed trace analytics pipeline is a massive systems engineering feat.
- How long does it take to vibe code Honeycomb?
- 6+ months of full-time systems engineering work — roughly 120-160 hours of hands-on time with an AI coding agent.
- How do you build your own Honeycomb?
- Scoped to personal use: Next.js with Tailwind CSS and shadcn/ui on the front, Next.js API Routes / Node.js OTLP receiver behind it, ClickHouse Cloud / SQLite for local personal subset for data. Follow the 6-step build guide on this page — each step has a paste-ready prompt for an AI coding agent.
- How do you code your own Honeycomb without being an expert?
- Use an AI coding tool (Claude Code or Cursor) and work in small steps: scaffold, data model, core screens, then deploy. Realistic effort: 6+ months of full-time systems engineering work. The prompts on this page are written so the AI does the heavy lifting.
- How much does it cost to vibe code Honeycomb instead of paying?
- About $40 one-time to start and ~$10/mo to run, versus $150/mo (Pro Plan) for Honeycomb. Break-even: Not viable (building a custom APM store is a systems project, buy the SaaS).
- What stack should you use to vibe code Honeycomb?
- Next.js with Tailwind CSS and shadcn/ui; Next.js API Routes / Node.js OTLP receiver; ClickHouse Cloud / SQLite for local personal subset; plus OpenTelemetry JS SDK, Recharts for visualization.