# How to Vibe Code Your Own Honeycomb (and Stop Paying for It)

> AI-Ready Observability Platform

- Site: https://honeycomb.io
- Category: Observability & APM
- Verdict: **Don't bother** (15/100 vibecodeable)
- Estimated effort: 6+ months of full-time systems engineering work

## Verdict

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.

### 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

## What it 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

- Free: Free — For testing and individual projects
- Pro: $150/mo — For teams with production applications
- Enterprise: Custom — For large-scale multi-team workloads

### Funding

$150M raised.
- Seed Round ($4M)
- Series A ($11M)
- Series B ($20M)
- Series C ($50M)
- Series D ($50M)
Investors: Insight Partners, Scale Venture Partners, Headline, Storm Ventures, Industry Ventures, NextWorld Capital, Uncorrelated Ventures

Founded 2016.
Team size: 186-300.

## The hard parts

- 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 vibe code Honeycomb

### Prerequisites

- Node.js (free): Required for running the Next.js web application frontend and local development server.
- GitHub (free): Source control and integration with deployment platforms.

### Recommended AI tools

- Claude Code: Best-in-class terminal agent for scaffolding multi-file Next.js apps and writing complex OTLP endpoint parsers.
- Cursor: Ideal AI code editor for iteratively polishing interactive UI components, heatmaps, and trace visualization trees.

### 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

### Hosting

- Railway (Hosting the Node.js OTLP telemetry ingestion server and ClickHouse-compatible database backend.): ~$10/mo
- Vercel (Hosting the Next.js observability frontend dashboard and query interface.): $0/mo

### Build guide

1. **Project 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.
```

2. **OpenTelemetry 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.
```

3. **Query 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.
```

4. **Trace 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.
```

5. **BubbleUp 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.
```

6. **Authentication & 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

**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 the SaaS instead: $150/mo (Pro Plan)
- Build time: 120-160 hours
- AI tool credits: $40
- Break-even: Not viable (building a custom APM store is a systems project, buy the SaaS)

## Sources

- [Honeycomb Official Website](https://www.honeycomb.io)
- [PitchBook Honeycomb Company Profile](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEphbzK_Aufj_zx8PAloAb7RPoGEJVzTZzIT5_OTQ_rI1CuhsDqgTol2dUyg8dhRK3LjySZy0HU2Lnj5sO061tAzhHpyGrIZS8-ksgegNabp3EUgY7zwPgl0vrAGJMAlvmYxN4qy0Y=)
- [SiliconANGLE - Observability platform Honeycomb pockets $50M in new funding](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEoGnyhfdN7doIy28nKfZozAwfpOM9GbrH4cHHXTaSQoneE7GBnbmNgqddefJexEi4EeSd09F_VCmOBhAgiwfGALJQSq0kuVZpxtKr9y3ADpbKFArxbPlyxS09he4RubJGckQHB3NC0bi0zxTdTeZhgvDdFncPY2b-dVhTq7aUc8jqB-Jvf-fhZc6kgM2cp0gEQfCk=)
- [Wikipedia - Honeycomb (company)](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQH23Wy8pd9K-U9f7MWifWfBe29EtuEGsOoBlqy7V-_CoR85thpWPfQiB5GuxDD2woJA7jnELfEEuM902FAc5tjZthsONHBrTF2WtIX_hj2sE0MpJBEb9LnxO5g4YDLieXRygZ5YqPuY)
- [Tracxn - Honeycomb Company Profile & Metrics](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHudXqRyD5tXHSglGxHxyPzbADOBhyf06TunsLDaM9kIsUBgVWXgPFxGoN6rrZZti8AWfVEvBCASYRPWQ2FEppz8Tt11GGfUzuRLkeYhSnpFeeS1SumfVSBjUSvMJAljbsFjKT3maFRirlEcEcAWEJA2y_wmPMobyo3CVMHcOOjv5EHTmNbT_RILB33tw=)