Honeycomb: instrument and query.
Dstl8: distill and fix.
Honeycomb is built on high-cardinality events and tracing, for engineers who already know what to query. Honeycomb’s Agent Timeline watches AI agents running in production. Dstl8 is observability rebuilt around the loop back to those agents. Telemetry distillation reads every log line at the source, with no instrumentation work, Möbius finds the failures nobody wrote a rule for, in staging and production, and the cause lands with the developer who shipped the change. Agents as subject, agents as audience. Here is what each one does, so you can pick one, the other, or both.
# same failure, two questions HONEYCOMB ANSWERS "show me every request where user_tier = enterprise and latency > 2s, grouped by build_id" you drive the exploration, you find it DSTL8 SENDS YOU "what broke in the vercel → supabase path after last night's merge?" it drove the exploration, you get the cause
What Dstl8 and Honeycomb each do
Written plainly, without the marketing layer on either side.
Runtime feedback for AI-speed development
Dstl8 distills telemetry at the point of capture, reasons over it with Möbius, our own fine-tuned model, and hands verified incident context back to the agents and engineers who shipped the code.
- Reads the content of every log line, not just the status code: sentiment, patterns, anomalies, severity. No instrumentation work required
- Opens incidents for failures nobody wrote a rule for. Nothing to configure, no query to know
- Runs in staging and test as well as production, so every change gets a runtime verdict before customers see it
- Correlates one incident across Kubernetes, AWS, Vercel, Supabase, Railway, GitHub and OTel
- Streams context into Claude Code, Cursor and Codex over MCP, and remembers every past incident and prior fix
- Distills inside your environment. Raw volume stays put; only signal and selected evidence lines go up. Priced on agents and users, not events
An observability platform built on tracing
Honeycomb is built on wide events and high-cardinality querying, and rewards teams that instrument richly.
- Exploratory querying over wide events with any cardinality you throw at it, plus distributed tracing, logs and metrics
- Canvas, a collaborative workspace with a chat interface and autonomous agent, and Canvas Skills that encode debugging playbooks
- Auto-investigations that start when an alert fires, an SLO burns or an anomaly surfaces, and propose remediation
- Agent Timeline, which lines up every agent invocation, LLM call and tool call as one view
- OpenTelemetry GenAI semantic conventions built in, with
gen_ai.*attributes treated as first-class - MCP integrations and agent skills for Claude Code and Cursor, plus SLOs and error budgets
Observability was built for human-speed development. The world has changed.
Honeycomb argued that the interesting failures are the ones nobody predicted, and built a tool for a curious engineer to go find them. The model underneath is still instrument richly, store the events, and put a human, now with an agent alongside, at the query box.
AI-speed development strains all three. More product, faster means more code than anyone can instrument well, and more events than anyone can afford to store, so you need signal at the source, not richer spans. New kinds of failure means wrong outputs, truncated responses, LLM refusals wrapped in a 200 and config drift, so you need detection that reads what the code actually said, with nobody deciding what to query. A streamlined SDLC means the developer and the agent that shipped the change need the answer in their flow, in staging, before anyone has a hunch to chase. Dstl8 is observability rebuilt for that world: telemetry distillation at the source, Möbius reasoning over what is left, continuously, and the cause delivered back to the code.
Where Dstl8 and Honeycomb overlap
More than a checklist would suggest, because we share the premise. In each case the surface is shared and the mechanism underneath is not.
Dstl8 vs Honeycomb: key differences
Two products that agree about the problem and made different bets on the answer.
| The job | Dstl8Answer one question: what did the change we just shipped do, and what should the agent do next? | HoneycombLet an engineer, with Canvas alongside, slice production by any dimension and find the thing nobody predicted. |
|---|---|---|
| What triggers detection | Dstl8Nothing you configure. Möbius reads every line continuously and opens incidents for failures nobody wrote a rule for. 336 incidents in one enterprise POC, zero rules written. | HoneycombTriggers and SLO burn alerts you define, plus Auto-investigations that start when one of those fires or an anomaly surfaces. Between alerts, a human asks. |
| What you collect | Dstl8Telemetry distillation. Every log line is read at the source and only the signal is kept: sentiment, patterns, anomalies, severity. You store a fraction of what you produced: raw volume stays in your environment, and only the signal and selected evidence lines go up. | HoneycombWide events and traces, stored in full so they can be queried by any attribute later. Sampling is how you keep volume in bounds. |
| What it reads | Dstl8Log content. Möbius reads what each line says, so services nobody instrumented still give you signal on day one. | HoneycombThe attributes you put on your spans. Where instrumentation is thin, there is little to see. |
| Where it runs | Dstl8Staging, test and production. A runtime verdict on every change before customers see it. Start in staging and add production when you’re ready. | HoneycombWherever you send events, in practice production. The unit is the query and the trace, not the deploy. |
| Who the AI serves | Dstl8The coding agent and the developer in flow. Evidence lands in Claude Code or Cursor before the next change is written. | HoneycombThe engineer at the Canvas, and Agent Timeline makes production agents legible to that engineer. |
| Runtime coverage | Dstl8First-class sources for Vercel, Supabase, Railway and Cloudflare next to Kubernetes, AWS and Google Cloud: the deployment chains AI-native teams actually assemble. | HoneycombAnything that emits OpenTelemetry, which in practice means services you own and instrument. |
| Memory | Dstl8A knowledge graph. What one team learns, every team keeps, and it compounds across services and incidents. | HoneycombRetained events, shareable Canvas investigations and Canvas Skills that encode what your best engineers know how to do. |
| Breadth | Dstl8Narrow on purpose. No exploratory query language, no distributed tracing, no SLOs, no agent observability call by call. | HoneycombWide across telemetry types: events, traces, logs, metrics, SLOs and a dedicated view for AI agents in production. |
| How you pay | Dstl8Agents and users. A team on Scale is $250/mo. Volume does not move the number, because distillation happens before anything is stored. | HoneycombPer event, on tiered plans. Richer instrumentation and more traffic both raise the bill, and sampling is how you manage it. |
Running Dstl8 and Honeycomb together
Both speak OpenTelemetry, so there is almost nothing to wire up twice. Honeycomb stays on your instrumented services. Dstl8 sits upstream and adds the loop back into development.
Keep Honeycomb for instrumented services
Traces, wide events, SLOs and Canvas stay put. This is the part Honeycomb does better than anyone.
Point Dstl8 at what nobody instrumented
dstl8 sources add vercel, supabase, cloudwatch, kubernetes, otlp. The edges of the chain that never got spans. Start in staging if you like.
Let it distill while Honeycomb stores
Telemetry distillation reads each line at the source and keeps the signal. Because Dstl8 runs upstream, teams also use what it finds to decide which events are worth sending to Honeycomb at all.
Wire the MCP server to your editor
dstl8 install claude-code. Honeycomb’s server gives your agent query access to instrumented production. Dstl8’s gives it the cause and the history of the service being changed.
Dstl8 vs Honeycomb: capability comparison
Rows are problems, not SKUs.
| Capability | Dstl8 | Honeycomb |
|---|---|---|
| Finds failures with no alert rules written | Supported | Partial |
| Useful with no instrumentation work | Supported | Not supported |
| Reads log content, not just status codes | Supported | Partial |
| Telemetry distillation at the source; bulk volume stays in your network | Supported | Not supported |
| Runs in staging and test, with a runtime verdict keyed to each deploy | Supported | Partial |
| Continuous analysis, nobody on watch required | Supported | Partial |
| Correlates Vercel, Supabase and Railway in one view | Supported | Not supported |
| Streams runtime context into the coding agent, keyed to the deploy | Supported | Partial |
| MCP server for Claude Code, Cursor, Codex | Supported | Supported |
| Ships packaged agent skills and a CLI | Supported | Supported |
| Knowledge graph carried across incidents | Supported | Partial |
| Autonomous root cause investigation | Supported | Supported |
| OpenTelemetry ingestion | Supported | Supported |
| Priced without a volume meter | Supported | Not supported |
| Self-serve trial, no sales call | Supported | Supported |
| Exploratory high-cardinality querying | Not supported | Supported |
| Distributed tracing and wide events | Not supported | Supported |
| Metrics, SLOs and error budgets | Not supported | Supported |
✓ Supported ● Partial — Not supported
Dstl8 or Honeycomb: which to pick
Here is how to tell which problem you have.
Pick Dstl8 if
- Speed: you ship at AI speed with Claude Code, Cursor or Codex and nobody knows enough about the code to know what to query
- Developers: you want the answer delivered to the developer who shipped the change, not a place to go looking for it
- Stack: much of it never got instrumented and will not soon, or spans Vercel, Supabase and Railway where spans never line up
- Staging: you want a runtime verdict on every change before it ships, not a hunch to chase in production
- Cost: you want a price that does not rise with event volume or with how richly you instrument
Honeycomb alone is enough if
- you trust that sampling never drops the request that broke
- every service is richly instrumented and will stay that way as agents write more of the code
- your engineers always know what to query
- there’s time to explore after something breaks
- your event volume and bill aren’t growing
- the developer who shipped the change doesn’t need the answer in their editor
Questions people ask about Dstl8 and Honeycomb
Short answers, written to stand on their own.
How is Dstl8 different from Honeycomb?
Honeycomb is an observability platform built on high-cardinality wide events and tracing, with Canvas for investigation and Agent Timeline for watching AI agents run in production. It rewards teams that invest in instrumentation. Dstl8 is observability rebuilt for AI-speed development: telemetry distillation reads the content of every log line at the source with no instrumentation work, Möbius finds failures nobody wrote a rule for, in staging and production, and the cause lands with the developer or coding agent that shipped the change.
Do I need instrumentation to use Dstl8?
No. Dstl8 reads log content directly from sources such as Kubernetes, CloudWatch, OTLP, Vercel, Supabase and Railway, so services that were never instrumented still produce signal. Honeycomb's depth comes from well-instrumented spans and rich attributes.
Can Dstl8 and Honeycomb run together?
Yes. Both accept OpenTelemetry natively, so one collector pipeline can feed both. Honeycomb handles instrumented services and exploratory querying; Dstl8 distills at the source and covers the uninstrumented edges of the deployment chain.
What does Dstl8 leave to Honeycomb?
Exploratory high-cardinality querying, distributed tracing and wide events, metrics, SLOs and error budgets, agent observability call by call through Agent Timeline, and shareable Canvas investigations.
Is Dstl8 an AI SRE?
Yes. Dstl8 is AI SRE tooling that closes the loop for both infrastructure and code, and feeds what it finds back into engineering rather than stopping at the incident. In Honeycomb, AI helps an engineer investigate inside Canvas. Möbius investigates unprompted: it distills every log line at the source, with no instrumentation work, opens incidents nobody wrote a rule for, and hands the cause and a recommended fix to your coding agent, which applies it under your policy, fully automated or reviewed. See how Dstl8 compares with other AI SRE tools.
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Honeycomb is a trademark of Hound Technology, Inc. ControlTheory is not affiliated with or endorsed by Honeycomb. What we describe here comes from Honeycomb’s public documentation and announcements and was correct in October 2026. Capabilities and pricing change, so check anything decision-critical with the vendor.
Sources: Honeycomb 2026 Pro plan changes · Dstl8 pricing
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