Dash0: store, dashboard, AI on top.
Dstl8: distill and fix.
Dash0 rebuilt the observability backend on OpenTelemetry and put an AI SRE on top of what it stores. Dstl8 is observability rebuilt around the loop. Telemetry distillation reads every log line at the source and keeps the signal, Möbius finds the failures nobody wrote a rule for, in staging and production, and the cause lands with the developer who shipped the change. Here is what each one does, so you can pick one, the other, or both.
# one collector, two destinations, no duplicate instrumentation exporters: otlp/dash0 → stores, queries, dashboards, alerts, Agent0 otlp/dstl8 → distills at the source, reasons, feeds Claude Code service: pipelines: logs: [otlp/dash0, otlp/dstl8] traces: [otlp/dash0, otlp/dstl8]
What Dstl8 and Dash0 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
- Opens incidents for failures nobody wrote a rule for. Nothing to configure
- 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 data
An OpenTelemetry-native platform
Dash0 is a full observability backend built on OpenTelemetry, PromQL and Perses.
- Metrics, logs, traces and profiles in one store, with dashboards, alerting, service maps, website monitoring and synthetics
- Agent0, an AI SRE that scans continuously, surfaces degrading services with no alert coverage, traces issues to a commit and drafts a pull request
- Automations, so Agent0 can act on event-driven workflows without anyone opening a chat
- Darkplane, which tracks adoption, cost and cycle time for Claude Code and Cursor across your engineering org
- SignalControl, a rule-based pipeline that filters, samples and aggregates telemetry before it is stored
- 100% PromQL, a Kubernetes operator, a Terraform provider, a CLI and published agent skills
Observability was built for human-speed development. The world has changed.
The first generation of observability was built for a deterministic world: humans wrote the code, humans knew how it failed, humans wrote the alert rules and watched the dashboards. Monitoring encodes what someone anticipated. The model underneath is collect everything, pay for all of it, configure it yourself. Dash0 rebuilt that backend on open standards and put an AI SRE on top of the store. That is a real improvement. It is still the same model.
AI-speed development breaks all three. More product, faster means more telemetry than anyone can afford to store, so you need signal before storage, not sampling rules after ingest. New kinds of failure means wrong outputs, truncated responses, silent LLM API errors and config drift that never trip a threshold, so you need detection that reads what the code actually said, not what a metric summarized. A streamlined SDLC means the developer and the agent that shipped the change need the answer in their flow, in staging, before it becomes a production incident for an AI SRE 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 Dash0 overlap
More than with anyone else on this site. In each case the surface is shared and the mechanism underneath is not, so the overlap is real but shallower than a checklist suggests.
Dstl8 vs Dash0: key differences
The differences that decide which one finds your next incident.
| The job | Dstl8Answer one question: what did the change we just shipped do, and what should the agent do next? | Dash0Be the OpenTelemetry-native backend for everything in production, then run an AI SRE over it. |
|---|---|---|
| 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. | Dash0Alerting rules you define, plus Agent0 scanning stored metrics and traces for services degrading without alert coverage. |
| 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. | Dash0Everything you send, into SignalStore. SignalControl lets you write rules to filter, sample and aggregate on the way in, so you decide up front what the AI will never see. |
| Telemetry trust | Dstl8Every line is read before anything is filtered, sampled or dropped, so conclusions rest on the full stream. Where telemetry goes missing. | Dash0Filter and sampling rules decide what is kept on the way in. Agent0 reasons over what is left. |
| 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. | Dash0Production. Agent0 is a production AI by Dash0’s own description; pre-production coverage is a second environment and a second bill. |
| Which way the loop runs | Dstl8Back into the coding session. The agent that wrote the code gets the cause, the evidence, the file and line, and every prior fix, before it writes the next change. | Dash0Into a review queue. Agent0 traces the incident to a commit and opens a pull request. Context for coding agents is on the roadmap. |
| 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. | Dash0Deep Kubernetes and cloud coverage with a large integration hub, aimed at conventional infrastructure and anything that emits OTLP. |
| Where you work | Dstl8Terminal, CLI, editor. Findings arrive where the code is written; the dashboard is there when you want it, not where you have to live. | Dash0A web app first: dashboards, PromQL, service maps and Agent0 chat. A CLI and MCP server reach out from there. |
| Memory | Dstl8A knowledge graph, shipping today. What one team learns, every team keeps, and it compounds across services and incidents. | Dash0History lives in retained telemetry and platform config. A context graph for agents is announced as coming soon. |
| Breadth | Dstl8Narrow on purpose. No PromQL, no synthetics, no Kubernetes operator, no coding-spend analytics. | Dash0Wide for a young company. Metrics, traces, logs, RUM, synthetics, alerting, IaC, and a whole product line for measuring AI coding. |
| 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. | Dash0Usage-based storage, Agent0 billed separately at $0.60 per credit, charged by what each run consumes, and Darkplane per user. The pricing is transparent, and the more telemetry and the more incidents you have, the more it costs. |
Running Dstl8 and Dash0 together
Both speak OpenTelemetry, so there is almost nothing to wire up twice. Dash0 stays the record. Dstl8 sits upstream and adds the loop back into development.
Keep Dash0 as your backend
Metrics, traces, dashboards, alerting and PromQL stay exactly where they are. Your collector config barely moves.
Add Dstl8 as a second OTLP destination
dstl8 sources add otlp, then direct sources for Vercel, Supabase, Railway or GitHub if you run them. Start in staging if you like.
Let it distill while Dash0 stores
Telemetry distillation reads each line at the source and keeps the signal. Because Dstl8 runs upstream, teams also use what it finds to tune what is worth sending to Dash0 at all.
Wire the MCP server to your editor
dstl8 install claude-code. Runtime context reaches the tool that wrote the code, with no tab switch, from the first staging deploy onward.
Dstl8 vs Dash0: capability comparison
Rows are problems, not SKUs.
| Capability | Dstl8 | Dash0 |
|---|---|---|
| Finds failures with no alert rules written | Supported | Supported |
| Reads log content, not just status codes | Supported | Partial |
| Telemetry distillation at the source; bulk volume stays in your network | Supported | Partial |
| Runs in staging and test, with a runtime verdict keyed to each deploy | Supported | Partial |
| Continuous analysis, nobody on watch required | Supported | Supported |
| Correlates Vercel, Supabase and Railway in one view | Supported | Partial |
| 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 | Announced, not yet shipping |
| Autonomous root cause investigation | Supported | Supported |
| AI investigation included, with no credits or per-task charge | Supported | Not supported |
| OpenTelemetry ingestion | Supported | Supported |
| Kubernetes and AWS monitoring | Supported | Supported |
| Priced without a volume meter | Supported | Not supported |
| No per-investigation AI charge | Supported | Not supported |
| Self-serve trial, no sales call | Supported | Supported |
| Metrics and full PromQL | Not supported | Supported |
| Website and synthetic monitoring | Not supported | Supported |
| AI coding tool adoption and spend analytics | Not supported | Supported, separately priced product |
✓ Supported ● Partial — Not supported add-on Separately priced product
Dstl8 or Dash0: 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 holds a full mental model of what is in production
- Developers: you want failures to reach the developer who shipped them before they reach an AI SRE or on-call
- Stack: yours also spans Vercel, Supabase or Railway, which are quick to wire up and hard to see across
- Staging: you want a runtime verdict on every change before it ships, not a pull request after it broke production
- Cost: you want a price that does not move with log volume or with how many incidents the AI investigates
Dash0 alone is enough if
- you trust that the filter rules you wrote up front never drop the line that matters
- you only need answers in production
- a hard week turning into an expensive week of Agent0 credits is acceptable
- the fix belongs in a vendor’s pull request queue rather than your own coding session
- your telemetry volume isn’t growing
Questions people ask about Dstl8 and Dash0
Short answers, written to stand on their own.
How is Dstl8 different from Dash0?
Dash0 is an OpenTelemetry-native observability backend: metrics, traces, logs, dashboards, alerting, and Agent0, an AI SRE that runs on top of what the backend stores. Dstl8 is observability rebuilt for AI-speed development. Telemetry distillation reads every log line at the source and keeps only the signal, Möbius finds failures nobody wrote a rule for in staging as well as production, and the cause lands with the developer or coding agent that shipped the change. Dstl8 prices on agents and users, not data volume or AI credits.
Should I pick Dash0 or Dstl8?
Pick Dash0 if you are replacing an incumbent platform and need metrics, traces, PromQL, dashboards and alerting from one OpenTelemetry-native vendor. Pick Dstl8 if you ship with coding agents and want failures caught in staging, with no alert rules to write, and delivered to the developer who shipped the change. Many teams run both.
Can Dstl8 and Dash0 run together?
Yes. Both are OpenTelemetry-native, so one collector pipeline can export to both. Dash0 stores, queries and alerts; Dstl8 distills at the source and feeds the coding session.
What does Dstl8 leave to Dash0?
Metrics with full PromQL, dashboards, alerting, website and synthetic monitoring, a Kubernetes operator, a Terraform provider, pull requests drafted by Agent0, and AI coding tool adoption and spend analytics through Darkplane.
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. Dash0’s Agent0 is an AI SRE that reasons over what the Dash0 backend has stored. Möbius reads every log line at the source before anything is stored or dropped, in staging as well as production, 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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Dash0 is a trademark of Dash0 Inc. ControlTheory is not affiliated with or endorsed by Dash0. What we describe here comes from Dash0’s public documentation and announcements and was correct in October 2026. Capabilities and pricing change, so check anything decision-critical with the vendor.
Sources: Agent0 model selection and credits · Agent0 credit consumption · Dstl8 pricing
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