Sentry: catch what throws.
Dstl8: distill and fix.
Sentry is built for exceptions. Drop in an SDK and every exception your code raises becomes a triaged issue with a stack trace. The gap is everything in production that fails without raising one: a pod that keeps restarting, a deploy that degrades without erroring, config drift, a platform limit, an LLM refusal wrapped in a 200. Dstl8 is observability rebuilt for teams shipping at AI speed. Telemetry distillation reads what every log line says rather than its status code, across your whole deployment chain, 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.
# 14:02 UTC checkout completes. nothing throws. SENTRY SHOWS YOU errors 0 status 200 across the board crash-free 99.98% nothing raised, so there is nothing to report DSTL8 SENDS YOU pattern 4,995 responses carry a content-filter refusal in the body, HTTP 200 every time where llm-enrichment writes null to supabase impact payment succeeds, the order enriches empty it lands in Claude Code, with the recommended fix
What Dstl8 and Sentry 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, so a 200 that carries a refusal is as visible as a 500
- Opens incidents for failures nobody wrote a rule for. Nothing to configure, no SDK, no code change
- 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, including infrastructure and config outside your application code
- 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
Error monitoring built for developers
Sentry turns exceptions into triaged issues with the stack trace, the release, the commit and the owner attached. A few lines of SDK and it works, for the code you instrument.
- Error and crash monitoring across web, mobile, games and desktop
- Seer, an AI debugging agent that root-causes issues and writes merge-ready patches through Autofix
- AI Code Review, which checks a GitHub pull request against real production error history before it merges
- Session replay, profiling, tracing, metrics, uptime and cron monitoring under one SDK
- Agent tracing for LLM spans, tool calls and token spend
- An MCP server, a CLI, and open source under a permissive licence, with a free tier to start on
Observability was built for human-speed development. The world has changed.
Error monitoring was built for a deterministic world: humans wrote the code, humans knew how it failed, and when it failed it threw. An exception is a written confession that the code knew something went wrong. The model underneath is capture the exception, group it, assign it, and price it per event.
AI-speed development breaks that assumption. More product, faster means more code, across more surfaces, than anyone can instrument by hand. New kinds of failure means wrong outputs, truncated responses, LLM refusals wrapped in a 200 and config drift that never raises, so you need detection that reads what the code actually said, not what it threw. A streamlined SDLC means the developer and the agent that shipped the change need the answer in their flow, before the deploy, not after a user reports it. 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 Sentry overlap
Both of us decided the answer belongs where the developer already is. In each case the surface is shared and the mechanism underneath is not.
Dstl8 vs Sentry: key differences
Almost all of it comes down to one thing. Sentry starts from an exception. Dstl8 starts from the log line.
| The job | Dstl8Answer one question: what did the change we just shipped do, and what should the agent do next? | SentryTurn every exception your application raises into a triaged, owned, fixable issue. |
|---|---|---|
| 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. | SentryAn exception. Something in your code has to raise before the issue exists. Seer’s open-ended investigation of symptoms is in early preview. |
| 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. | SentryEvents, spans and replays your SDK sends, retained and priced per event, with sampling and rate limits to keep the bill in bounds. |
| How it connects | Dstl8Runtime sources. No SDK, no code change, and it covers services nobody instrumented. | SentryAn SDK inside your application. A few lines, and the depth of what you get comes from being in the process. |
| What it can see | Dstl8Application logs plus Kubernetes events, CloudWatch, OTLP streams and platform logs from Vercel, Supabase, Railway and Cloudflare. The whole deployment chain. | SentryWhat your instrumented application reports. Infrastructure and config sitting outside that code is out of frame. |
| Where it runs | Dstl8Staging, test and production. A runtime verdict on every change, keyed to the deploy, before customers see it. | SentryAnywhere the SDK runs, including staging. The unit is the exception, not the deploy, so a release that degrades without throwing passes clean. |
| Where the AI acts | Dstl8Hands the cause, the evidence and the file and line to your coding agent, which writes the change with full context. | SentrySeer Autofix writes merge-ready patches for captured issues, and AI Code Review checks pull requests before they land. |
| Memory | Dstl8A knowledge graph. Touch a service and it shows you what went wrong there before, across teams and services. | SentryIssue history per project, tied to releases and commits, which is where Seer draws its context from. |
| Breadth | Dstl8Narrow on purpose. No replay, no profiling, no uptime checks, no mobile crash reporting. | SentryWide across the application layer, with ten products under one SDK. |
| 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. | SentryPer event, span and replay on a tiered plan, plus Seer as a separate add-on billed per active contributor. More errors and more contributors both raise the bill. |
Running Dstl8 and Sentry together
These two sit next to each other with almost no overlap in what they watch. Sentry stays on the application. Dstl8 sits upstream and adds the loop back into development.
Keep Sentry on your application
The SDK stays where it is. Exceptions, crashes, replays, releases and issue ownership all keep working exactly as they do now.
Point Dstl8 at the runtime
dstl8 sources add kubernetes, cloudwatch, otlp, vercel, supabase, github. The layers your SDK cannot reach. Start in staging if you like.
Let it distill while Sentry captures
Telemetry distillation reads each line at the source and keeps the signal, including the failures that returned 200. Nothing is sampled away before Möbius has read it.
Wire the MCP server to your editor
dstl8 install claude-code. Sentry’s server gives your agent the stack trace and the commit that broke. Dstl8’s gives it the pattern nothing threw an exception for, plus what happened last time.
Dstl8 vs Sentry: capability comparison
Rows are problems, not SKUs.
| Capability | Dstl8 | Sentry |
|---|---|---|
| Finds failures with no alert rules written | Supported | Partial |
| Surfaces failures that never raise an exception | Supported | Partial |
| Reads log content, not just status codes | Supported | Partial |
| Telemetry distillation at the source; bulk volume stays in your network | Supported | Not supported |
| Works with no SDK and no code change | Supported | Not supported |
| Runtime verdict on every deploy in staging | Supported | Partial |
| Continuous analysis, nobody on watch required | Supported | Partial |
| Kubernetes, CloudWatch and OTLP infrastructure sources | Supported | Partial |
| 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 and teams | Supported | Partial |
| Autonomous root cause investigation | Supported | Supported, separately priced product |
| AI investigation included, with no separate AI charge | Supported | Not supported |
| Priced without a volume meter | Supported | Not supported |
| Self-serve trial, no sales call | Supported | Supported |
| Session replay | Not supported | Supported |
| Mobile and game crash reporting | Not supported | Supported |
| Distributed tracing and spans | Not supported | Supported |
✓ Supported ● Partial — Not supported add-on Separately priced product
Dstl8 or Sentry: 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, including the ones that never threw
- Stack: what breaks sits in Vercel, Supabase, Railway, Kubernetes or config, outside your application code
- Staging: you want a runtime verdict on every change before it ships, not an issue after a user hits it
- Cost: you want a price that does not move with error volume or with how many people commit
Sentry alone is enough if
- every failure you care about raises an exception
- nothing that breaks sits outside your application code, whether Kubernetes, config or platform limits
- your LLM calls never return a 200 with the wrong answer inside
- finding out after a user hits it is soon enough
- you’re happy paying per event, plus per contributor for the AI
Questions people ask about Dstl8 and Sentry
Short answers, written to stand on their own.
Is Dstl8 a replacement for Sentry?
It depends on what fails. Sentry captures exceptions and crashes through an SDK inside your application and turns them into triaged issues. Dstl8 is observability rebuilt for AI-speed development: telemetry distillation reads the content of every log line across your deployment chain, so failures that never raise an exception still surface, in staging and production, with no SDK and no alert rules. Many teams run both: Sentry for what throws, Dstl8 for what does not.
What does Dstl8 catch that Sentry does not?
Production failures that happen outside your application’s exceptions. Infrastructure trouble like pods that keep restarting, OOM kills and node pressure; platform limits and errors in Vercel, Supabase, Railway or CloudWatch; deploys and config changes that degrade behavior without erroring; and silent failures such as an LLM call that returns 200 with a refusal in the body, or a job that completes and writes nothing. None of these raise an exception in your code, so none reach an error monitor. Dstl8 reads what every log line says across the runtime and ties it back to the change that caused it.
What does Dstl8 leave to Sentry?
Exception and crash capture with stack traces, session replay, profiling, uptime and cron monitoring, mobile and game crash reporting, distributed tracing, Seer Autofix pull requests, and AI Code Review on GitHub pull requests.
Do I need to add an SDK to use Dstl8?
No. Dstl8 connects to runtime sources such as Kubernetes, CloudWatch, OTLP, Vercel, Supabase, Railway and GitHub, so it covers services nobody instrumented and infrastructure that sits outside your application code. Distillation runs inside your environment: raw volume stays put, and only the signal and selected evidence lines go up.
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. Sentry’s Seer is a debugging agent for issues Sentry has already captured. Möbius doesn’t wait for an exception: it reads every log line across the runtime, from the app down to the infrastructure, 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.
Not sure which side you are on?
Tell us what you run and what broke last. If it threw, Sentry probably caught it. If production was wrong while every dashboard stayed green, that is what we built for, and we will show you whether Dstl8 would have surfaced it.
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Sentry is a registered trademark of Functional Software, Inc. ControlTheory is not affiliated with or endorsed by Sentry. What we describe here comes from Sentry’s public documentation and product pages and was correct in October 2026. Capabilities and pricing change, so check anything decision-critical with the vendor.
Sources: Sentry pricing docs, including Seer · Dstl8 pricing
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