Production context. Connected.

Your next error.
Your next answer.

Follow a production failure from the user's session to the failing request and the code change behind it.

Start free Explore an investigation ↓

Logs, traces, events and on-call in one workspace.
Paid plans from $15/month. AI uses your own provider key.

Works with your stack.NETPythonNode.jsGoJavaOpenTelemetry All integrations →
Follow the evidence

One failure. Three useful questions.

Explore the workflow below. Example data illustrates how the product connects your investigation.

Logs + patterns + traces

Find the failure in the noise.

Group repeated messages into patterns. Open a full example, inspect its stack trace, then follow the request across your services.

Explore traces →
ZipLogger Search: 24 hours of traffic in the histogram with log patterns clustered by template Thousands of near-identical lines collapsed into a handful of templates, so the spike is visible without scrolling.
EXAMPLE / ERROR PATTERNPayment failed for cart <*>
checkout-apiError
Browser request
Checkout
Payment

Open the failing span → inspect the exception

Detect. Page. Investigate.

Wake the right person.
Bring the evidence.

On-call schedules, escalation policies and incident timelines sit beside the logs and traces. Use built-in paging or connect your existing PagerDuty or Opsgenie workflow.

Explore incident management →
Checkout errors risingCritical
  1. Rule triggered 10:43
  2. On-call responder notified 10:43
  3. Acknowledged 10:45

Illustrative timeline · SMS and voice allowances apply.

Underneath it all

The telemetry the chain is built on

All of the above needs somewhere for the data to land. These are the parts you would expect from a log platform, and they are what make the connecting work possible. Running on Kubernetes? One read-only Helm chart adds cluster monitoring: every workload and pod, failures explained from Kubernetes’ own evidence, and incidents when something breaks. Kubernetes monitoring →

Search, patterns and issues

Full-text and field queries with a click-to-zoom histogram, clustering that turns a wall of near-identical lines into a handful of templates, and errors grouped into Issues you can assign and resolve.

Distributed traces

An OpenTelemetry receiver and request waterfalls, with a field explorer over every span attribute you send.

APM

Latency, errors and traffic for every service and endpoint, built from your OpenTelemetry traces, with dependencies, an observed service map and regression detection. APM →

Product events

Event explorer, funnels, retention and cohorts, sitting beside the logs and traces from the same window.

Dashboards

One canvas for all of it. A funnel and an error rate can share a board with deployments and suspected regressions.

Service health

Every service on one board, with health-check URLs polled for up or down, and daily rollups that outlive the raw data.

Releases

What shipped and when, derived from the release your SDK stamps on every record, so "did that deploy do this" is a question you can answer.

Retention you set

Per signal, within your plan's window, with a storage cap and a warning threshold you choose.

AI, on your terms

Give your coding agent production context

Analysis on your own key

Every AI feature runs on your own Anthropic, OpenAI or Google account. The key is verified when you save it, encrypted at rest, and never shown again. Nothing reaches a model until you ask for an analysis, and with no key configured the rest of the product is unaffected.

Ask in plain English, summarise an error storm, explain an incident, or get a root cause and a suggested fix on a regression case.

MCP: let the agent look for itself

ZipLogger runs a Model Context Protocol server, so Claude Code, Cursor or any MCP client can query your workspace while you work. Its tools only read: they list your environments, search logs, group error patterns, list traces and open one, read service status, and list git regressions.

Connect with OAuth from the client, or with a read API key for CI (an ingest-only key is refused, because it is safe to ship in a browser). Read-only by design: the agent investigates and explains, and every change stays yours to make.

Connect an AI assistant →

Getting data in

Send what you already have

The official SDKs behave identically across languages: a bounded queue, batching, retries that respect a rate limit, and dropping rather than blocking when the queue is full. Your application never waits on us.

Language SDKs

.NET with an ILogger provider and a Serilog sink, Python, Node.js with Pino and Winston transports, Go with a log/slog handler, Java, and the browser with error capture, a React error boundary and fetch tracing.

All integrations →

OpenTelemetry

A native OTLP over HTTP receiver for logs and traces. Point an existing exporter or Collector at ZipLogger and change nothing else.

OTLP setup →

Anything else

Fluent Bit and Vector pipelines for apps you cannot modify, an NDJSON HTTP API, Mixpanel-shaped event ingestion, and a Grafana-compatible read surface so existing dashboards keep working.

Grafana as a data source →

4signals in one workspace: logs, traces, metrics, events
6official SDKs, plus OpenTelemetry
0servers for you to run
$0to start, free forever

Built by developers who were tired of grepping through logs at 3 AM. ZipLogger is in active beta: no invented testimonials here, just the product. Try it on your own logs and tell us what breaks.

Trust

What happens to your data

Encrypted in transit and at restHTTPS everywhere; git tokens, AI keys and integration secrets sealed with AES-GCM in the database.
Isolated per workspaceEvery query is scoped to your tenant, and roles (Admin, Editor, Viewer) gate destructive and administrative actions.
Retention you chooseUp to 3, 14, 30 or 90 days depending on your plan; your workspace can choose shorter windows per signal, and a storage cap you set can remove the oldest days sooner. Editors and Admins can delete matching logs at any time.
AI only when you askZipLogger does not sell your telemetry or use it to train models. When you request an AI analysis, selected context is sent to the AI provider you configured, under your account terms with that provider.

We hold no security certification yet, and the terms do not promise an uptime level. Both are stated plainly in the privacy policy and terms rather than implied away.

Pricing

Flat, predictable pricing

Free$03 GB a month · 3-day retention
Pro$1520 GB a month · git regressions · AI
Team$49100 GB a month · 5 users · all alert channels
See all plans Business and Enterprise available. Annual billing saves two months.

When production breaks, know what changed.

Send your first log with two lines of code. Connect a repository when you are ready, and your next incident points at the commit behind it.

Start free View pricing
FAQ

Questions, answered

Is there really a free plan?

Yes: 3 GB of logs and traces a month with 3-day retention, dashboards, and alerts, free forever. It is enough for a side project or a proper evaluation. Start free, no credit card required: you confirm your email address and the workspace is yours; on Free every allowance is a hard stop, so nothing can turn into a bill. Paid plans start at $15/month for 20 GB a month, and add AI analysis, git regression attribution and longer retention. $49 buys 100 GB a month and $149 buys 300 GB. Each comes with a 14-day free trial, which is where a card is first asked for.

How does the git integration work?

Connect a repository in one click with the ZipLogger GitHub App, or with a fine-grained token; GitLab (cloud or self-managed) works the same way. Nothing is cloned. When you analyze an error, ZipLogger uses the provider's API to blame only the files in that stack trace and read the one suspect commit, on demand. Access is read-only and you can revoke it at any time.

How does ZipLogger handle my data?

Logs travel over HTTPS and stay scoped to your workspace; credentials such as git and AI keys are encrypted at rest. Raw data is kept for up to 3, 14, 30 or 90 days depending on your plan. Your workspace can choose shorter windows, and a storage cap you set can remove the oldest days sooner. Editors and Admins can delete matching logs at any time. ZipLogger does not sell your telemetry or use it to train models. AI analysis is opt-in: when you ask for one, a bounded selection of the data you chose is sent to the AI provider you configured, under your account terms with that provider.

Which languages and frameworks are supported?

Official SDKs cover .NET (ILogger, Serilog), Python (logging), Node.js (Pino, Winston), Go (slog), Java (java.util.logging), and the browser (with a React error boundary), every one dependency-free with identical batching and retry behavior. Everything else can send logs through the OpenTelemetry OTLP receiver, Fluent Bit/Vector pipelines, or the plain NDJSON HTTP API. If it can POST JSON, it can log to ZipLogger.

How is ZipLogger different from Datadog, Sentry, or Loggly?

Most stacks can each do part of this. What ZipLogger joins up is the whole path: the user's session, the trace their browser started, the error, the pattern it belongs to, and the commits your stack trace blames. Attribution runs over the GitHub or GitLab API without cloning anything, and an AI root cause runs on your own provider key. Pricing is flat rather than per-GB, there is nothing for you to install or scale, and the SDKs behave identically across languages.