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Autometrics is a Rust library that automatically instruments functions with production-ready metrics (request rate, error rate, latency) via a simple macro decorator. It integrates with Prometheus and OpenTelemetry, generates pre-built Prometheus queries, and includes Grafana dashboards—but the project is no longer actively maintained.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | autometrics-dev/autometrics-rs |
| Owner | autometrics-dev |
| Primary language | Rust |
| License | Apache-2.0 — OSI-approved |
| Stars | 834 |
| Forks | 33 |
| Open issues | 14 |
| Latest release | v3.0.0 (2025-09-18) |
| Last updated | 2025-09-18 |
| Source | https://github.com/autometrics-dev/autometrics-rs |
What autometrics-rs is
A procedural macro (`#[autometrics]`) that wraps functions to emit standardized metrics through pluggable backends (Prometheus, OpenTelemetry, metrics crate). It correlates metrics with code versions, supports SLO-based alerts and trace exemplars, and produces ~140-2100 ns overhead depending on the backend used.
Get the autometrics-rs source
Clone the repository and explore it locally.
git clone https://github.com/autometrics-dev/autometrics-rs.gitcd autometrics-rs# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Verify the exact version of the underlying metrics library (prometheus-0_14, prometheus-client-0_21, etc.) matches your codebase; mismatches cause metrics registry duplication and data loss.
- Configure the metrics exporter endpoint early (e.g., /metrics) and ensure Prometheus scrape config targets it; no automatic service discovery is provided.
- Benchmark overhead with your chosen backend (140 ns for prometheus-0_14 up to ~2100 ns for opentelemetry-0_20); validate against your function call frequency and latency budgets.
- Plan cardinality: function-level metrics are granular but still bounded; avoid instrumenting generated/dynamic function names to prevent label explosion.
- Decide whether to instrument all public functions or selectively add the macro; the examples show both patterns but systematic adoption requires a team convention.
When to avoid it — and what to weigh
- Requires active upstream support and rapid bug fixes — Project is explicitly no longer actively maintained. Security patches, breaking dependency updates, and compatibility fixes are not guaranteed.
- Need metrics backends other than Prometheus/OpenTelemetry/metrics — Autometrics is tightly coupled to specific metric collection libraries. Custom or proprietary backends require significant additional integration work.
- Prefer minimal macro-based code generation — The macro modifies function signatures and behavior. Teams uncomfortable with procedural macros or their compile-time overhead should use explicit instrumentation libraries.
- Existing complex, heterogeneous observability stack — If your organization already has custom metric schemas, labeling conventions, or non-standard collectors, Autometrics' opinionated standardization may create conflicts.
License & commercial use
Licensed under Apache-2.0, a permissive OSI license allowing commercial use, modification, and distribution with minimal restrictions (requires attribution and a copy of the license).
Apache-2.0 permits commercial use, but the project is no longer actively maintained. For production deployments, assess your tolerance for unmaintained dependencies: no vendor support, no guarantee of security patches, and potential incompatibility with future Rust toolchain or ecosystem changes.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Stale |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Possible |
| Assessment confidence | High |
Standard library security hygiene applies. No built-in metrics authentication; the /metrics endpoint must be protected separately (e.g., firewall, reverse proxy). Metrics themselves can leak information about application behavior; use appropriate access controls. Unmaintained status means security advisories for transitive dependencies may not be promptly addressed.
Alternatives to consider
tracing + metrics crates (manual instrumentation)
Lower-level, actively maintained, and gives full control over metric schemas and cardinality. More boilerplate but no macro-based coupling or unmaintained risk.
OpenTelemetry Rust SDK + otel-prometheus (manual setup)
Industry-standard, vendor-neutral, and actively maintained. Requires more configuration but offers richer context (traces, logs, metrics) and direct supplier support.
Prometheus client libraries (prometheus-rs)
Direct, battle-tested, and actively maintained. No automation, but full transparency and control; better for teams with established observability practices.
Build on autometrics-rs with DEV.co software developers
Autometrics offers quick instrumentation but is no longer maintained. Assess your risk tolerance and consider actively-maintained alternatives before production adoption.
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autometrics-rs FAQ
Is autometrics still supported?
What is the performance overhead?
Can I use autometrics with my existing Prometheus setup?
Do I need to set up Grafana?
Custom software development, end to end
Standing up autometrics-rs properly takes more than a deploy. As a software development agency, DEV.co handles the open-source observability architecture, the web development on top, and the operational work underneath.
Evaluate Autometrics for your Rust observability
Autometrics offers quick instrumentation but is no longer maintained. Assess your risk tolerance and consider actively-maintained alternatives before production adoption.