Sluice vs Grafana + Prometheus for Celery Monitoring

Grafana and Prometheus are serious infrastructure tools, and for good reason -- they're the backbone of observability at thousands of companies. If you're running Celery in production, there's a decent chance you've already got Prometheus scraping metrics and Grafana rendering dashboards. The question isn't whether the Grafana stack is good. It is. The question is whether it gives you what you actually need when a Celery task fails at 3am.

This page compares two approaches: the standard celery-exporter + Prometheus + Grafana stack, and Sluice -- a purpose-built Celery monitoring platform. We'll be specific about what each does well, where each falls short, and when you should use one, the other, or both.

The standard production stack

Most teams running Celery in production eventually land on the same setup: celery-exporter (537 stars, 122 forks) scrapes Celery events and exposes Prometheus metrics. Prometheus ingests those metrics on a scrape interval. Grafana renders dashboards from the Prometheus data. Some teams add Alertmanager for threshold-based alerts.

celery-exporter exposes roughly 15 Prometheus metrics:

• Counters: celery_task_sent_total, celery_task_received_total, celery_task_started_total, celery_task_succeeded_total, celery_task_failed_total, celery_task_rejected_total, celery_task_revoked_total, celery_task_retried_total
• Gauges: celery_worker_up, celery_worker_tasks_active, celery_queue_length, celery_active_consumer_count, celery_active_worker_count, celery_active_process_count
• Histogram: celery_task_runtime_bucket

These are aggregate counters and gauges -- they tell you the shape of your system at a high level. Pre-built Grafana dashboards plot task throughput over time, runtime distributions via histograms, and worker status as up/down indicators. It works. For teams that already operate a Prometheus+Grafana stack, the marginal cost of adding celery-exporter is low.

But this stack has a fundamental limitation built into its architecture.

Aggregate vs individual -- the core difference

Grafana shows you "47 tasks failed in the last hour." Sluice shows you which 47, what each one was doing, and the full traceback for every failure.

This is not a minor UX difference -- it's a structural one. Prometheus is a time-series database optimized for aggregate metrics. It stores counters and gauges over time, not individual events. celery-exporter exposes celery_task_failed_total{name="payment.process", queue="default"} as a counter that increments, but it cannot tell you:

• Which specific invocation of payment.process failed
• What error or traceback that task produced
• What arguments the task was called with (Sluice V1, opt-in)
• Whether retrying that specific task would fix the problem
• The state history of that task (when it was queued, started, failed)

When your Grafana dashboard lights up with a failure spike, the next step is almost always: SSH into a worker, grep through logs, try to correlate timestamps, maybe check Sentry for the exception. You're context-switching between three or four tools to answer one question: "What happened?"

Sluice captures every individual task as a persistent record. Each task has a name, state history, queue, worker assignment, timestamps, duration, error message, and full traceback. You search for payment.process, filter by failed, click the task, read the traceback, and retry it -- all from one screen.

celery-exporter provides aggregate Celery metrics to Prometheus -- task counts and runtime distributions -- but cannot show individual task details, tracebacks, or management actions. This is by design; Prometheus is not built to store per-event data. It's the right tool for answering "what is the failure rate?" and the wrong tool for answering "why did this specific task fail?"

Feature comparison

Capability	Grafana + celery-exporter	Sluice
Individual task visibility	No. Aggregate counters only.	Yes. Every task with full detail -- name, state history, traceback, result.
Task search and filter	No. Prometheus has no concept of individual tasks.	Yes. Search by name, ID, queue. Filter by state, time range.
Aggregate metrics	Yes. Counters, gauges, histograms via PromQL.	Yes. Overview dashboard with throughput, failure rate, queue depth.
Queue depth monitoring	Yes. celery_queue_length gauge.	Yes. Per-queue depth, throughput rate, consumer count.
Worker health	Partial. celery_worker_up gauge. No resource metrics.	Yes. Status, active tasks, task rate, last heartbeat.
Task actions (retry/revoke)	No. Grafana is read-only.	Yes. Retry or revoke individual tasks and bulk selections.
Traceback visibility	No. Exceptions are not captured.	Yes. Full Python traceback on every failed task.
Alerting	Yes, via Alertmanager. Threshold-based on PromQL queries.	Upcoming (V1). Purpose-built Celery alert conditions -- failure rate spikes, queue backlog growth, silent stalls.
Beat schedule monitoring	No. celery-exporter does not track Beat.	Upcoming (V1). Missed check-in detection for periodic tasks.
Setup time	2--4 hours. Exporter + Prometheus config + Grafana + dashboards + Alertmanager.	30 seconds. pip install sluice + API key, or docker run sluice/agent.
Infrastructure required	3 services minimum: celery-exporter, Prometheus, Grafana. Plus Alertmanager for alerts.	None. SaaS -- data collection runs in your infra (SDK or agent), dashboard is hosted.
Real-time latency	Prometheus scrape interval (default 1m, often overridden to 15--30s) + Grafana refresh. Typically 15--75s depending on configuration.	SSE-based streaming. Near real-time event delivery.
Data retention	Configurable in Prometheus. Typically 15--90 days for aggregate metrics.	Postgres persistence. 24 hours (free tier). Longer retention coming with paid tiers in V1.
Custom dashboards	Yes. Grafana's strength -- build any visualization from PromQL.	Overview dashboard included. Custom dashboards planned for V2.
Maintenance burden	You maintain celery-exporter, Prometheus, Grafana, and Alertmanager. Version upgrades, storage, availability.	Zero. Sluice maintains the platform. You maintain the SDK or agent in your infra.

Two things stand out. First, Grafana has a real advantage in custom dashboards and alerting maturity -- Alertmanager is battle-tested infrastructure, and PromQL is enormously flexible. Second, the entire category of "individual task operations" -- search, inspect, retry, revoke -- simply does not exist in the Grafana stack. These are different tools solving different layers of the same problem.

Setup tax -- hours vs seconds

The Grafana stack

Getting celery-exporter + Prometheus + Grafana running requires touching multiple services and configuration files:

Step 1: Deploy celery-exporter -- Run the exporter container or process alongside your Celery workers. Configure it with your broker URL. Ensure it can connect to your Redis or RabbitMQ instance.

docker run -e CE_BROKER_URL=redis://redis:6379/0 \
  -p 9808:9808 danihodovic/celery-exporter

Step 2: Configure Prometheus scrape target -- Add celery-exporter to your prometheus.yml:

scrape_configs:
  - job_name: 'celery'
    static_configs:
      - targets: ['celery-exporter:9808']

Step 3: Set up Grafana -- If you don't already have Grafana running, that's another service to deploy, configure, and maintain. Add Prometheus as a data source.

Step 4: Import or build dashboards -- Find a community dashboard JSON (there are a handful on Grafana's dashboard marketplace) or build panels from scratch using PromQL queries like rate(celery_task_failed_total[5m]).

Step 5: Configure Alertmanager -- Define alert rules in Prometheus, configure Alertmanager with notification channels (Slack webhooks, PagerDuty keys), tune thresholds to avoid alert fatigue.

Step 6: Maintain all of it -- Version upgrades, storage management, dashboard drift, alert rule tuning. Each service has its own configuration surface.

Realistic setup time: 2--4 hours for someone familiar with the stack. Longer if it's your first Prometheus deployment.

Sluice

Option A: Python SDK

pip install sluice

# settings.py or celery config
import sluice
sluice.init(api_key="sk_...")

Option B: Go agent (Docker)

docker run -e REDIS_URL=redis://your-redis:6379/0 \
  -e SLUICE_API_KEY=sk_... \
  ghcr.io/sluice-project/agent:latest

That's it. Tasks start appearing in the dashboard within seconds. The SDK auto-configures the three Celery flags needed for event emission (worker_send_task_events, task_send_sent_event, task_track_started). The Go agent subscribes directly to your Redis broker's celeryev.* PUB/SUB channels and polls queue lengths via LLEN.

Realistic setup time: 30 seconds.

The difference isn't just convenience -- it's operational surface area. The Grafana stack is four services you need to keep running, monitored, and updated. Sluice is one SDK or one Docker container in your infrastructure, with the dashboard and persistence handled for you.

When Grafana is the right choice

The Grafana + Prometheus stack is genuinely the better option in several scenarios, and we'd be dishonest to pretend otherwise.

You already run Prometheus and Grafana for everything else. If your team has standardized on the Grafana stack for infrastructure, application, and database metrics, adding celery-exporter is incremental. Your on-call engineers already know PromQL. Your dashboards already live in Grafana. Adding Celery metrics to the same pane of glass means less context-switching for aggregate monitoring.

You need custom metric aggregation across many services. PromQL is extraordinarily powerful for cross-service correlation. If you need to query something like "show me the Celery failure rate alongside the Postgres connection pool usage and the Kubernetes pod restart count," Prometheus is the tool. Sluice monitors Celery. Prometheus monitors everything.

You want full control over data retention and storage. Prometheus retention is configurable down to the byte. You decide where data lives, how long it's kept, and who can access it. For teams with strict data residency requirements or those who need metrics retained for years (compliance, capacity planning), self-managed Prometheus gives you complete control.

You need to correlate Celery metrics with infrastructure metrics. The power of the Grafana ecosystem is unified observability. If your debugging workflow starts with "CPU spiked on the worker host" and ends with "which Celery tasks were running during the spike," Grafana's ability to overlay infrastructure and application metrics is unmatched.

When Sluice is the right choice

Sluice is the better option when the debugging workflow starts with "which task failed and why" rather than "what does the aggregate failure rate look like."

You need individual task visibility. Search for a specific task by name or ID. View its full state history -- when it was queued, when a worker picked it up, when it failed. Read the traceback. This is the workflow that Prometheus structurally cannot provide because it stores time-series aggregates, not individual events.

You don't want to maintain monitoring infrastructure. Running Prometheus and Grafana is not free -- these are services that need uptime, storage, backups, and version upgrades. If Celery monitoring is the only reason you'd stand up the Grafana stack, the operational cost likely outweighs the benefit. Sluice is hosted -- the only thing running in your infrastructure is the lightweight SDK or Go agent.

Your team needs task management actions. Retrying a failed task or revoking a stuck one from a dashboard saves the SSH-and-grep workflow that follows every Grafana alert. This is the difference between "I see that something is wrong" and "I fixed it from the dashboard in 30 seconds."

You want monitoring that understands Celery semantics. Prometheus treats Celery metrics as generic counters. Sluice understands what PENDING actually means in Celery (it means "no information," not "waiting in queue" -- a distinction that trips up every new Celery team). Sluice's V1 alerting will include Celery-specific conditions like silent stall detection: "this task was supposed to run every 5 minutes and hasn't run in 15."

You need fast time-to-value. If you're debugging a production Celery issue right now and need visibility in minutes, not hours, Sluice's 30-second setup gets you from zero to "I can see every task" faster than you can configure a Prometheus scrape target.

Running them together

Here's something most comparison pages won't tell you: Sluice and Grafana are complementary, not mutually exclusive. They operate at different layers of the observability stack, and many teams will benefit from running both.

Use Grafana + Prometheus for:

• Infrastructure metrics (CPU, memory, network, disk across your fleet)
• Cross-service correlation (Celery failure rate alongside database latency, HTTP error rates, Kubernetes events)
• Long-term trend analysis and capacity planning
• Custom aggregate dashboards tailored to your team's specific KPIs

Use Sluice for:

• Individual task inspection, search, and filtering
• Traceback visibility on failed tasks
• Task management actions (retry, revoke) from the browser
• Real-time task streaming with sub-5-second latency
• Celery-specific alerting on failure spikes, queue backlogs, and stalled tasks (V1)
• Beat schedule monitoring with missed check-in detection (V1)

The Sluice SDK and Go agent don't interfere with celery-exporter. Both subscribe to Celery events independently -- celery-exporter translates them into Prometheus metrics, while the Sluice agent normalizes them into persistent task records. Running both adds negligible overhead because Celery's event system is PUB/SUB-based; additional subscribers don't increase load on the broker.

Sluice consolidates the Celery-specific functionality spread across Flower, celery-exporter, Grafana, and Sentry into a single purpose-built platform. But if you already have Grafana for your broader infrastructure, keep it. Let Sluice handle the Celery-specific layer where individual task visibility and management actions matter.

The metrics celery-exporter actually provides

It's worth being precise about what celery-exporter gives you, because the metrics are useful -- they're just not the whole picture.

Task state counters (celery_task_sent_total, celery_task_succeeded_total, celery_task_failed_total, etc.) -- labeled by task name and queue. These let you compute rates and ratios: rate(celery_task_failed_total{name="payment.process"}[5m]) gives you the per-second failure rate for a specific task over a 5-minute window. This is genuinely valuable for trend monitoring and alerting on rate changes.

Runtime histogram (celery_task_runtime_bucket) -- labeled by task name. Gives you p50/p90/p99 runtime distributions. Useful for detecting performance regressions: "our email-send task used to take 200ms at p95 and now it's taking 2 seconds."

Worker status (celery_worker_up, celery_worker_tasks_active) -- binary up/down per worker hostname and count of active tasks. Basic but functional for worker health monitoring.

Queue depth (celery_queue_length) -- current message count per queue. Essential for backpressure alerting: "the default queue has 10,000 messages and it's growing."

What's missing from these metrics is the individual event data that would let you answer "which task failed, what error did it throw, and can I retry it?" That's not a criticism of celery-exporter -- it's a limitation of the Prometheus data model. Prometheus stores time-series, not event logs. celery-exporter is doing exactly what it should: translating Celery events into the Prometheus-native format. The gap is structural.

Alerting: Alertmanager vs Sluice

Grafana's alerting story is mature. Alertmanager handles routing, deduplication, silencing, and notification dispatch. You write alert rules in PromQL, define notification channels, and Alertmanager handles the rest. It's battle-tested at massive scale.

Sluice's alerting is coming in V1 and takes a different approach -- purpose-built for Celery semantics rather than generic metric thresholds. Where Alertmanager fires when rate(celery_task_failed_total[5m]) > 0.05, Sluice's V1 alerting will support conditions like:

• Silent stall detection: "This periodic task was supposed to run every 5 minutes and hasn't run in 15 minutes." This is structurally difficult in Prometheus because it requires monitoring the absence of an event -- the thing that didn't happen. You can approximate it with absent() and absent_over_time() queries or PushGateway-based timestamp approaches, but these are fragile and require per-task configuration.
• Task-specific failure patterns: "The same ConnectionError is recurring across 10 different task invocations in the last 5 minutes." This requires inspecting exception strings, which celery-exporter doesn't capture.
• Queue wait time anomalies: "Tasks are sitting in the queue for 30+ seconds before a worker picks them up." This requires per-task timing data, not just aggregate queue depth.

For now, if you need alerting today, Alertmanager is the proven choice. Sluice V1 will offer Celery-specific alert conditions that complement -- and in some cases replace -- generic metric-based alerting.

What about Grafana Cloud?

Grafana Cloud removes the infrastructure management burden of self-hosted Prometheus and Grafana. You get hosted Prometheus (Mimir), hosted Grafana, and managed Alertmanager. celery-exporter pushes metrics via remote-write, and you get the same dashboards without maintaining the stack yourself.

This addresses one of the Grafana stack's biggest drawbacks -- operational overhead -- but the fundamental limitation remains: Grafana Cloud still only receives aggregate metrics from celery-exporter. You get hosted dashboards and managed alerting, but you still can't search for an individual task, read a traceback, or retry a failed job. The data model hasn't changed; only the hosting has.

If you're choosing between self-hosted Grafana and Grafana Cloud for Celery monitoring, Grafana Cloud is the better option for most teams (less infra to manage). If you're choosing between Grafana Cloud and Sluice, the deciding factor is the same as before: do you need aggregate metrics (Grafana Cloud) or individual task visibility and management (Sluice)?

FAQ

Can I use Sluice alongside Grafana?

Yes, and many teams will. The Sluice SDK and Go agent subscribe to Celery events independently from celery-exporter. Both can run simultaneously without conflict. Use Grafana for infrastructure-wide dashboards and cross-service correlation; use Sluice for Celery-specific task visibility, search, and management actions. See the complementary positioning section above.

Does Sluice replace Prometheus?

No. Prometheus is a general-purpose time-series database for infrastructure and application metrics. Sluice monitors Celery job queues specifically. If you use Prometheus for CPU metrics, HTTP request rates, database performance, and Kubernetes health -- keep using it. Sluice replaces the Celery-specific slice of your monitoring: the task-level visibility and management that celery-exporter + Grafana can't provide.

What metrics does celery-exporter provide?

celery-exporter exposes approximately 15 Prometheus metrics: 8 task state counters (sent, received, started, succeeded, failed, rejected, revoked, retried), a runtime histogram, and several gauges for worker status, active task counts, queue length, and process/consumer counts. These are aggregate metrics -- they tell you how many tasks are in each state, not which specific tasks or why they're in that state. Full metric list: celery-exporter GitHub.

How does Sluice alerting compare to Alertmanager?

Alertmanager is production-proven for threshold-based alerting on any Prometheus metric. It supports routing trees, silencing, grouping, and dozens of notification integrations. Sluice V1 alerting (upcoming) focuses specifically on Celery-aware conditions: failure rate spikes, queue backlog growth, worker disappearance, and -- uniquely -- silent stall detection (alerting when an expected task doesn't run). For generic metric alerting across your stack, Alertmanager is the right tool. For Celery-specific conditions that require individual task context, Sluice V1 alerting will handle cases that PromQL can't express natively. Learn more about debugging Celery failures.

What about Grafana Cloud?

Grafana Cloud eliminates the infrastructure management of self-hosted Prometheus and Grafana, which is a meaningful improvement. But it doesn't change the data model: celery-exporter still pushes aggregate metrics, so you still can't see individual tasks, read tracebacks, or manage jobs. Grafana Cloud is the right choice if you want hosted aggregate Celery metrics alongside your other infrastructure dashboards. Sluice is the right choice if you need the individual-task layer.

Is celery-exporter still maintained?

As of early 2026, celery-exporter (maintained by Dani Hodovic and contributors) is actively maintained with regular releases and community contributions. It's a solid open-source project. The limitations described on this page aren't about code quality -- they're about the structural boundaries of the Prometheus data model for per-event visibility.

Can Sluice show me the same aggregate metrics as Grafana?

Yes. Sluice's overview dashboard shows throughput, failure rate, active worker count, and queue depth -- the same high-level metrics you'd build in Grafana. The difference is that Sluice derives these from individual task records rather than pre-aggregated counters, which means you can click through from "47 failures in the last hour" to the actual list of 47 failed tasks. Grafana's advantage is custom dashboards: you can build any visualization from PromQL, whereas Sluice provides purpose-built views optimized for Celery workflows.

The bottom line

The Grafana + celery-exporter + Prometheus stack is a solid, proven approach to Celery monitoring -- especially if you already run Grafana for other infrastructure metrics. It gives you aggregate trends, threshold alerting via Alertmanager, and the flexibility of PromQL for custom queries.

Sluice gives you the individual-task layer that the Grafana stack structurally cannot: search for a specific failed task, read its traceback, retry it from the dashboard. It trades PromQL flexibility for Celery-specific depth -- and it sets up in 30 seconds instead of 2--4 hours.

For many teams, the right answer is both. Grafana for infrastructure-wide observability. Sluice for the Celery-specific debugging and management workflow that starts with "which task failed?" rather than "what does the failure rate look like?"

If you're currently flying blind on Celery -- no monitoring at all -- either option is a massive improvement over checking AsyncResult.state in a Python shell. If you're currently using Flower and frustrated by its limitations, Sluice is the direct upgrade path.

Try Sluice free at sluice.sh -- 30 seconds from pip install to seeing every task in your queues. Or read about how Celery's PENDING state actually works and what to do when your workers run out of memory.