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@@ -14,6 +14,7 @@ AIS consistently shows balanced I/O distribution and linear scalability across a
 * ✅ **Deploy Anywhere:** AIS runs on any Linux machine, virtual or physical. Deployment options range from a single [Docker container](https://github.com/NVIDIA/aistore/blob/main/deploy/prod/docker/single/README.md) and [Google Colab](https://aistore.nvidia.com/blog/2024/09/18/google-colab-aistore) to petascale [Kubernetes clusters](https://github.com/NVIDIA/ais-k8s). There are [no built-in limitations](https://github.com/NVIDIA/aistore/blob/main/docs/overview.md#no-limitations-principle) on deployment size or functionality.
 * ✅ **High Availability:** Redundant control and data planes. Self-healing, end-to-end protection, n-way mirroring, and erasure coding. Arbitrary number of lightweight access points.
 * ✅ **HTTP-based API:** A feature-rich, native API (with user-friendly SDKs for Go and Python), and compliant [Amazon S3 API](/docs/s3compat.md) for running unmodified S3 clients.
+* ✅ **Monitoring:** Comprehensive observability with integrated Prometheus metrics, Grafana dashboards, detailed logs with configurable verbosity, and CLI-based performance tracking for complete cluster visibility and troubleshooting. See [Observability](/docs/monitoring-overview.md) for details.
 * ✅ **Unified Namespace:** Attach AIS clusters together to provide fast, unified access to the entirety of hosted datasets, allowing users to reference shared buckets with cluster-specific identifiers.
 * ✅ **Turn-key Cache:** In addition to robust data protection features, AIS offers a per-bucket configurable LRU-based cache with eviction thresholds and storage capacity watermarks.
 * ✅ **ETL Offload:** Execute I/O intensive data transformations [close to the data](/docs/etl.md), either inline (on-the-fly as part of each read request) or offline (batch processing, with the destination bucket populated with transformed results).
@@ -127,13 +128,13 @@ Let others know your project is powered by high-performance AI storage:
 * [Overview and Design](/docs/overview.md)
 * [Getting Started](/docs/getting_started.md)
 * [Buckets and Bucket Management](/docs/bucket.md)
+* [Observability](/docs/monitoring-overview.md)
 * [Technical Blog](https://aistore.nvidia.com/blog)
 * [S3 Compatibility](/docs/s3compat.md)
 * [Batch Jobs](/docs/batch.md)
 * [Performance](/docs/performance.md) and [CLI: performance](/docs/cli/performance.md)
 * [CLI Reference](/docs/cli.md)
 * [Authentication](/docs/authn.md)
-* [Prometheus & Metrics](/docs/metrics.md)
 * [Production Deployment: Kubernetes Operator, Ansible Playbooks, Helm Charts, Monitoring](https://github.com/NVIDIA/ais-k8s)
 
 ### How to find information
 
@@ -184,7 +184,7 @@ rm $TMPF 2>/dev/null
 
 ## NOTE: to enable StatsD instead of Prometheus, use build tag `statsd` in the make command, as follows:
 ## TAGS=statsd make ...
-## see docs/metrics.md and docs/prometheus.md for more information.
+## For more information, see docs/build_tags.md and/or docs/monitoring-overview.md.
 ##
 if ! TAGS=${TAGS} AIS_BACKEND_PROVIDERS=${AIS_BACKEND_PROVIDERS} make --no-print-directory -C ${AISTORE_PATH} node; then
   exit_error "failed to compile 'aisnode' binary"
 
@@ -283,7 +283,7 @@ When running a copy or transform job between these buckets, AIStore automaticall
 
 ## 7. Monitoring and Troubleshooting
 
-There are statistics (and [Prometheus metrics](/docs/metrics.md)) to monitor all performance-related aspects including (but not limited to) rate-limiting.
+There are statistics and Prometheus metrics to [monitor](/docs/monitoring-overview.md) all performance-related aspects including (but not limited to) rate-limiting.
 
 Below are two tables — one for `GET`, another for `PUT` — that illustrate how the performance monitoring might look for an AIStore cluster under the described rate-limited scenario.
 
 
@@ -1,4 +1,4 @@
-# AIS Loader
+# AIS Loader (`aisloader`)
 
 AIS Loader ([`aisloader`](/bench/tools/aisloader)) is a tool to measure storage performance. It is a load generator that we constantly use to benchmark and stress-test [AIStore](https://github.com/NVIDIA/aistore) or any S3-compatible backend.
 
@@ -16,6 +16,12 @@ There's a large set of command-line switches that allow to realize almost any co
 
 Detailed protocol-level tracing statistics are also available - see [HTTP tracing](#http-tracing) section below for brief introduction.
 
+---
+
+**May 2025 UPDATE**: `aisloader`/StatsD integration and associated capabilities described in this document are _obsolete_. The corresponding functionality is not supported and most likely will be eventually completely removed.
+
+---
+
 ## Table of Contents
 
 - [Setup](#Setup)
@@ -416,7 +422,7 @@ format:
 * `metric_type` - can be: `gauge`, `timer`, `counter`
 * `hostname` - is the hostname of the machine on which the loader is ran
 * `loaderid` - see: `-loaderid` option
-* `metric` - can be: `latency.*`, `get.*`, `put.*` (see: [aisloader metrics](/docs/metrics.md#ais-loader-metrics))
+* `metric` - can be: `latency.*`, `get.*`, `put.*`
 
 ### Grafana
 
@@ -504,23 +510,19 @@ Detailed latency info is enabled
 
 # AISLoader Composer
 
-For benchmarking production-level clusters, a single AISLoader instance may not be able to fully saturate the load the cluster can handle. In this case, multiple aisloader instances can be coordinated via the [AISLoader Composer](/bench/tools/aisloader-composer/). See the [README](/bench/tools/aisloader-composer/README.md) for instructions on setting up. 
+For benchmarking production-level clusters, a single AISLoader instance may not be able to fully saturate the load the cluster can handle. In this case, multiple aisloader instances can be coordinated via the [AISLoader Composer](/bench/tools/aisloader-composer/). See the [README](/bench/tools/aisloader-composer/README.md) for instructions on setting up.
 
 
 ## References
 
-For documented `aisloader` metrics, please refer to:
-
-* [aisloader metrics](/docs/metrics.md#ais-loader-metrics)
-
-The same readme (above) also describes:
+For AIS observability (including CLI, Prometheus, and Kubernetes integration), please see:
 
-* [Statistics, Collected Metrics, Visualization](/docs/metrics.md)
+* [AIS Observability Overview](/docs/monitoring-overview.md)
 
 For [StatsD](https://github.com/etsy/statsd) compliant backends, see:
 
 * [StatsD backends](https://github.com/statsd/statsd/blob/master/docs/backend.md#supported-backends)
 
 Finally, for another supported - and alternative to StatsD - monitoring via Prometheus integration, see:
 
-* [Prometheus](/docs/prometheus.md)
+* [Prometheus](/docs/monitoring-prometheus.md)
@@ -138,8 +138,8 @@ Use `ais show performance` and its variations in combination with `ais show job`
 
 ### See also
 
-* [Observability](/docs/metrics.md)
-* [Prometheus](/docs/prometheus.md)
+* [Observability](/docs/monitoring-overview.md)
+* [Prometheus](/docs/monitoring-prometheus.md)
 
 ## `ais show job`
 
@@ -638,3 +638,11 @@ ais show log OqlWpgwrY --severity=warning
 ais show log OqlWpgwrY --severity=w | less
 ```
 
+## Related Documentation
+
+- [Introduction to AIS observability](/docs/monitoring-overview.md)
+- [Configuring, accessing, and utilizing AIS logs](/docs/monitoring-logs.md)
+- [Prometheus integration](/docs/monitoring-prometheus.md)
+- [Metrics reference](/docs/monitoring-metrics.md)
+- [Visualizing AIS metrics with Grafana](/docs/monitoring-grafana.md)
+- [Working with Kubernetes monitoring stacks](/docs/monitoring-kubernetes.md)
@@ -1,4 +1,4 @@
-AIStore supports distributed tracing via OpenTelemetry (OTEL), enhancing its observability capabilities alongside existing extensive [metrics and logging features](/docs/metrics.md).
+AIStore supports distributed tracing via OpenTelemetry (OTEL), enhancing its observability capabilities alongside existing extensive [metrics and logging features](/docs/monitoring-overview.md).
 Distributed tracing enables tracking client requests across AIStore's proxy and target daemons, providing better visibility into the request flow and offering valuable performance insights
 
 For more details:
 
@@ -119,10 +119,12 @@ redirect_from:
 
 ## Observability and Monitoring
 
-- [Prometheus & Metrics](/docs/metrics.md)
-- [Prometheus](/docs/prometheus.md)
-- [Reference: all supported metrics](/docs/metrics-reference.md)
-- [CLI: `ais performance` command](/docs/cli/performance.md)
+- [Introduction to AIS observability](/docs/monitoring-overview.md)
+- [Configuring, accessing, and utilizing AIS logs](/docs/monitoring-logs.md)
+- [Prometheus integration](/docs/monitoring-prometheus.md)
+- [Metrics reference](/docs/monitoring-metrics.md)
+- [Visualizing AIS metrics with Grafana](/docs/monitoring-grafana.md)
+- [Working with Kubernetes monitoring stacks](/docs/monitoring-kubernetes.md)
 
 ## Developer Resources
 
 
@@ -241,7 +241,7 @@ $ make aisloader # build aisloader tool
 $ aisloader -bucket=ais://abc -duration 2m -numworkers=8 -minsize=1K -maxsize=1K -pctput=100 --cleanup=false # run aisloader for 2 minutes (8 workers, 1KB size, 100% write, no cleanup)
 ```
 
-#### Step 4: Run iostat (or use any of the multiple [documented](/docs/prometheus.md) ways to monitor AIS performance)
+#### Step 4: Run iostat (or use any of the multiple [documented](/docs/monitoring-prometheus.md) ways to monitor AIS performance)
 
 ```console
 $ iostat -dxm 10 sda sdb
@@ -645,7 +645,8 @@ $ AIS_BACKEND_PROVIDERS="aws azure gcp oci" MODE="debug" make node
 
 # 5) cloud backends, debug, statsd
 ## Note: if `statsd` build tag is not specified `aisnode` will get built with Prometheus support.
-## For more information (including the binary choice between StatsD and Prometheus), please see docs/metrics.md and docs/prometheus.md
+
+## For AIS observability (including CLI, Prometheus, and Kubernetes integration), please see docs/monitoring-overview.md
 $ TAGS="aws azure gcp statsd debug" make node
 
 # 6) statsd, debug, nethttp (note that fasthttp is used by default)
 
@@ -19,9 +19,10 @@ AIS consistently shows balanced I/O distribution and linear scalability across a
 * ✅ **Deploy Anywhere:** AIS runs on any Linux machine, virtual or physical. Deployment options range from a single [Docker container](https://github.com/NVIDIA/aistore/blob/main/deploy/prod/docker/single/README.md) and [Google Colab](https://aistore.nvidia.com/blog/2024/09/18/google-colab-aistore) to petascale [Kubernetes clusters](https://github.com/NVIDIA/ais-k8s). There are [no built-in limitations](https://github.com/NVIDIA/aistore/blob/main/docs/overview.md#no-limitations-principle) on deployment size or functionality.
 * ✅ **High Availability:** Redundant control and data planes. Self-healing, end-to-end protection, n-way mirroring, and erasure coding. Arbitrary number of lightweight access points.
 * ✅ **HTTP-based API:** A feature-rich, native API (with user-friendly SDKs for Go and Python), and compliant [Amazon S3 API](/docs/s3compat.md) for running unmodified S3 clients.
+* ✅ **Monitoring:** Comprehensive observability with integrated Prometheus metrics, Grafana dashboards, detailed logs with configurable verbosity, and CLI-based performance tracking for complete cluster visibility and troubleshooting. See [Observability](/docs/monitoring-overview.md) for details.
 * ✅ **Unified Namespace:** Attach AIS clusters together to provide fast, unified access to the entirety of hosted datasets, allowing users to reference shared buckets with cluster-specific identifiers.
 * ✅ **Turn-key Cache:** In addition to robust data protection features, AIS offers a per-bucket configurable LRU-based cache with eviction thresholds and storage capacity watermarks.
-* ✅ **Custom ETL Offload:** Execute I/O intensive data transformations [close to the data](/docs/etl.md), either inline (on-the-fly as part of each read request) or offline (batch processing, with the destination bucket populated with transformed results).
+* ✅ **ETL Offload:** Execute I/O intensive data transformations [close to the data](/docs/etl.md), either inline (on-the-fly as part of each read request) or offline (batch processing, with the destination bucket populated with transformed results).
 * ✅ **Existing File Datasets:** Ingest file datasets from any local or remote source, either on-demand (ad-hoc) or through asynchronous [batch](/docs/overview.md#promote-local-or-shared-files).
 * ✅ **Data Consistency:** Guaranteed [consistency](/docs/overview.md#read-after-write-consistency) across all gateways, with [write-through](/docs/overview.md#write-through) semantics in presence of [remote backends](/docs/overview.md#backend-provider).
 * ✅ **Small File Optimization:** AIS supports TAR, ZIP, TAR.GZ, and TAR.LZ4 serialization for batching and processing small files. Features include [initial sharding](https://aistore.nvidia.com/blog/2024/08/16/ishard), distributed shuffle (re-sharding), appending to existing shards, listing contained files, and [more](/docs/overview.md#shard).
@@ -122,13 +123,13 @@ PyTorch integration is a growing set of datasets (both iterable and map-style),
 * [Overview and Design](/docs/overview.md)
 * [Getting Started](/docs/getting_started.md)
 * [Buckets and Bucket Management](/docs/bucket.md)
+* [Observability](/docs/monitoring-overview.md)
 * [Technical Blog](https://aistore.nvidia.com/blog)
 * [S3 Compatibility](/docs/s3compat.md)
 * [Batch Jobs](/docs/batch.md)
 * [Performance](/docs/performance.md) and [CLI: performance](/docs/cli/performance.md)
 * [CLI Reference](/docs/cli.md)
 * [Authentication](/docs/authn.md)
-* [Prometheus & Metrics](/docs/metrics.md)
 * [Production Deployment](https://github.com/NVIDIA/ais-k8s)
 
 ### How to find information
Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-AIStore supports distributed tracing via OpenTelemetry (OTEL), enhancing its observability capabilities alongside existing extensive [metrics and logging features](/docs/metrics.md).`
	`1`	`+AIStore supports distributed tracing via OpenTelemetry (OTEL), enhancing its observability capabilities alongside existing extensive [metrics and logging features](/docs/monitoring-overview.md).`
`2`	`2`	`Distributed tracing enables tracking client requests across AIStore's proxy and target daemons, providing better visibility into the request flow and offering valuable performance insights`
`3`	`3`
`4`	`4`	`For more details:`