Best log pipeline for cost control + long retention?

The best log pipeline architecture for cost control and long-term retention is a Vector/Fluent Bit + Cribl Stream + Object Storage (S3/GCS) + OpenObserve/Grafana Loki stack.

This combination uses edge reduction, intelligent routing, and cheap columnar object storage to cut storage bills by 50% to 80%.

Recommended Pipeline Stack Components

Core Strategies for Long Retention on a Budget

• Tiered Storage: Keep high-priority application and security logs in a high-performance hot tier for 7–30 days, then automatically transition older logs to a cold object storage tier.
• Drop and Reduce: Use your pipeline (like Cribl Stream ) to drop repetitive health-check lines, truncate stack traces for recurring non-fatal exceptions, and turn verbose info logs into metrics.
• Parquet/Columnar Compression: Store long-term archives in compressed columnar formats (like Parquet via tools like OpenObserve ) to make searching historical data cheaper and faster.

If you want, tell me:

• What is your approximate daily log volume (GB/TB per day)?
• Which cloud provider or environment (AWS, GCP, Kubernetes) are you using?

I can give you a tailored configuration blueprint.

For cost control + long retention, the best pattern is usually not keeping all logs in an expensive searchable index. Use a tiered pipeline: cheap storage for everything, fast search only for what you actively investigate.

Recommended architecture (low cost, long retention)

Apps / Servers / Kubernetes
          |
          v
 Fluent Bit / Vector / OpenTelemetry Collector
          |
          +--------------------+
          |                    |
          v                    v
 Hot Search (7-30 days)     Archive (1-7+ years)
 Loki / OpenSearch          S3 / GCS / Azure Blob
 ClickHouse                 Parquet + compression
          |                    |
          v                    v
 Dashboards + Alerts        Athena / BigQuery / Trino

Good default stack choices

Option 1 — Best cost/performance (most teams)

• Collector: Fluent Bit or Vector
• Hot logs: Grafana Labs Loki (7–30 days)
• Archive: Object storage (S3/GCS/Azure Blob) for years
• Query old logs: Athena/Trino/BigQuery

Why:

• Loki avoids indexing every log line, reducing storage cost.
• Object storage is much cheaper for long retention than hot log databases.

Option 2 — Heavy analytics/security workloads

• Collector → Kafka → ClickHouse
• Hot: ClickHouse tables
• Cold: S3-compatible object storage

Good when you need:

• SQL queries
• Large-scale analytics
• Fast searches over billions of events

Option 3 — Easiest managed approach

Use a cloud provider's logging service only for recent operational logs, then export/archive:

• AWS: CloudWatch → S3 → Athena
• GCP: Cloud Logging → Cloud Storage/BigQuery
• Azure: Azure Monitor → Blob/Synapse

Direct long-term retention in managed log platforms can become expensive because ingestion and indexing costs often dominate.

Retention strategy I usually recommend

Biggest cost savings

• Drop noisy DEBUG logs before ingestion.
• Keep only required fields.
• Compress before storage.
• Partition archives by date/service/environment.
• Avoid indexing everything forever.
• Separate security retention from application troubleshooting retention.

A practical "sweet spot" stack for many companies is:

Fluent Bit → Loki (30 days) + S3/Glacier (7 years) + Athena/Trino for historical searches

It gives low operational cost while preserving long retention.

If you share your environment (AWS/GCP/Azure/on-prem, GB/day of logs, Kubernetes or not, and retention requirement) I can suggest a more exact design and rough monthly cost.

The best log pipeline architecture for cost control and long retention is a combination of Vector or Fluent Bit at the edge for collection, Cribl Stream as the central routing and transformation tier, and S3-compatible object storage (using columnar formats like Parquet) for long-term cold retention.

Core Architecture Components

• Vector / Fluent Bit : Lightweight, high-performance agents that run on your nodes or apps to parse, filter, and ship logs locally with minimal CPU and memory overhead.
• Cribl Stream : The industry standard observability pipeline for dropping noise, masking PII, transforming high-volume verbose logs into compact metrics, and routing hot vs. cold data streams.
• Object Storage (S3/GCS) : Cheap, durable storage bucket tier utilizing extreme compression (such as Parquet or Zstandard) for long retention compliance without paying expensive indexing fees.

Pipeline Strategy for Cost Control

If you'd like, let me know:

• Your estimated daily log volume (GB/TB per day)
• Your target retention period (e.g., 90 days vs. 7 years)
• Your current storage/search backend (e.g., Datadog, Splunk, Elasticsearch)

I can help design a custom routing and pricing breakdown for your team.

For cost control + long retention, the best log pipeline is usually not a traditional “index everything forever” stack. The winning pattern is a tiered pipeline:

Apps / Servers / Kubernetes
          |
          v
 Fluent Bit / Vector / OpenTelemetry Collector
          |
          v
   Stream buffer (Kafka / Kinesis / Pub/Sub) [optional]
          |
          +----------------+
          |                |
          v                v
 Hot searchable logs       Long-term archive
 (7-30 days)               (1-10+ years)
 Loki / ClickHouse         S3 / GCS / Azure Blob
 OpenSearch (limited)      Parquet + compression

Alek's Blog CIOPages## My recommended cost-optimized designs

Option 1 — Lowest cost at scale (my default recommendation)

Hot: Grafana Loki Archive: Object storage (S3/GCS/Azure Blob) Format: compressed chunks or Parquet

Good for:

• Kubernetes
• microservices
• high-volume logs
• 1–10 year retention

Why:

• Loki avoids expensive full-text indexing and stores logs in compressed chunks with only metadata indexed.
• Object storage gives very cheap long retention. Lifecycle policies can move old data into colder tiers.

Example retention:

• 14 days hot searchable
• 90 days warm
• 7 years archive

---

Option 2 — Best query power + reasonable cost

Hot/Warm: ClickHouse Archive: S3-compatible object storage

Good for:

• security analytics
• compliance
• large SQL-style investigations
• many fields queried

ClickHouse is attractive because columnar compression works well for structured logs and it supports storage tiering patterns.

---

Option 3 — If you are already AWS-heavy

CloudWatch / Fluent Bit
        |
        v
   Kinesis Firehose
        |
        v
       S3
        |
        +--> Athena queries
        +--> Glue catalog

Use:

• S3 Standard for recent logs
• S3 Glacier tiers for old logs

AWS specifically recommends lifecycle transitions and archival approaches for large log volumes.

---

Avoid for long retention (unless required)

Full Elasticsearch/OpenSearch indexing forever

Works well operationally, but expensive because indexes consume much more storage than raw compressed logs.

Better:

• keep 7–30 days indexed
• archive everything else

SaaS ingestion pricing traps

Tools charging by GB ingested can become very expensive when:

• debug logs are noisy
• retention is years
• many environments exist

---

Biggest cost controls (often bigger than tool choice)

1. Drop useless logs before storage

• health checks
• verbose debug logs
• duplicate Kubernetes events

2. Sample intelligently

• keep 100% errors
• keep 1–10% info/debug

3. Structure logs JSON logs compress much better than random text.
4. Separate retention classes Example:

1. Compress before archive Compression + object storage is usually the largest savings lever.

---

Practical “sweet spot” architecture

For most companies:

Fluent Bit
    |
    +--> Loki (30 days)
    |
    +--> S3 bucket
           |
           +--> Parquet/ZSTD
           |
           +--> Glacier/Deep Archive after 180 days

This gives:

• fast incident debugging
• cheap compliance retention
• predictable bills

If you share your daily log volume (GB/day), cloud provider, retention target (1/3/7 years), and whether you need full-text search, I can size a more exact pipeline.

The absolute best strategy for a log pipeline targeting strict cost control and long retention is to use a decoupled pipeline architecture that filters data early and stores logs in cloud object storage.

Paying for hot index storage (like traditional Elasticsearch or SaaS indexes) for long-term retention will quickly blow out budgets. The winning combination splits your tools into a lightweight routing pipeline and a decoupled storage layer.

The Recommended Architecture

[Log Shippers] ──> [1. Pipeline / Reduction] ──> Hot Tier (7-14 Days) ──> [2. Analytics Tool]
                                            ──> Cold Tier (Years)     ──> [3. Object Storage]

1. The Processing Pipeline (The Gatekeeper)

You need an edge-routing layer to aggregate, filter, sample, and deduplicate logs before they are sent to any storage backend.

• Cribl Stream (Enterprise King): Cribl Stream is the market leader for enterprise log cost control. It lets you strip out junk text (like empty JSON blocks, verbose debug logs, or repetitive metrics), reducing total log volume by 40% to 60% before ingestion.
• Vector (Open-Source/Lightweight): If you prefer an open-source, highly performant agent, Vector (by Datadog) handles high-throughput filtering, sampling, and multi-destination routing using minimal CPU and memory.

2. The Log Storage Backend (Long Retention)

To keep costs low for multi-month or multi-year retention, you must avoid full-text indexing on all data.

• Grafana Loki (Best for DevOps/Kubernetes): Grafana Loki acts like Prometheus for logs. It only indexes specific metadata labels (like env, service, container_name ) rather than the full log message. Because the raw text is compressed and dumped directly into cheap object storage (AWS S3, Google Cloud Storage), retention costs are next to nothing.
• Parseable / VictoriaLogs (Best for SQL/Cloud Native): Options like Parseable and VictoriaLogs utilize columnar formats and cloud object-storage economics, allowing you to use standard SQL to query deeply compressed, long-term logs directly from S3.
• Grepr (Best Data Lake Layer): Grepr serves as a smart volume reduction data lake built on Apache Iceberg. It can cut retention costs by up to 93% compared to traditional cloud monitoring platforms by tiering cold logs into massive parallel processing pools.

3. Summary Comparison Table

Implementation Best Practices

To narrow this down, please share:

• Your approximate daily log volume (e.g., 50GB/day, 2TB/day)
• Where your infrastructure is hosted (e.g., AWS, on-prem, multi-cloud)
• Whether you prefer fully-managed SaaS or self-hosted open source

If your priority is lowest cost + long retention, I’d use:

Recommended: Loki + object storage

Pipeline:

Apps / Kubernetes → Grafana Alloy → Loki → S3/GCS/Blob Storage

Why this is attractive:

• Very cheap retention: Loki stores compressed log chunks in object storage and only indexes metadata/labels, rather than heavily indexing the log body.
• Keep logs for 1–7 years in S3/GCS without paying for expensive SSD-based logging infrastructure.
• Hot/cold split: keep recent logs readily queryable, while older data lives almost entirely in object storage.
• Per-stream retention: e.g. debug logs 7 days, normal application logs 90 days, audit/security logs 1–7 years. Loki supports per-stream retention policies.
• Loki's TSDB single-store mode is the recommended architecture for current versions.

A cost-conscious retention policy

For truly long-term logs, I'd also consider ClickHouse/ClickStack if you need sophisticated full-text/SQL analytics over hundreds of TB. ClickHouse supports tiering recent data to SSD and older data to S3/GCS, with TTL-based movement/deletion.

My rule of thumb

Choose Loki if you want cheap centralized logs + Grafana + Kubernetes/cloud-native operations.

Choose ClickHouse if you want analytics-heavy querying, SQL, and very large-scale log workloads.

Avoid OpenSearch/Elasticsearch as the default for long retention if cost is the primary concern; their indexing architecture generally makes multi-year retention substantially more expensive.

If you tell me roughly GB/day (or TB/day), number of services, and desired retention (e.g. 1 year/7 years), I can sketch the cheapest architecture and estimate the monthly storage/ingestion cost.

If your priority is cost control + long retention, I’d use a tiered object-storage-first pipeline rather than keeping years of logs on SSD.

My default recommendation

Apps → OpenTelemetry/Fluent Bit/Grafana Alloy → Loki → S3/GCS → lifecycle/retention

For a mostly Kubernetes/cloud-native environment, Grafana Loki + object storage is probably the best cost/performance sweet spot. Loki indexes labels/metadata rather than every log line, compresses the log data, and stores chunks in cheap object storage such as S3/GCS.

A practical retention policy:

• 0–7 days: fast/hot access
• 7–30 days: normal object storage
• 30–365+ days: cheap object-storage retention
• Compliance/archive: separate S3/GCS archive tier if you need multi-year retention
• Delete automatically after the regulatory/business retention period

Loki's Compactor can apply retention globally or per stream/tenant, which is useful for making noisy debug logs short-lived while keeping security/audit logs much longer.

If you need powerful arbitrary log analytics

Consider ClickHouse/ClickStack instead:

OTel → ClickHouse → hot NVMe → S3/object storage → TTL

ClickHouse specifically supports moving older observability data from SSD to S3/GCS and then deleting it after a defined TTL.

I'd choose:

Key cost-control rule: don't put every log line into a fully indexed search engine. That's where long-term log bills can explode.

If you tell me roughly GB/day (or TB/day), desired retention (e.g. 1 year/7 years), AWS vs GCP/Azure, and Kubernetes vs VMs, I can give you a concrete architecture and approximate monthly cost.

The gold standard architecture for maximizing cost control and long-term retention relies on a Tiered Storage / Decoupled Pipeline pattern. Instead of indexing everything into expensive hot storage (like traditional Elasticsearch clusters or per-GB SaaS indexing models), you route data through a smart processing layer into cheap object storage.

The Ultimate Cost-Control Pipeline Architecture

- Use **Vector** or **Fluent Bit** on your nodes/Kubernetes clusters. They consume minimal CPU/memory, parse formats at the edge, and drop or sample high-volume, low-value noise (like health-check logs or repetitive debug traces) *before* it hits the network or incurs data transfer fees.[[1]](https://google.com/goto?url=CAESXQHrOzAVg3uCu97VlPVrKsgzB8QkzAjZXjc3mBQnuPtNLnaeTABmN5Z0bVe5UNpP-7WaELOVjKd_uaNl0BvI89BY9UWsuXXhDF2p4jjcZtBEU5H1JFeAgBysJjSwmA)

- Route logs through a centralized processing tier.
- **Action:** Mask PII, extract metrics from logs (convert high-volume logs into cheap metrics), and split your streams:
	- *Hot/Warm Path:* Route error/warning logs and security events to your fast analytics backend.
	- *Cold/Archive Path:* Route *all* raw logs straight to object storage.[[1]](https://google.com/goto?url=CAESYQHrOzAVMTr9n3sNCuVhssqaMNNaWDFUuJtZX7qkA-iqehmwiZSAB9ED-UvW8yApMJ2q5_TvltzLnYGKLbVTA-TRRe7Qh0J2PIhaQ1GRRlCz6nphgjmX6V9tRNNaoCo_ByM)[[2]](https://google.com/goto?url=CAESgQEB6zswFds2ExA_GMHB6ITMNFadA-1wD10QsfyAVPR15FkQEv9_nDpTMPB1sxBFaBubsKIJNKIsMufj41OJHsKMF-OrQAtkMZevSQtMKRGRhCsAi9A50ug8ZsqQK1aUP-_WUBThB9uMremgC7BbVnqXKZMoYq_DfvT5l4_4DWz9zHY)[[3]](https://google.com/goto?url=CAEScAHrOzAVxD4jeell0DPJNhJeEXmG4LA9vS3-2XfvPrv6e-WXgULaR1On4y-3o2ZkWGkv7MWfTFmb4H8uxsfp7aOu6GIoxXE9C7WRwkGSw3k7YEKamX_6-LpX9bwAVPaABWmDvdwlu2eocBWCJAIplJI)

- **Grafana Loki:** Highly cost-effective because it indexes only metadata/labels rather than full-text payloads. Paired with object storage (S3/GCS) for chunks, it keeps operational costs low for recent data.
- **ClickHouse:** Emerging as a massive favorite for ultra-low-cost, high-speed log analytics via open-source tools like **SigNoz** or custom setups, providing columnar compression that shrinks log footprints by 5x–10x.[](https://google.com/goto?url=CAEScQHrOzAVup5DAMmDaqjLG8U7DZCnOJpQ11y6C2RaMzv4XnR_g2TbpbqVC0TDbmyxBfS7wWrV9r2X8lO7GS66LX0EvLxc9VF1UmHRt7V6jbko5wsig4HQxDvzLOOylIm4rMwJxNQoJIXVEXdNrNpg8OzK) [[1]](https://google.com/goto?url=CAEScQHrOzAVup5DAMmDaqjLG8U7DZCnOJpQ11y6C2RaMzv4XnR_g2TbpbqVC0TDbmyxBfS7wWrV9r2X8lO7GS66LX0EvLxc9VF1UmHRt7V6jbko5wsig4HQxDvzLOOylIm4rMwJxNQoJIXVEXdNrNpg8OzK)[[2]](https://google.com/goto?url=CAESdgHrOzAV7I9ab9GA547jwkY7uiZOdwVBp9cf3XOrQHhpsmq066MjdE6kXx-TSz55O3Z9ct0Yqm4JquR8L4t380fhCrFQ3L9NeahVtYvKFvwOztD1hKgUbeHcOnudRRFFMZkr8iIOyeu1EkfmGDT74UW6VuR8zz0)[[3]](https://google.com/goto?url=CAESggEB6zswFQTbnga4Zj0ZcwZtG4TreOXS3OBWHlSftZVj0jYfV-HuWzmXtNkNSSpfJdohDwX-J67jK0gAZWUs0EJUQkkH91_JKpfbURY6wZ_0arq38DC0GureonYds_uDM88r_wu-vo4BGmk4DltZySndW_-dyl-WgeRHQeJb7AFrp6BB)[[4]](https://google.com/goto?url=CAESVAHrOzAVVPwKH3g6nwjtnwExL5zrhRrVtiQI4vs8ohj38ip4M4NnK1krHtoe23kigXaVN2YjaHD2wAQyy6f79AStbVvlrVpMv9JBZshmcg7P_NZJ1w)[[5]](https://google.com/goto?url=CAESUwHrOzAVINHS4Myx5RA8SJsUE0csaaTizXYJMq8wnF5GCBpjKdC3Ls0c-wHhMeXuikrcnTDuF_nauT_EoF0ibVUCFX-RCB8tTiBP4LBP7axcc_cW)

- Dump raw, compressed logs (compressed JSON or Parquet format) directly into AWS S3, Google Cloud Storage, or Azure Blob.
- Apply lifecycle policies to transition data to Glacier Flexible/Deep Archive after 30–90 days for compliance-level long retention at virtually zero active maintenance cost.[[1]](https://google.com/goto?url=CAESnwEB6zswFcQVlNfybe4jzYgkhZJn84oLmoVTTdvdTKIPiHje5tlD4aNlQRiXTWhXbyPFONlTlUSDVHsHGTJPG2VdiaUpaE0mRPcr3uz3k3gki5ikvtMHlXCBVIN1wqB5TZkwkTay7xKqZpTvorY5lbxT-bs-L7m7krGS_6PCbyC7KYGEmiQmCdC3U-FlXWSIvCPYbLf1dXtARqpqTwtiyiY)[[2]](https://google.com/goto?url=CAESggEB6zswFRtDLGGkbG7cu2oqE21YBwBe9iu7FJGmjKTxMO7O5uwB2-Ekn0tAGPvFvD88neMhYerpxqVwkLnWAAV8y6uRDl-bZSoxOd3g24vcuCpupF9NXukVDWZaAVSCo5154fRnMLWDez20gVH3G5T1ee1y-HXJmJDvA5Jt6qk13ojV)[[3]](https://google.com/goto?url=CAESTwHrOzAVjczCTb2EE9k_Ra9kVHLCWjY7cK8pyNf4yuaSuWUUYnyPtcdlQlvG7P6Rt_35Mjv0sGVRuIf9fEGXRLz1yr6Y2rneFjEp2ehwLOA)[[4]](https://google.com/goto?url=CAESVwHrOzAVWg8ldjbOvyffq0eURRCK1XCDtOcTDlhwKQV8rbxOx48FBdU3-RUe_OqzPXgZFABKV9J45wp2y_ISLXYUfr6xYcaQJU47PWkKMWZLcFgpEOAIQA)[[5]](https://google.com/goto?url=CAESWQHrOzAVNLMMRc0UVcVDUixouRUetyKuQx5Y6lOH0dbfLiY2XBjoaUQ4qhYvrYdLqyDFt2G-om11hMQshgcmGn1F_bXPpK3kRdAkUdsr2m3lMfOVK6p6ZMaH)

Top Tool Combinations for This Strategy

• Open-Source / Self-Hosted (Lowest Cost at Scale):
  Vector (Agent/Router) → Grafana Loki or ClickHouse (Hot, 7–30 days) → AWS S3 + Athena (Cold retention queryable via SQL without indexing costs).
• Vendor-Assisted / Hybrid (Best Balance of Engineering Time & Cost):
  Cribl Stream or Vector for edge-filtering and routing → A modern observability platform with tiered pricing like Coralogix (which uses in-stream analytics to avoid indexing everything) or Grafana Cloud.

To help narrow down the ideal layout, tell me:

• Approximate daily volume (GB/TB per day)
• Target retention period (e.g., 90 days vs. 7 years for compliance)
• Infrastructure preference (Fully self-hosted open source vs. SaaS/Cloud-managed)

When optimizing a log pipeline for cost control and long retention , the golden rule is simple: Do not send raw, unparsed, high-volume verbose logs directly into an expensive indexed SaaS backend.

The industry-standard architecture for solving this balances an efficient edge collector, a smart routing/filtering layer, and a tiered storage backend (hot search vs. cheap object storage).

The Ultimate Cost-Control Architecture Blueprint

1. The Edge Collector: Fluent Bit or OpenTelemetry Collector
   • Why: Fluent Bit or the OpenTelemetry Collector have a tiny memory footprint, run on your nodes/hosts, and allow you to drop noisy debug logs before they ever cross a network or incur ingestion charges.
2. The Routing & Transformation Layer: Vector
   • Why: Vector acts as an ultra-fast transformation and routing engine. It lets you parse, sample, aggregate, or strip heavy keys (like stack traces or redundant access logs) dynamically, routing different log levels to different destinations based on value.
3. The Storage Strategy (Hot / Warm / Cold Tiering)
   • Hot Tier (0–7 days): High-performance search (e.g., Grafana Loki or ClickHouse-backed backends like SigNoz or OpenObserve ) for active debugging and incident response.
   • Cold/Long Retention Tier (30–365+ days): Object storage (AWS S3, Google Cloud Storage, or MinIO) in compressed columnar or parquet formats.

Top Pipeline Combinations by Use Case

• Best Open-Source / Self-Hosted Stack (Maximum Cost Reduction):
  • Fluent Bit → Vector → Grafana Loki → S3/Object Storage
  • Why: Loki avoids full-text indexing by indexing only metadata labels, making it dramatically cheaper to run than traditional Elasticsearch/OpenSearch. Old chunks automatically roll down to cheap object storage.
• Best Modern Analytics / High-Speed Engine:
  • OTel Collector → Vector → ClickHouse → S3
  • Why: ClickHouse offers incredible compression ratios (often 5x–10x less storage than traditional solutions) and blazing-fast queries over months of historical log data at a fraction of the hardware cost.
• Best SaaS Hybrid (If you must use a commercial platform):
  • Use Vector or Logstash on-prem to sample or filter 80% of routine info/debug logs into cheap object storage, and stream only critical/error logs (warn, error, fatal ) to a commercial platform like Datadog or Mezmo.

Core Tactics to Enforce in Your Pipeline

• Dynamic Sampling: Drop 90% of repetitive health-check or 200-status-code HTTP access logs, but keep 100% of 4xx/5xx errors.
• Field Extraction & Redaction: Strip out bloated parameters, redundant timestamps, or duplicate JSON keys at the edge.
• Compression: Ensure your pipeline compresses batches using Zstandard (zstd ) or Gzip before writing to long-term storage.

To help narrow down the ideal configuration for your stack, tell me:

• What is your approximate log volume (GB/TB per day)?
• Are you deployed primarily on Kubernetes, VMs, or serverless?
• Do you have to adhere to strict compliance/audit retention (e.g., keeping logs unmodified for 1 year)?

If your priority is low cost + long retention, my default recommendation is:

Best cost-control architecture

OpenTelemetry Collector → Grafana Loki → S3/Object Storage

• Hot/recent logs: Loki, with roughly 7–30 days of fast queryability.
• Long retention: Keep Loki chunks/indexes in cheap object storage such as S3/GCS/Azure Blob rather than expensive local disks.
• Retention tiers: Give high-value logs longer retention and noisy/debug logs shorter retention.
• Compression: Loki stores logs as compressed chunks in object storage, with a relatively small index compared with systems that index every log field.
• Automatic deletion: Loki's Compactor can enforce retention and supports per-stream/per-tenant policies.

A practical policy might be:

Key cost-control trick: don't send everything into an expensive full-text search engine. Keep labels/cardinality low and put the bulk of the data in object storage.

If you want very long retention (1–7+ years), I'd make S3 the source of truth and treat Loki as the query layer rather than maintaining a large Elasticsearch/OpenSearch cluster.

Loki's current documentation recommends TSDB with object storage, and its Compactor handles retention; object-store lifecycle policies can be used as an additional safety net but should be scoped carefully.

If you tell me your approximate GB/day (or TB/day), retention period, Kubernetes vs VMs, and AWS/GCP/Azure, I can lay out the cheapest architecture and rough monthly cost.