StorageArchitectureCost

Data Tiering for High‑Volume Claims Storage: When to Use PLC SSDs vs Cloud Object Storage

aassurant

2026-02-06

10 min read

Practical guidelines for using PLC SSDs, hybrid tiers and cloud object storage to balance performance and cost for high-volume claims imaging.

Hook: When claims imaging growth collides with cost and latency

High-volume claims operations in 2026 face a familiar and growing tension: imaging pipelines and AI-driven analytics demand low latency and high IOPS, while compliance and retention policies push massive volumes into cheaper long-term storage. The result is spiraling operating cost, brittle legacy stacks, and slower time-to-decision — exactly where business buyers and small insurers cannot afford delays or budget surprises.

This article gives practical, implementable guidelines for balancing performance and cost using PLC SSDs (on‑prem), cloud object storage, and hybrid tiers. It focuses on claims imaging and high-throughput processing workflows, with decision criteria, architecture patterns, TCO examples and a rollout checklist you can use today.

Executive summary — the one-paragraph decision

Use on-prem PLC SSDs or NVMe tiers as a dedicated hot processing cache when latency, concurrent IOPS and ingest bursts directly impact adjudication or fraud-detection SLAs. Offload canonical copies, warm datasets and long-retention archives to cloud object storage for durability, compliance and elastic capacity. Implement metadata-driven lifecycle rules and an immutable archive layer to meet regulatory requirements while minimizing egress and storage cost.

Quick decision checklist

Choose PLC SSDs when per-claim processing latency & concurrent throughput directly affect settlements, fraud detection or customer SLAs.
Choose cloud object storage when retention, global access, durability and analytics integration matter more than ultra-low latency.
Use a hybrid tier: PLC for hot, on-prem object gateway or cloud for warm, and cloud archive for long-term/immutable retention.

Why 2026 is a turning point

Two forces shifted the economics and design choices between late 2025 and early 2026:

PLC SSD viability: Manufacturer techniques released in late 2025 (notably improvements announced by major flash vendors) reduced the cost-per-GB gap and improved PLC endurance and error management. That makes dense NVMe tiers more economical for hot caches than previously possible.
Cloud storage evolution: Object stores introduced smarter lifecycle tiers, automated retrieval optimizations and better integration with ML pipelines for image analytics. At the same time, cloud providers expanded regional sovereignty options to meet insurance compliance needs, reducing the legal friction for moving claims data to cloud archives.

PLC SSDs in 2026 — strengths, constraints and best uses

What changed technically

PLC (5‑bits-per-cell) increases density dramatically versus TLC/Q SLC, lowering $/GB for NVMe form factors. Recent controller and firmware advances improve write amplification mitigation, error correction and adaptive refresh strategies. Practically, PLC enables larger on‑prem NVMe pools at price points closer to cloud cold tiers while still delivering very high IOPS and low latency.

When to use PLC SSDs

Ingest-heavy pipelines: If your claims imaging pipeline receives thousands of concurrent uploads and needs immediate processing (OCR, visual fraud detection, auto-triage), local PLC NVMe reduces bottlenecks.
Low-latency adjudication: Real-time decisioning or interactive claims adjuster consoles require consistent sub-10ms reads where cloud round-trip hurts SLAs.
Edge processing: Local offices, catastrophe response units, or mobile claims vans where cloud connectivity is intermittent benefit from dense on-prem NVMe.

Operational and lifecycle limits to plan for

Endurance: PLC has lower program/erase cycles than SLC/TLC. Use wear-leveling, overprovisioning and monitoring (SMART/TBW tracking).
Data protection: Combine local RAID/erasure coding with fast replication to cloud to avoid on-prem hardware-death scenarios.
Cost of scale: Even with PLC price improvements, scaling past a few PB on-prem increases facility, power and management costs.

Cloud object storage in 2026 — strengths, tradeoffs and when it wins

Core strengths

Elastic capacity: Virtually unlimited storage for imaging datasets and long retention at progressively lower prices.
Persistence and durability: 11‑9s class durability, cross-region replication and immutable/WORM storage for regulatory retention.
Analytics integrations: Native ML and search pipelines can access object stores directly — reducing extract-transform costs and simplifying model training on historical claims imagery.

Tradeoffs

Access latency: Object read latency and small IOPS costs (per-request) make cloud less suitable for ultra-low-latency, small-file random access unless complemented by a cache.
Egress and retrieval cost: Frequent retrieval from deep-archive tiers or high egress out of cloud regions can erode the cost advantage.
Regulatory geography: Some jurisdictions still prefer on‑prem or sovereign clouds for sensitive PII — hybrid patterns help mitigate this.

Recommended hybrid architecture patterns

Three patterns cover most insurer needs in 2026. Mix and match them depending on SLAs, scale and regulatory constraints.

Pattern A — Hot PLC + Canonical Cloud

Best for fast processing with cloud-backed durability.

  [Ingest layer] --> [PLC NVMe Hot Cache / Local Processing] -->
                              | (write-through/async) 
                              v
                      [Object Storage (cloud) - canonical copy]

Use write-through for critical claims so canonical copy exists immediately in cloud; use async replication for higher throughput where short window risk is acceptable.
Keep metadata and small indices in a distributed DB for search; store images as objects with fingerprinting.

Pattern B — On-prem object gateway + Cloud archive

Best where data sovereignty or private connectivity is required.

  [Ingest] --> [Local Object Gateway (S3-compatible)] --> [Cloud Object Store (Warm/Archive)]

Local gateway provides S3 APIs for existing pipelines and implements lifecycle rules to tier to cloud.
Use MinIO or vendor gateways with native sync/async policies.

Pattern C — Edge-first PLC + Nearline & Archive in Cloud

Best for distributed operations (field adjusters, catastrophe response).

  [Field Capture] --> [Local PLC Node for pre-processing] --> [Regional Cloud Warm] --> [Cloud Archive]

Local PLC does object recognition, handoff to regional warm object storage for team access, and then lifecycle to archive.

Data lifecycle rules and operational policies for claims imaging

Define lifecycle based on access frequency, business value and compliance. Use three axes: time, access and governance.

Suggested lifecycle policy

Immediate processing (0–7 days): store on PLC NVMe and in-memory indices for fastest response.
Nearline (7–90 days): push optimized copies to cloud warm tier (S3 Standard/Hot) for adjuster access and ML retraining.
Archive (90+ days or policy-based): move to low-cost archive (Glacier Deep Archive / Archive) with immutability/WORM flags for compliance windows.

Metadata & indexing rules

Store small, denormalized metadata in a fast DB (Elasticsearch/managed equivalent) and keep object keys pointing to object storage.
Use content-addressable fingerprints (SHA256) to deduplicate identical uploads across claims and reduce storage footprint.

Security, compliance and auditability

Insurance data requires both robust technical controls and provable process controls.

Encryption: Encrypt at rest (on‑device and in cloud) and in transit. Manage keys via HSM/KMS with separation of duties.
Immutability & retention: Use WORM/immutable bucket features for regulated retention. Log all lifecycle transitions for eDiscovery.
Provenance: Persist chain-of-custody metadata — uploader ID, timestamp, processing steps, model versions used for AI decisions.
Access controls: Enforce least privilege using IAM, tokenized object URLs for temporary access, and DLP scans on ingress.

Cost & TCO modeling — a practical example (3‑year comparison)

Below is a simplified, transparent example to help you model options. Replace numbers with your supplier quotes and local OPEX assumptions.

Assumptions (example)

Dataset: 1 PB of claims imaging data active at start
On‑prem PLC NVMe cost (capex): $150/TB (controller, chassis, SSD). Add 20% overhead for networking/servers.
Cloud object storage cost: $0.023/GB-month (S3 Standard equivalent) = $23/TB-month
Archive tier cloud (deep archive) average: $1/TB-month (approximate deep archive pricing/assumption)
Operational OPEX for on‑prem (power, cooling, space, staff): $30k/year for 1PB node (example)

3‑year cost estimates (example)

On‑prem (full 1PB on PLC):

CapEx: 1,000 TB × $150/TB = $150,000
Hardware overhead: +20% = $30,000
OPEX (3 years): $30,000 × 3 = $90,000
Total 3‑yr: $270,000 (plus replacement/refresh risk & admin)

Cloud (canonical in S3 standard for 1PB for 3 years):

Monthly: 1,000 TB × $23/TB-month = $23,000/month
3‑yr: $23,000 × 36 = $828,000 (warm tier)
If 80% of data is archived to deep archive after 90 days: effective 3‑yr cloud cost ≈ $? (complex — use lifecycle to calculate)

Interpretation:

Keeping all active data on PLC looks cheaper in raw 3‑year math; but PLC capex includes replacement and scaling risk, and does not include long‑term immutability features.
Cloud is more expensive for warm, always‑on copies but provides retention features, region replication and removes hardware management.
Mixed model (hot PLC for 5–10% active working set + cloud archive for the remainder) often yields the best TCO while meeting SLA needs.

Actionable rollout checklist — pilot to production

Measure current workload: capture ingest rate (GB/s), IOPS, concurrent users and latency percentiles over 30 days.
Define SLAs: day-zero ingest SLA, 95/99 latency targets for retrieval, RPO/RTO for failure scenarios.
Map data lifecycle: classify images by business value, access frequency and retention rules.
Pilot hot tier: deploy PLC NVMe node sized for the active working set (start at 5–10% of total footprint) with write-through to cloud.
Instrument & monitor: implement TBW & SMART monitoring, request cost telemetry for cloud object access, and track dedupe rates.
Run cost & risk simulation: model egress and retrieval patterns to ensure archive retrievals won’t blow budget.
Iterate policies: tune lifecycle thresholds, compression/dedupe ratios, and pipeline batching to reduce small-object inefficient workloads.

KPIs and metrics to track

Average ingest latency and 99th percentile processing latency.
Active working set % vs total stored PB.
TBW and drive replacement events (for PLC nodes).
Cloud retrieval costs and number of GET requests.
Deduplication rate and storage reduction from compression.
Compliance audits passed and retention policy enforcement events.

Practical pitfalls and how to avoid them

Pitfall: Putting small files directly into cloud warm tier without aggregation. Fix: bundle small files into composite objects or use metadata pointers to reduce per‑request costs.
Pitfall: Overestimating PLC endurance. Fix: simulate write profiles and overprovision by 20–40% depending on workload.
Pitfall: Unexpected egress spikes during audits or large-scale eDiscovery. Fix: pre-stage critical datasets to a warm tier and negotiate retrieval pricing or use provider-native eDiscovery features.

Closing — pragmatic next steps for insurance operations

In 2026, the right mix of PLC SSDs and cloud object storage gives insurers the ability to process claims faster, reduce operational cost, and meet strict compliance regimes. The best approach is hybrid: use dense on‑prem NVMe (PLC) as a purpose-built hot tier for real-time workloads, and move canonical/warm/archival copies to cloud object stores using metadata-driven lifecycle rules.

Action now: measure your working set, run a 90‑day PLC hot‑cache pilot, and tier the remainder to cloud archive — you’ll cut latency where it matters and cap long‑term storage costs.

Immediate checklist (3 items)

Run a 30-day measurement of ingest and access patterns.
Deploy a PLC NVMe hot cache sized for 5–10% active working set with write-through to cloud.
Create lifecycle rules: 0–7 days (hot), 7–90 days (warm), 90+ days (archive/immutable).

If you want a tailored TCO and architecture plan for your claims workloads — including a 3‑year cost model, PLC sizing and cloud tiering rules — contact our solutions team at assurant.cloud for a free assessment and pilot blueprint.

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