Valkey Database Usage Export: Capacity Planning Guide

A Valkey database usage export exists to answer a single sizing question: are you overpaying for idle RAM, or are you running so close to your memory ceiling that evictions are silently degrading your application? Instead of guessing from dashboard graphs or reacting to memory-pressure alerts after a latency spike, extracting raw telemetry gives you the empirical 95th-percentile (p95) memory and client connection metrics needed to pick the right managed tier.

For engineering teams managing steady backend workloads, capacity planning requires defensible numbers rather than point-in-time snapshots. Whether you are auditing an existing cache or evaluating a migration, this guide walks through using exported database telemetry to translate memory metrics into concrete tier decisions and establish a repeatable capacity review.

The Decision This Export Is Supposed to Answer

Most capacity planning reviews come down to a sizing decision: whether to remain on your current tier, upgrade memory allocation, or shift away from a metered pay-as-you-go provider to a fixed monthly plan. Making that call accurately requires tracking workload dynamics over time.

A proper Valkey database usage export does not extract your keys and values. It extracts operational telemetry: per-database memory consumption, active connection counts, command throughput, keyspace size, eviction counters, and p50, p95, and p99 latency percentiles. Because usage export is not a database-content export, you cannot use it to restore data or seed a replica. Its sole function is to give you the telemetry required to evaluate capacity, stability, and infrastructure spend.

Operating near your memory ceiling leaves negligible headroom for sudden key growth, connection overhead, and memory fragmentation.

How you interpret that threshold depends on your specific workload type, as detailed in the Valkey memory and eviction documentation:

Rebuildable Cache: When memory is exhausted, the engine evicts keys based on your eviction policy (such as allkeys-lru ). Headroom can sit closer to capacity (such as a many to many margin), provided your downstream datastore can absorb the cache-miss penalty during an eviction wave.
Session Store: Evictions destroy authenticated sessions, forcing active users to log in again. For session stores, engineering teams generally maintain wider headroom above p95 memory so that unexpired session keys are not prematurely evicted under burst conditions.
Rate Limiter: Keys are tiny and carry short, strict time-to-live (TTL) values. Memory scales directly with concurrent active callers rather than historical data. Here, the critical ceiling is rarely memory alone—it is connection limits and command execution latency under load.

What a Managed Valkey Dashboard Actually Exposes

Visual dashboards work well for spotting a sudden incident, but they fail when you need to calculate multi-week trends. A production dashboard for managed Valkey generally aggregates several core operational metrics:

Used Memory vs. According to Steada's pricing documentation, each database runs in a memory-capped instance that defines the provisioned capacity against which active datasets and operational overhead can be evaluated.
Connected Clients: Active file descriptors open for client connections, which consume dedicated buffer memory and risk hitting instance connection ceilings.
Commands Per Second (Throughput): Aggregate command velocity, separating read commands (such as GET and MGET) from write commands (such as SET and INCR).
Hit and Miss Ratios: The proportion of read queries that successfully return a key versus those that fall back to cold storage.
Evicted vs. Expired Keys: Counters identifying whether keys are leaving memory organically through TTL expiration or forcefully through memory reclamation.
Latency Percentiles: Rolling p50, p95, and p99 execution latencies, revealing tail-latency degradation before it causes upstream HTTP gateway timeouts.

Many managed platforms also display a projected month-end cost figure. It is vital to understand that a projected cost figure on a dashboard is merely a hypothesis derived from current usage patterns, not an immutable bill. If your throughput doubles mid-month on a provider that charges per command or per gigabyte of egress, that hypothesis immediately becomes invalid.

When monitoring capacity thresholds, evaluating utilization relative to provisioned instance limits can provide a practical operational baseline. Percentage-based alerts persist across tier adjustments, sparing your team from manually recalculating alert thresholds every time you change database sizes.

Within Steada, operational visibility is included out of the box: Steada includes per-database usage telemetry, percentile latency, a projected month-end cost labeled a hypothesis, native threshold alerting, and read-only Prometheus + CSV export on the same tier. Backend engineers can evaluate this monitoring layout directly in the Steada live dashboard preview without entering billing credentials or provisioning infrastructure.

Exporting Valkey Prometheus Metrics Without Building a Collector

Building and maintaining a dedicated sidecar exporter (such as redis_exporter) inside your application cluster introduces operational overhead: you must manage exporter credentials, allocate cluster CPU, configure scrapes, and patch dependencies. Modern managed datastores eliminate this by exposing read-only Prometheus telemetry directly over HTTPS.

In this architecture, each database provisioned within the workspace receives a dedicated scrape endpoint. Telemetry credentials are scoped strictly to read-only metrics, preventing scraper credentials from issuing commands against your data plane. Your Prometheus or VictoriaMetrics agent queries the endpoint at set intervals over TLS, ingesting system state without placing load on the engine's main execution loop.

Recommended Scrape Configuration

For capacity planning, high-frequency scrapes are unnecessary and inflate time-series database (TSDB) storage. A 30-second scrape interval offers a sensible balance: it captures transient load spikes without overloading network interfaces. Retaining this telemetry for 15 to 30 days provides sufficient historical context to calculate monthly percentiles, aligned with standard practices outlined in the official Prometheus scrape configuration reference.

scrape_configs:
  - job_name: 'valkey-usage'
    scrape_interval: 30s
    scrape_timeout: 10s
    scheme: https
    metrics_path: /metrics
    basic_auth:
      username: 'telemetry_reader'
      password: '${TELEMETRY_EXPORTER_TOKEN}'
    static_configs:
      - targets: ['metrics.steada.dev:443']
        labels:
          database_id: 'db-cache-prod-01'
          environment: 'production'
          region: 'nyc3'

While a 30-second interval is well suited for capacity modeling and identifying multi-day trends, remember that it is too coarse for diagnosing short, sub-second microbursts during incident forensics. For real-time incident triage, live dashboard metrics and client-side profiling remain essential.

Core Metric Families to Graph

When building a capacity planning dashboard in Grafana, focus on these fundamental metric families:

valkey_memory_used_bytes compared directly against valkey_memory_limit_bytes to track utilization trends.
valkey_connected_clients compared to the maximum allowed client connections to spot connection leakage before connection refusal occurs.
valkey_evicted_keys_total to track involuntary key purges over time.
valkey_expired_keys_total to verify that application TTL policies are functioning.
valkey_commands_total{class=~"read|write"} to identify shifts in workload profile.
valkey_latency_seconds{quantile="0.99"} to observe engine responsiveness under memory pressure.

A critical rule for Prometheus collection: maintain low metric cardinality. Avoid introducing per-key, per-session, or per-user labels into Prometheus scrapes. Labeling time series with dynamic values such as user IDs will rapidly expand your TSDB series count, exhaust memory, and compromise scraper reliability. Restrict your labels strictly to static metadata: database_id, plan, and region.

Consider network topography when placing your scrapers. Scraping metrics from a collector located in Europe against an engine hosted in US East incurs long-distance network latency and unneeded data transfer egress fees. To preserve measurement accuracy and avoid egress costs, run your metrics scrapers in the same cloud region or geographically proximate data center as the target database.

Valkey CSV Usage Export: The Spreadsheet Path to a Tier Decision

While Prometheus is the standard for continuous telemetry, engineering leaders and founders often need a discrete, portable snapshot when presenting infrastructure budgets, planning migrations, or justifying tier expansions to finance teams.

Telemetry exports can capture timestamped metrics across regular sampling intervals to help teams monitor operational trends. Key columns include:

timestamp_utc: The ISO-8601 sample collection timestamp.
used_memory_bytes: Exact memory allocated to datasets and buffers.
memory_limit_bytes: The active plan memory ceiling.
connected_clients: Active concurrent TCP connections.
instantaneous_ops_per_sec: Instantaneous engine command throughput.
evicted_keys_delta: Number of keys evicted since the preceding sample.
p95_latency_ms: 95th-percentile execution latency during the sample window.

The Sizing Formula

To determine an appropriate database tier from historical usage telemetry, teams can calculate baseline requirements using a standard capacity formula.

Filter out initial maintenance and setup windows from your CSV dataset.
Calculate the 95th percentile of used_memory_bytes over the 30-day period. Relying on average memory masks peak production cycles, while relying strictly on an isolated maximum may force you to over-provision for an outlier that occurred during a one-off database backfill.
Apply a many headroom buffer to the p95 figure to accommodate organic business growth and prevent eviction cascades: Target Memory Allocation = p95_used_memory_bytes × 1.30 .
Compare this final value against published tier ceilings.

Worked Example: Sizing a SaaS Cache

Suppose your SaaS application runs an active Redis-compatible cache. Evaluating historical memory metrics from an exported usage dataset helps establish actual workload demand. The calculations reveal:

Average Memory: 310 MiB
Peak Memory (single anomalous spike): 480 MiB
95th Percentile Memory (p95): 330 MiB

Applying a many safety margin yields:

330 MiB × 1.30 = 429 MiB required capacity

Evaluating this modeled 429 MiB requirement against the self-service plans published on the Steada pricing page illustrates the sizing decision across tiers:

Growth (512 MiB at $89/month): Per the published tiers, the 512 MiB allocation covers the modeled 429 MiB requirement while leaving roughly 83 MiB of operational buffer beneath the plan ceiling.

Calculating 95th-percentile memory utilization and adding an intentional headroom buffer enables teams to identify an appropriate tier without overspending on surplus capacity. Always verify current published tier ceilings and rates on the pricing page before finalizing sizing decisions.

Reading the Export Correctly: Evictions, Expiry and Restarts

Interpreting a Valkey database usage export requires distinguishing between planned TTL expirations and forced memory evictions. Confusing these two behaviors often leads teams to misdiagnose their workload health.

Expired keys represent standard cache lifecycles. Your application sets a key with an explicit TTL (such as SET session:user:42 "..." EX 86400). When the TTL lapses, the engine reclaims that memory passively during key lookup or actively via background sampling. A high expiration count indicates that your TTL strategy is working effectively.

Evicted keys , on the other hand, signal memory saturation. When memory consumption reaches your limit, the engine does not free memory immediately; instead, key eviction runs as a background process guided by your configured maxmemory policy. It identifies and discards keys that have not yet reached their TTL expiration.

If your metrics show a steadily climbing eviction rate while hit ratios decline or plateau, do not assume your caching code is defective. This metric signature indicates that your active working set has outgrown your memory tier. The cache is continuously shedding keys that your application still needs, increasing cache misses and driving redundant query load to your downstream datastore.

Accounting for Restart and Eviction Behavior

When reviewing capacity metrics, consider your application's sensitivity to restarts. Steada instances run as single, isolated Valkey processes in DigitalOcean NYC3 without automatic replica failover. Upgrading or downsizing your database tier adjusts provisioned resources and may involve restarting the instance.

Because in-memory storage clears on restart, cache contents will empty, and active session stores may require users to re-authenticate. Before executing a tier resize based on your telemetry analysis, confirm your application can handle a cold cache without overwhelming your backend datastore. Steada is for cache, sessions, rate limiting, and low-risk metadata that can roll back — not source-of-truth data without an independent recovery path. Review the Steada observability documentation to learn how to track memory re-population following instance restarts.

Turning Export Numbers into a Cost Comparison

Sizing telemetry provides the foundational dataset needed to compare managed datastore costs objectively. An accurate cost comparison requires holding operational variables constant: evaluate identical dataset sizes, identical peak throughput, matching bandwidth transfer, and realistic add-on costs across providers using dated primary sources.

Consider the pricing model differences between a flat-rate tier and a request-metered architecture such as Upstash. Upstash Fixed plan examples include:

According to the Upstash pricing page, the Fixed 250MB plan costs $10 per month (plus $5 per additional read region) and includes unlimited commands alongside a 50 GB monthly bandwidth allowance.
The Fixed 1GB plan from Upstash costs $20 per month and includes unmetered commands alongside a 100 GB monthly bandwidth allowance.
The Fixed 5GB plan from Upstash costs $100 per month and includes unmetered commands alongside a 500 GB monthly bandwidth allowance.

Under Upstash's Pay as You Go plan, commands are metered at $0.20 per 100,000 commands, storage costs $0.25 per GB after the first free gigabyte, and bandwidth is free up to 200 GB per month before incurring a $0.03 per GB fee. An optional $200/month Production Pack is offered for advanced support and isolation, though it is not strictly required for standard data persistence.

In contrast, Steada charges a flat monthly price per plan; cost does not scale per request or per command, which is the explicit contrast with request-metered providers. However, flat-rate pricing is not universally cheaper for every deployment. The crossover point depends heavily on command volume.

Workload Profile	Telemetry Profile (from Export)	Upstash PAYG Model	Steada Flat-Rate Model	Economic Fit
Low-Volume Internal SaaS Session Store	180 MiB p95 Memory, 200,000 commands/month, negligible transfer	~$0.45/month (PAYG command and storage minimums)	$49/month (Starter tier, 256 MiB limit, per pricing)	Upstash PAYG: Workloads with low, intermittent command traffic cost less on metered infrastructure.
High-Throughput API Rate Limiter	120 MiB p95 Memory, 85,000,000 commands/month, 40 GB transfer	~$170.00/month ($170 in commands alone @ $0.20/100k)	$49/month (Starter tier, 256 MiB limit, per pricing)	Steada Flat-Rate: Command-dense workloads benefit from predictable flat monthly pricing.
Steady Production Web Cache	420 MiB p95 Memory, 35,000,000 commands/month, 80 GB transfer	~$70.00/month (commands) + storage + transfer	$89/month (Growth tier, 512 MiB limit, per pricing)	Comparable: Choice depends on whether you prefer predictable billing or pay-as-you-go metering.
Active Session Store with Burst Traffic	850 MiB p95 Memory, 120,000,000 commands/month, 150 GB transfer	~$240.00/month (commands) + storage	$149/month (Scale tier, 1 GiB limit, per pricing)	Steada Flat-Rate: High sustained write throughput avoids linear billing growth.

Your Valkey database usage export reveals precisely where your application sits along this spectrum. If your export reflects low command counts and minimal memory, a pay-as-you-go model remains highly economical. Conversely, if your metrics show steady, high-frequency command execution (characteristic of high-traffic caches or rate limiters), flat-rate pricing eliminates variable billing surprises. For a deeper breakdown of this economic inflection point, evaluate your monthly throughput projections against provider tiers.

When running these comparisons, avoid using raw virtual machine costs (such as a bare DigitalOcean droplet or AWS EC2 instance) as an equivalent baseline. An unmanaged VM requires you to configure security hardening, patch operating system packages, construct backup scripts, and manage process supervisory systems yourself—operational work that managed services handle out of the box.

What the Export Cannot Tell You

While usage telemetry provides indispensable data on memory, connections, and latency, capacity planning must also account for architectural constraints that cannot be measured through metrics exports alone.

Managed hosting environments operate within specific architectural boundaries:

Availability Architecture: Steada does not offer multi-region or active-active replication. Databases run as independent, standalone instances in DigitalOcean's NYC3 data center without Redis Cluster or automatic replica failover. Furthermore, Steada does not offer a formal SLA or uptime guarantee. Workloads requiring zero-downtime guarantees across infrastructure failures require distributed enterprise architectures.
Engine Extensibility: Steada does not support Redis modules such as RediSearch, RedisJSON, or RedisBloom. If your application architecture relies on secondary indexes or specialized probabilistic structures provided by these extensions, that functionality must run on alternative infrastructure. Check the Steada command compatibility documentation before finalizing a deployment plan.
Compliance and Data Protection: Steada has no completed compliance certifications (SOC 2, HIPAA, PCI, ISO 27001) today. In addition, Steada makes no regulated-data commitments; do not store regulated or protected data such as PHI. Capacity plans should restrict deployments to transient, non-regulated application cache and session data.
Durability and Persistence Tiers: Default configurations treat instances as rebuildable in-memory caches. Durability upgrades are operator-assisted rather than an instant self-service purchase. An operator-assisted $20/month durable storage add-on is advertised, but billing, persistence, backup coverage, and restore evidence must be confirmed for the specific database before activation. Do not promise point-in-time recovery, zero data loss, automated restore, or a specific RPO/RTO without verifying the implementation.
Protocol and Wire Access: Steada does not claim full Upstash REST API parity; the default path is native RESP over TLS, with only a narrow REST compatibility preview. The default connection path is native Redis/Valkey RESP over TLS with password authentication. Applications should connect using native drivers (such as ioredis, redis-py, or go-redis) over TLS.

A Repeatable Monthly Review You Can Hand to a Teammate

Capacity planning should not be a reactive exercise triggered by an out-of-memory crash. Establishing a simple, repeatable review on the first business day of each month ensures your infrastructure scales smoothly alongside application growth.

Export Usage Telemetry: Download the Valkey CSV usage export from the dashboard or query Prometheus metrics over the relevant time window, as supported on Steada's pricing page.
Compute 95th-Percentile Values: Calculate the p95 memory utilization and identify the peak concurrent connection count across the sampling period.
Assess Memory Headroom: Add a planned safety margin (such as many to many) to your p95 memory figure. If this projected requirement approaches or exceeds your provisioned tier ceiling over consecutive weeks, queue a tier resize.
Evaluate Connection Capacity: If peak connected clients approach your tier's maximum concurrent connection ceiling, audit your application runtime before upgrading tiers. Serverless runtimes and microservices often open unpooled connections. Implementing client-side connection pooling or an intermediate proxy often resolves connection exhaustion without requiring a more expensive memory tier.
Teams can export usage columns and cost line items as a CSV for ranges up to 30 days using Steada's usage telemetry export endpoint. Retaining timestamped records provides clear documentation explaining why infrastructure tiers were adjusted or maintained over time.

Because Steada provides customer support via business-hours email without 24/7 emergency incident response guarantees, schedule planned tier changes and maintenance during regular business hours rather than during late-night production windows.

Frequently Asked Questions

Does Valkey database usage export include the actual keys and values?

No. A Valkey database usage export extracts operational performance telemetry, including memory allocation, connection counts, command throughput, eviction rates, and latency percentiles. It does not export keys