TigerData vs AWS Aurora PostgreSQL for telemetry workloads — cost and scaling differences?

Telemetry workloads push PostgreSQL into an uncomfortable corner: high-ingest, ever-growing tables; real-time and historical queries on the same data; and strict uptime expectations. Both AWS Aurora PostgreSQL and TigerData sit on top of Postgres, but they make fundamentally different trade-offs for this kind of load—especially around cost and scaling.

Quick Answer: TigerData is a Postgres platform purpose-built for time-series and telemetry, so it scales ingestion and queries via database-level primitives (hypertables, columnar compression, tiered storage) rather than bigger instances and more replicas. Aurora PostgreSQL is an excellent general-purpose managed Postgres, but for sustained telemetry workloads it usually becomes more expensive and harder to operate as you scale, because you pay with larger nodes, more I/O, and more replicas to compensate for time-series pain.

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Quick Answer: TigerData is a Postgres platform (Tiger Cloud + TimescaleDB) optimized for telemetry and time-series data. It matters because it lets you keep standard Postgres while handling 10–1000x larger telemetry workloads at lower cost by using time-series primitives instead of bigger hardware and more replicas.

The Quick Overview

• What It Is:
  A managed Postgres platform that extends vanilla PostgreSQL with time-series primitives (hypertables, row–columnar storage, compression, tiered storage, lakehouse integration) to handle high-ingest telemetry workloads efficiently.

• Who It Is For:
  Teams running metrics, IoT, event, or tick data on Postgres (or Aurora PostgreSQL) who are hitting scaling walls—rising storage/I/O costs, bloated indexes, slow aggregates, and too many read replicas—especially when they need both live and historical access in one database.

• Core Problem Solved:
  Avoid the “just scale up Aurora and add more replicas” spiral by giving Postgres the storage and partitioning model that telemetry workloads actually need—so you store more, query faster, and spend less.

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How It Works

On paper, TigerData and Aurora PostgreSQL both look like “managed Postgres.” In practice, they solve telemetry scaling in very different layers:

• Aurora PostgreSQL focuses on cloud infrastructure: decoupled compute/storage, high-availability across AZs, and managed backups/replicas. It keeps the standard Postgres engine and expects you to handle time-series patterns (partitioning, retention, aggregation) yourself in schema and app code.
• TigerData (Tiger Cloud) keeps a familiar Postgres interface but extends the engine for live telemetry: automatic partitioning (hypertables), hybrid row–columnar storage (Hypercore), aggressive compression, and tiered storage to object storage. These are built for time-based data; you use standard SQL, but the storage engine behaves differently under the hood.

At a high level, here’s how TigerData handles the core telemetry challenges compared to Aurora PostgreSQL:

1. High ingest at scale

   • Aurora PostgreSQL:

     • Ingest performance is bounded by single-node write capacity and WAL/I/O throughput.
     • Large append-only tables plus many indexes can cause increasing write amplification.
     • To keep ingestion fast, you typically:
       • Scale up instance class (more vCPUs/RAM).
       • Trim indexes aggressively.
       • Offload some writes to other systems (e.g., stream to a warehouse/lake for historical).
     • Cost model: ingest drives instance size and I/O; bigger nodes and high write throughput increase spend.

   • TigerData:

     • Uses hypertables (automatic time + key partitioning) to keep inserts going to “hot chunks,” reducing index bloat and vacuum overhead.
     • Writes go to row-oriented segments, optimized for inserts and updates.
     • Compression policies convert older chunks to columnar and compressed form in the background, so hot ingestion remains fast.
     • Result: sustained ingest on a smaller compute footprint, because storage layout matches time-series patterns.

2. Real-time + historical queries on the same data

   • Aurora PostgreSQL:

     • A single logical table is physically heap-based, with B-tree indexes; as it grows, COUNT, GROUP BY time_bucket, or wide range scans get slower.
     • Common mitigation patterns:
       • Add more read replicas and pin reporting workloads there.
       • Use partitioning, but you manage it: CREATE TABLE ... PARTITION BY RANGE (time) and rolling partitions, plus custom retention jobs.
       • Export historical data to Redshift or a data lake for heavy analytics, keeping Aurora for the hot subset.
     • Cost impact:
       • More replicas → more instances billed.
       • Heavy analytics on Aurora → more I/O and CPU, often pushing a node class upgrade.

   • TigerData:

     • Automatic partitioning (hypertables) keeps segment sizes optimal for time range queries.
     • Columnar, compressed chunks for older data significantly reduce I/O for scans and aggregates on history.
     • 200+ time-series functions (e.g., time_bucket, time_weight, LOCF/interpolated gap filling) run directly in Postgres.
     • Optional continuous aggregates maintain rollups over large telemetry tables with policy-based refresh.
     • Outcome: you can run both real-time dashboards and multi-month analytics in the same Tiger Cloud service with sub-second queries on vast datasets, without needing a second analytics warehouse.

3. Retention, storage growth, and cost

   • Aurora PostgreSQL:

     • Storage is managed and auto-growing, but:
       • You pay per GB-month for the full uncompressed footprint.
       • Telemetry tables grow quickly; long retention directly drives cost.
       • Indexes and TOAST storage further inflate size.
     • Retention logic (TTL) is custom: DELETE or DROP PARTITION jobs, risk of long-running deletes and bloat if not carefully managed.

   • TigerData:

     • Compression can reduce historical telemetry data size by up to 98% (docs cite this for TimescaleDB/TigerData workloads).
     • Tiered storage moves cold chunks to low-cost object storage automatically; compute stays lean.
     • Policy-based retention: you define when to compress, when to move to columnstore, when to drop.
     • Result: you keep longer telemetry history at a fraction of the storage cost, with predictable behavior.

4. Scaling model

   • Aurora PostgreSQL:

     • Scale mostly via:
       • Vertical scaling: move to a larger instance class.
       • Horizontal read scaling: add more Aurora read replicas.
       • Storage: auto-expands up to the Aurora limit per cluster.
     • For telemetry:
       • You often hit write throughput or I/O limits before storage.
       • You end up with a “primary for writes, multiple replicas for reads” pattern, plus a separate analytics stack for heavy workloads.

   • TigerData:

     • In Tiger Cloud, you can independently scale compute and storage.
     • Compression and tiering keep storage growth under control; you often scale compute based on active query load, not raw data size.
     • Operational add-ons: HA, read replicas, and lakehouse integration (“Ingest from Kafka and S3, replicate to Iceberg”) are all Postgres-native workflows, not separate data stores with custom glue.

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Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Hypertables & automatic partitioning	Partition telemetry data by time (and optionally by key/device) into manageable chunks, transparently to the application.	Maintains high ingest and fast time-range queries without manual partition management or continually larger Aurora instances.
Hybrid row–columnar storage (Hypercore)	Stores recent data in row format for fast writes and updates; converts older chunks to columnar, compressed segments.	Enables sub-second analytics on months of telemetry while keeping write performance high and storage cost low.
Tiered storage & compression policies	Automatically compresses and moves cold data to low-cost object storage, managed by policies.	Keeps long-term telemetry history at a fraction of the Aurora storage footprint, reducing total cost of ownership.

From a cost-and-scaling perspective, these primitives are the core difference. Aurora gives you managed Postgres and elastic storage; TigerData changes how Postgres stores and processes telemetry so you need fewer and smaller resources to achieve the same throughput and latency.

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Ideal Use Cases

• Best for “single-system” telemetry platforms:
  Because TigerData lets you ingest, store, and query live telemetry and long history in one Postgres-native service—without exporting to a separate warehouse or cobbling together Kafka + Flink + custom pipelines. You avoid the “fragile and high-maintenance” multi-system glue that many Aurora + streaming setups evolve into.

• Best for cost-sensitive, high-scale metrics/IoT workloads:
  Because compression (up to 98%), columnar storage, and tiered storage drastically cut storage and I/O compared to uncompressed Aurora tables, so you don’t have to pay for ever-larger instances and replica fleets just to keep queries under SLA.

Aurora PostgreSQL still shines when:

• You want tight integration with the rest of AWS and your workload is a mix of transactional queries with modest telemetry volume.
• You’re already using Aurora as a system-of-record and telemetry is a secondary concern with relatively short retention.

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Limitations & Considerations

• Aurora ecosystem lock-in vs Tiger Cloud deployment model:
  Aurora is deeply integrated into AWS (IAM, CloudWatch, Lambda, etc.). Tiger Cloud runs as managed Postgres with its own console, APIs, and networking controls (VPC peering, IP allow lists), and is not positioned as an AWS-native managed service.

  • Context: If your ops model assumes everything is Aurora-native, switching means adopting Tiger Cloud’s console/CLI and networking patterns. You still connect over standard Postgres drivers, but you’re not managing it via RDS APIs.

• Operational expectations and plan choices:
  Both systems offer HA, automated backups, and point-in-time recovery, but the details differ.

  • Aurora: HA and storage durability are built into the cluster model; support SLAs depend on your AWS support plan.
  • Tiger Cloud: plans expose HA, read replicas, PITR, automated backups, SOC 2 compliance, and support SLAs explicitly.
  • Note: For regulated workloads (e.g., HIPAA), TigerData requires Enterprise; Aurora requires specific configuration and AWS agreements.

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Pricing & Plans

Exact pricing numbers change over time, but the cost shape is different.

• Aurora PostgreSQL cost drivers for telemetry:

  • Instance-hours for primary + read replicas.
  • Storage per GB-month (uncompressed) and I/O charges.
  • Cross-AZ traffic for replicas.
  • Additional analytics systems (e.g., Redshift, Athena, MSK) if you offload heavy queries or streaming.

  In practice, telemetry-heavy Aurora setups get expensive because:

  • Tables and indexes balloon; storage and I/O grow linearly with raw data.
  • You add replicas to keep real-time dashboards responsive.
  • You potentially introduce a second analytics stack for historical workloads.

• TigerData (Tiger Cloud) cost posture for telemetry:

  • Compute and storage are billed transparently; you do not pay per query or for automated backups or ingest/egress networking.
  • Compression and tiered storage significantly reduce the number of GB-month you pay for in hot storage.
  • One service often replaces:
    • Aurora primary
    • One or more Aurora replicas
    • A separate streaming + warehouse pipeline for analytics.

Within Tiger Cloud, plans typically map like this:

• Performance Plan: Best for teams needing high-ingest telemetry, real-time dashboards, and SQL-first development on a single service, with strong performance but without the full enterprise governance stack. You get:

  • Managed Postgres with TimescaleDB extension
  • Automatic partitioning, compression, time-series functions
  • High availability options and automated backups/PITR

• Scale / Enterprise Plans: Best for organizations needing large-scale telemetry (billions/trillions of metrics per day), compliance (SOC 2, GDPR, HIPAA on Enterprise), and formal SLOs/SLAs around uptime and support. You get:

  • All of the above, plus:
  • Multi-AZ HA, read replicas, private networking (VPC peering/Transit Gateway)
  • Advanced security (TLS 1.2+, mutual TLS internally, encryption at rest with per-service keys)
  • 24/7 ops with severity-based response, auditability

Because TigerData’s primitives directly reduce storage and compute needs, many teams see net lower monthly cost compared to Aurora PostgreSQL for the same telemetry workload, even if headline per-CPU or per-GB pricing appears similar.

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Frequently Asked Questions

Is TigerData cheaper than Aurora PostgreSQL for telemetry workloads?

Short Answer: For sustained telemetry workloads with meaningful history, TigerData is usually cheaper at scale because compression, columnar storage, and tiering cut storage and I/O—and you don’t need as many replicas or separate analytics systems.

Details:
Cost differences show up in three main places:

1. Storage footprint:

   • Aurora stores data in standard Postgres heap + indexes; telemetry rows and indexes balloon as retention grows.
   • TigerData compresses historical chunks by up to 98% and moves them to cheap object storage. For a dataset with 12 months of telemetry, this can translate into an order-of-magnitude reduction in paid hot storage.

2. Replica and analytics sprawl:

   • Aurora often needs multiple read replicas for dashboards + ad-hoc analytics and a separate warehouse for heavy aggregates. You pay for each system.
   • TigerData is optimized to serve those workloads from a single Tiger Cloud service, backed by columnar and compressed chunks, so you can often run with fewer replicas and no second analytics database.

3. I/O and query efficiency:

   • Aurora’s heap-scans on large tables incur more I/O for the same query; you pay in both performance and cost.
   • TigerData’s columnar scans and compression reduce physical reads, so the same query consumes fewer resources.

Your actual bill will depend on exact volume, retention, and query patterns, but structurally TigerData’s model is designed to be more cost-efficient for telemetry.

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When should I stay on Aurora PostgreSQL instead of moving to TigerData?

Short Answer: Stay on Aurora PostgreSQL if telemetry is a small, secondary workload and Aurora already meets your SLAs, or if you need tight integration with specific AWS-native features and services that are central to your architecture.

Details:
TigerData is most compelling when telemetry is a primary workload: high ingest, long retention, and serious analytics demands. If you’re in one of these situations, Aurora might still be the better fit:

• Your app’s main workload is OLTP (orders, users, catalog), and telemetry is only a small stream with short retention that fits comfortably in Aurora without elaborate partitioning.
• You rely heavily on Aurora-specific integrations (RDS Proxy, certain AWS-only features) and have no current performance pain from telemetry.
• Your org standard is “Aurora for everything,” and changing that standard is more expensive politically/operationally than the incremental Aurora cost.

On the other hand, if you’re hitting:

• Slow dashboards,
• Constant instance class upgrades,
• Growing fleets of Aurora replicas,
• Or a fragile pipeline exporting telemetry to S3 + a warehouse,

then TigerData’s Postgres-native time-series engine can simplify your stack and reduce total cost.

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Summary

Aurora PostgreSQL is a strong, general-purpose managed Postgres; it’s great when you want AWS-native infrastructure and moderate telemetry loads. But it doesn’t change PostgreSQL’s core storage model, so at telemetry scale you end up paying for the mismatch via larger instances, more replicas, and extra systems for analytics.

TigerData takes the opposite approach: keep Postgres as the interface, change the engine for telemetry. Hypertables, hybrid row–columnar storage, compression (up to 98%), and tiered storage turn Postgres into a system that comfortably handles real-time ingestion and historical analytics in one place. The result is simpler architecture and usually lower cost for serious telemetry workloads.

If you’re currently stretching Aurora PostgreSQL to keep up with metrics, IoT, events, or ticks, TigerData gives you a Postgres-native way out—without giving up SQL or the mental model you already have for Postgres.

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