TigerData vs AWS Aurora PostgreSQL for telemetry workloads — cost and scaling differences? | Time-Series Databases | Codeables

Telemetry workloads—metrics, events, ticks, logs—push plain Postgres in ways it wasn’t originally designed for. Both TigerData and AWS Aurora PostgreSQL start from Postgres, but they make very different trade-offs once you need 10–100k+ inserts/sec, always-on queries over months of history, and predictable costs.

Quick Answer: TigerData is Postgres extended specifically for telemetry (automatic time partitioning, row-columnar storage, compression, and tiered storage), so you keep one database as ingest and analytics scale. Aurora PostgreSQL is a highly available managed Postgres; for telemetry at serious scale, you typically pay more in compute, storage, IO, and extra services (Kafka, Kinesis, S3, Lambda, Glue) to get the same behavior TigerData bakes into the engine.

The Quick Overview

What It Is:
TigerData is a managed Postgres platform (Tiger Cloud + TimescaleDB) optimized for live telemetry: high-ingest time-series, events, and tick data with both real-time and historical queries. AWS Aurora PostgreSQL is Amazon’s managed, distributed storage engine for Postgres, focused on availability and scaling transactional workloads.
Who It Is For:
- TigerData: Teams who want Postgres as the single system for ingest, analytics, and retrieval of telemetry at high scale, without stitching together Kafka/Flink/S3/Glue.
- Aurora PostgreSQL: Teams already standardized on AWS managed services, running mostly OLTP workloads or moderate telemetry volumes where scaling is more about HA and replicas than about engine-level time-series optimization.
Core Problem Solved:
- TigerData: Plain Postgres becomes slow and expensive for large telemetry tables (index bloat, vacuum pressure, timeouts, fragile pipelines). TigerData changes the engine primitives (hypertables, columnstore, compression, tiering) so you can keep using Postgres at telemetry scale.
- Aurora PostgreSQL: Running Postgres yourself is operationally heavy (failover, backups, storage, replication). Aurora gives you a managed, highly available Postgres-compatible service with autoscaling storage and read replicas.

How It Works

At a high level, these platforms answer different questions:

TigerData: “How do we keep one Postgres that can ingest and query telemetry at 3 trillion metrics/day without splitting into OLTP + streaming + data lake?”
Aurora PostgreSQL: “How do we run Postgres in AWS with minimal ops, high availability, and easy replica scaling?”

The mechanisms reflect that:

TigerData: Postgres with time-series primitives built in

TigerData extends a single Postgres instance with TimescaleDB and engine-level features:
- Hypertables: automatic partitioning on time and key.
- Hypercore row–columnar storage: row layout for fast ingest, columnstore for fast analytics.
- Compression & tiered storage: automatic policies to compress, convert to columnstore, and move older chunks to cheap object storage.
- Lakehouse integration: ingest from Kafka/S3 and replicate to formats like Iceberg, without building fragile glue.
- Hybrid retrieval: time-series SQL plus search and vector (pgvector/pgai) in the same Postgres database.
You still write standard SQL against Postgres tables. The “magic” is in how those tables are stored, partitioned, and moved across storage tiers.
Aurora PostgreSQL: Managed Postgres with distributed storage

Aurora keeps the Postgres front-end but replaces the storage layer with a distributed, replicated volume:
- Shared storage volume that auto-scales up to 128 TB.
- Decoupled compute: primary instance + up to 15 read replicas.
- Managed backups: continuous backups to S3, point-in-time restore.
- AWS-native integration with VPC, IAM, KMS, CloudWatch, etc.
From a schema perspective, you’re still dealing with plain Postgres tables and indexes. For telemetry, you’ll usually add your own partitioning (PARTITION BY RANGE) and carefully manage indexes and retention with custom SQL or Lambda-based jobs.
Operational posture
- Tiger Cloud (TigerData):
  One service that combines engine-level telemetry optimization with managed operations: HA, backups, point-in-time recovery, read replicas, and per-plan SLAs. You scale compute and storage independently and don’t pay per-query or per-backup fees.
- Aurora PostgreSQL (AWS):
  Aurora focuses on operational convenience and high availability with well-known AWS primitives. For telemetry-specific patterns (downsampling, rollups, cold storage, streaming), you typically add more managed services and significant schema and job logic yourself.

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit for Telemetry
Automatic time-based partitioning	TigerData hypertables partition on time/key; Aurora uses plain tables unless you add native partitioning.	TigerData keeps inserts and deletes fast even at billions+ rows with no manual partition churn.
Row–columnar storage (Hypercore)	TigerData stores hot data row-wise; converts cold data to compressed columnstore.	Fast ingest and fast analytics in one table; Aurora stays row-only, so big scans get slower/costlier.
Compression & tiered storage	TigerData compresses and moves older chunks to low-cost object storage automatically.	Up to ~98% compression and lower storage costs without separate archiving pipelines.
Time-series SQL & continuous aggregates	TigerData provides 200+ functions and continuous aggregates for rollups.	Always-fresh metrics dashboards with minimal query load; Aurora needs manual materialization logic.
Lakehouse integration	TigerData streams from Kafka/S3 and replicates to Iceberg-style tables.	Replaces fragile Kafka/Flink/Glue stacks with native infrastructure.
Managed HA & transparent billing	Both offer HA and backups; TigerData emphasizes no per-query/backup/egress fees.	More predictable costs at high query volumes vs Aurora’s per-IO/storage/feature charges.

Phase-by-Phase: Telemetry Scaling Pattern

Telemetry workloads typically evolve in three phases on Postgres. Here’s how TigerData and Aurora compare in each.

Phase 1: Initial deployment (0–100M rows)
- Aurora PostgreSQL:
  - You start with a single instance, maybe 1–3 read replicas.
  - Schema looks like plain Postgres:
```
CREATE TABLE metrics (
  time        timestamptz NOT NULL,
  device_id   text        NOT NULL,
  metric_name text        NOT NULL,
  value       double precision,
  PRIMARY KEY (time, device_id, metric_name)
);
```
  - Works fine for early load. Costs are dominated by instance size and modest IO.
- TigerData:
  - You create a hypertable instead of a plain table:
```
CREATE TABLE metrics (
  time        timestamptz NOT NULL,
  device_id   text        NOT NULL,
  metric_name text        NOT NULL,
  value       double precision
);

SELECT create_hypertable('metrics', by_range('time'), by_hash('device_id'));
```
  - Same SQL semantics, but now each time window is a chunk; TigerData will auto-manage indexes and chunk sizing.
  - Ingest and queries look effectively identical to Aurora at this scale. The difference shows up as volume grows.
Phase 2: “It worked… until we hit billions of rows”

This is where vanilla Postgres (and Aurora as vanilla Postgres) hits cost and scaling friction.
- Aurora PostgreSQL pain points:
  - Index bloat & vacuum pressure:
    - Your metrics table is now 100s of GBs; B-tree indexes on (time, device_id) and (device_id, time) grow large.
    - VACUUM and ANALYZE take longer and interfere with latency.
  - Slow queries across large ranges:
    - Dashboards that scan 30 days of data start to plateau. You start to see timeouts or need heavier instances.
  - DIY partitioning and retention:
    - You introduce native partitioning:
```
CREATE TABLE metrics_y2025m01
  PARTITION OF metrics
  FOR VALUES FROM ('2025-01-01') TO ('2025-02-01');
```
    - You build maintenance jobs (e.g., via Lambda or cron) to:
      - Create/drop partitions monthly or daily.
      - DELETE or TRUNCATE old partitions.
      - Archive to S3 via UNLOAD, custom ETL, or DMS.
  - Cost pattern:
    - You scale Aurora instances up for CPU and memory; storage IO and backup traffic increase.
    - You add read replicas for analytics. Each replica is a full-priced Aurora instance.
    - You’re likely adding Kinesis / MSK, Lambda, Glue, S3, and maybe Redshift or another store for long-term analytics.
- TigerData mechanics at this phase:
  - Hypertables keep ingest and deletes cheap:
    - Each chunk behaves like a partition, but TigerData creates and drops chunks automatically as data ages.
    - Retention policies:
```
SELECT add_retention_policy('metrics', INTERVAL '90 days');
```
      handle old data without you defining new partition tables or custom jobs.
  - Hypercore row–columnar storage and compression:
    - TigerData can transform older chunks:
```
ALTER TABLE metrics SET (
  timescaledb.compress,
  timescaledb.compress_segmentby = 'device_id',
  timescaledb.compress_orderby   = 'time'
);

SELECT add_compression_policy('metrics', INTERVAL '7 days');
```
    - Hot data (last 7 days) remains row-based and uncompressed for fast writes.
    - Data older than 7 days is compressed and columnar, with up to ~98% compression in many telemetry workloads.
  - Analytics stay on the same database:
    - Continuous aggregates provide precomputed rollups:
```
CREATE MATERIALIZED VIEW metrics_5m
WITH (timescaledb.continuous) AS
SELECT
  time_bucket('5 minutes', time) AS bucket,
  device_id,
  metric_name,
  avg(value) AS avg_value
FROM metrics
GROUP BY bucket, device_id, metric_name;

SELECT add_continuous_aggregate_policy(
  'metrics_5m',
  start_offset => INTERVAL '7 days',
  end_offset   => INTERVAL '1 hour',
  schedule_interval => INTERVAL '1 minute'
);
```
    - Dashboards and alerting query metrics_5m for most use cases instead of raw data, reducing IO and compute load.
  - Cost pattern:
    - Compression and columnstore mean you store more data on cheaper tiers, with less IO per query.
    - You don’t need separate analytics databases or streaming ETL for most workloads—one Tiger Cloud service handles ingest + analytics.
Phase 3: Lakehouse, AI, and cross-system analytics

At very high scale (petabytes, trillions of events/day), you usually need lakehouse-style access and AI workflows.
- Aurora PostgreSQL approach:
  - Aurora remains your transactional / limited-history telemetry store.
  - Analytics / ML / AI move to:
    - S3 data lake via DMS, Glue, or custom ETL jobs.
    - Redshift, Athena, or Snowflake for heavy queries.
    - Extra systems for search and vector (e.g., OpenSearch, specialized vector DB).
  - You’re now running:
    - Aurora for OLTP / short history.
    - Kinesis/MSK + Lambda/Flink/Glue for streaming.
    - S3 + Redshift/Athena/Snowflake for analytics.
    - Possibly OpenSearch/Elasticsearch + a vector DB for search/AI.
  - Cost implications:
    - Multiple full-priced managed services.
    - Data movement (DMS, Glue, Lambda) and cross-service IO/bandwidth charges.
    - Operational cost of “fragile and high-maintenance” pipelines keeping schemas and state in sync.
- TigerData approach:
  - Lakehouse integration as a primitive:
    - TigerData can ingest from Kafka and S3, and replicate to Iceberg-style lakehouse tables without bespoke streaming code.
    - You can still query recent hot data directly in Postgres while lakeside systems read the same data from object storage.
  - Hybrid search & AI on Postgres:
    - Use pgvector/pgai inside the same database as telemetry, combining:
      - Time filters
      - Structured filters
      - Full-text
      - Vector similarity
    - This keeps retrieval-augmented generation (RAG), search, and telemetry aligned without shipping data into yet another system.
  - Cost implications:
    - A single Postgres-native system handles:
      - High-ingest telemetry
      - Real-time and historical analytics
      - Search & vector
      - Lakehouse replication
    - You reduce the number of managed services and data movement pipelines, which is where a lot of Aurora-based telemetry costs hide.

Cost Model Differences for Telemetry

Cost comparisons are very workload specific, but you can think of them in terms of line items and scaling behavior.

Aurora PostgreSQL cost drivers

Compute instances:
- Primary + optional read replicas, billed hourly.
- Telemetry tends to push you into larger instance classes for CPU/memory.
Storage and IO:
- Aurora storage charged per GB-month.
- IO operations charged per million requests.
- Backups from storage to S3 are included in storage pricing, but high write and read IO for telemetry can be non-trivial.
Additional services for telemetry patterns:
- Kinesis/MSK, Lambda, Glue, DMS, S3, Redshift/Athena, OpenSearch, etc.
- Each has its own per-GB, per-request, or per-hour pricing.
- Data transfer between services can add up at scale.
Operational effort:
- Designing partitioning, retention, rollups, and archival.
- Maintaining ETL and streaming glue for analytics and lakehouse.

TigerData (Tiger Cloud) cost drivers

Database service (compute + storage):
- Tiger Cloud is a single optimized Postgres instance with TimescaleDB and engine enhancements.
- Compute and storage scale independently: you can keep a moderate compute size while pushing a lot of compressed telemetry into object storage tiers.
Built-in primitives that reduce extra services:
- Automatic partitioning (hypertables) replaces custom partition management.
- Compression and tiered storage reduce raw storage and IO.
- Continuous aggregates and time-series functions offload a lot of OLAP work from ad-hoc queries.
- Lakehouse integration and search/vector reduce the need for multiple external systems.
Billing posture:
- No per-query fees, no extra charges for automated backups or ingest/egress networking within the service.
- Costs are tied primarily to provisioned compute, data volume, and optional features (HA, extra replicas).

Where teams typically see cost divergence

At low scale (≤ 100 GB, light queries):
- Costs may look similar. Aurora gives you AWS-native comfort, TigerData gives you telemetry-friendly primitives you may not fully leverage yet.
At moderate scale (100 GB – a few TB, mixed ingest + dashboards):
- Aurora: increasing instance sizes and IO, plus at least one or two extra services (streaming, data lake) come into play.
- TigerData: compression and columnstore kick in, so you store more with less IO, and you can often keep analytics on the same service.
At high scale (multi-TB+, trillions of rows):
- Aurora: total cost includes Aurora itself plus Kinesis/MSK, Glue, S3, Redshift/Athena/Snowflake, OpenSearch, and cross-service data movement.
- TigerData: the bulk of cost sits in a single Postgres-native cluster with high compression and tiered storage, plus whatever lakehouse you choose to attach. Teams like Flowco report sizable savings (e.g., 66% monthly cost reduction) by consolidating this way.

Ideal Use Cases

Best for TigerData:
- Telemetry-heavy applications where most data is time-based and append-heavy:
  - IoT sensor platforms and device fleets
  - Observability & monitoring (metrics, logs, traces)
  - Crypto and financial tick data
  - SaaS apps with event streams and product analytics
- You want:
  - One Postgres system to handle ingest, queries, search, and vector.
  - Real-time + historical analytics without standing up Kafka + Flink + Glue + Redshift.
  - Compression and tiered storage to keep long retention without runaway cost.
Best for AWS Aurora PostgreSQL:
- General-purpose Postgres workloads where telemetry is a supporting workload, not the main data model:
  - Typical web/SaaS OLTP.
  - Systems already strongly standardized on AWS managed services.
- You’re comfortable:
  - Using Aurora for OLTP plus a separate streaming and analytics stack.
  - Paying for multiple services in exchange for tight integration into AWS and managed ops.

Limitations & Considerations

TigerData limitations / considerations:
- Cloud footprint:
  - Tiger Cloud runs in specific regions and clouds listed in TigerData docs (commonly AWS, with Azure support). If you require Aurora’s full AWS region footprint or cross-region replication patterns, confirm region availability and replication options with TigerData.
- Learning the time-series primitives:
  - Hypertables, compression policies, and continuous aggregates introduce new concepts. They’re Postgres-native, but there’s a learning curve vs plain tables and indexes.
Aurora PostgreSQL limitations / considerations for telemetry:
- No built-in time-series engine:
  - You’re responsible for schema design, partitioning, retention, and rollups. Aurora doesn’t provide hypertables, columnstore, or time-series SQL primitives out of the box.
- Multi-service complexity:
  - At larger scales, you almost always add several AWS services; the architecture can become fragile and high-maintenance if not carefully designed and monitored.

Pricing & Plans (Conceptual Comparison)

Exact numbers change over time, so think in terms of patterns.

TigerData / Tiger Cloud plans:
- Typically structured around:
  - Performance / Scale / Enterprise-style tiers with increasing:
    - Compute sizes, storage tiers, and IO capacity.
    - HA options (multi-AZ), read replicas, and dedicated support.
  - Included features:
    - Automated backups and point-in-time recovery.
    - Transparent billing (no per-query fees, no charges for automated backups or internal networking).
    - SOC 2 Type II, GDPR support, and HIPAA (Enterprise) where applicable.
- Performance-style plan: Best for teams starting telemetry workloads or mid-scale fleets who need high ingest, continuous aggregates, and straightforward HA.
- Scale/Enterprise-style plan: Best for teams already at billions of rows/day, requiring strict SLAs, 24/7 support, private networking (VPC peering/Transit Gateway), and compliance guarantees.
AWS Aurora PostgreSQL pricing:
- Instance-based:
  - db.r6g/db.r7g or similar instance types, charged hourly.
  - Extra for read replicas.
- Storage and IO:
  - Per-GB-month storage, plus per-million IO operations.
  - Storage auto-scales; you don’t (usually) manage volumes.
- Add-on services:
  - Kinesis/MSK, Glue, DMS, Lambda, Redshift/Athena, OpenSearch, etc., each with its own pricing.
  - Data transfer and cross-region replication have separate line items.
- Support:
  - AWS support plans (Developer, Business, Enterprise) are account-wide and priced as a percentage of AWS bill.

Frequently Asked Questions

Is TigerData more expensive than Aurora PostgreSQL for telemetry?

Short Answer: At small scale, costs can be similar. As telemetry volume, retention, and analytics needs grow, TigerData is often less expensive in practice because it compresses more, scans less, and replaces multiple AWS services with one Postgres-native system.

Details:
If you compare a single Aurora instance vs a single Tiger Cloud service at a few tens of millions of rows, per-hour compute rates may look comparable. The divergence appears when:

You need to keep months/years of data online.
- TigerData uses compression and columnstore to reduce both storage volume and IO.
- Aurora stores everything row-wise in its distributed volume; analytics scans cost more IO and CPU.
You add analytics pipelines.
- TigerData handles rollups, retention, and many analytics queries natively.
- Aurora typically pushes you to add Kinesis/MSK, S3, Glue, and an analytics engine (Redshift/Athena/Snowflake).

When you factor in all services and data movement, teams often find TigerData cheaper for telemetry-heavy workloads, especially when they’d otherwise be paying for multiple AWS managed services.

Why not just use Aurora PostgreSQL with native partitioning for telemetry?

Short Answer: You can, and it works up to a point. But you’ll be manually recreating features (partitioning, retention, rollups, compression, archiving) that TigerData provides as engine-level primitives—and you’ll still be missing columnstore and tiered storage.

Details:
With Aurora and native Postgres partitioning, you might:

Create daily or monthly partitions.
Write Lambda or cron jobs to:
- Create/drop partitions.
- Delete old data.
- Export to S3.
Add materialized views, or rollup tables, to keep dashboards fast.

This is viable, but:

It’s fragile and high-maintenance: schema drift, job failures, and backfills are your responsibility.
You don’t get columnar storage or built-in compression tuned for time-series; historical queries over large ranges still put heavy load on row-based storage.
You still need more services (Kinesis/S3/Glue/Redshift) when analytics outgrow Aurora.

TigerData’s hypertables, compression policies, continuous aggregates, and tiered storage are designed specifically to avoid that “stitched together Kafka, Flink, and custom code” failure mode, while keeping everything Postgres-native.

Summary

For telemetry workloads, the main difference between TigerData and AWS Aurora PostgreSQL isn’t that one “supports Postgres” and the other doesn’t—they both do. The difference is what they optimize for:

Aurora PostgreSQL optimizes for managed Postgres with strong availability and AWS-native integration. For telemetry, you’ll rely on your own schema design and a growing set of AWS services to handle scale, compression, rollups, and lakehouse integration.
TigerData optimizes Postgres itself for telemetry: hypertables for automatic partitioning, Hypercore row–columnar storage, compression and tiered storage, and native time-series and retrieval features. The result is one Postgres-native system that can ingest, store, and analyze huge telemetry volumes at predictable cost.

If your main problem is “run Postgres with HA in AWS,” Aurora is a solid choice. If your main problem is “keep all our telemetry—real-time and historical—queryable in one place without building a fragile streaming stack,” TigerData is built for exactly that.

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