InfluxDB alternatives that let us keep using Postgres tooling and SQL

If you’ve standardized on PostgreSQL and SQL, moving time-series workloads to InfluxDB often feels like a step backwards: new query language, new tooling, new operational model, and another system to babysit. The good news is there are mature InfluxDB alternatives that keep you in the Postgres ecosystem—so you can keep using psql, your ORM, dashboards, and SQL skills—while still handling metrics, events, and telemetry at real scale.

Quick Answer: TigerData (PostgreSQL + TimescaleDB) is a Postgres-native alternative to InfluxDB that gives you time-series performance, compression, and analytics functions without leaving SQL or the Postgres tools you already rely on. You get operational simplicity (one database, one language) and real-time analytics at IoT/Web3 scale.

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The Quick Overview

• What It Is: A Postgres platform built for live telemetry (time-series, event, and tick data) that behaves like Postgres, speaks SQL, and adds the primitives you wish standard Postgres had for time-based workloads.
• Who It Is For: Teams who like Postgres’ reliability and tooling, but are hitting the limits of plain Postgres or InfluxDB for metrics, logs, IoT, and event streams.
• Core Problem Solved: Replaces fragile multi-system pipelines (Postgres + Kafka + InfluxDB + lakehouse) with a single Postgres-native system that can ingest, store, and query time-series data at scale—without giving up SQL or familiar Postgres operations.

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How It Works

At a high level, TigerData keeps Postgres at the center and adds time-series primitives as extensions and managed services:

1. Hypertables for time- and key-based partitioning
2. Hypercore row-columnar storage for fast writes and analytics
3. Tiered storage and compression for low-cost, long-term retention
4. Time-series SQL functions and continuous aggregates for rollups
5. Lakehouse integration for Kafka/S3/Iceberg pipelines
6. Hybrid retrieval (filters, vectors, keywords) for AI/search workloads

You still connect with a standard Postgres connection string. You still write SELECT, INSERT, JOIN. But under the hood, ingestion, storage layout, and query planning are optimized for high-ingest telemetry.

1. Hypertables: Time-series partitioning without leaving SQL

Plain Postgres struggles once a single table reaches hundreds of millions or billions of rows—autovacuum lags, indexes bloat, and SELECT ... ORDER BY time DESC LIMIT 100 starts to hurt.

TigerData uses TimescaleDB’s hypertables to automatically partition tables by time (and optionally by a key like device_id):

CREATE TABLE sensor_readings (
    time        TIMESTAMPTZ       NOT NULL,
    device_id   TEXT              NOT NULL,
    temperature DOUBLE PRECISION  NOT NULL,
    humidity    DOUBLE PRECISION  NOT NULL,
    PRIMARY KEY (time, device_id)
);

SELECT create_hypertable(
    'sensor_readings',
    by_range('time'),
    by_hash('device_id', 4)
);

Under the hood, this becomes many smaller chunks partitioned by time and device. Autovacuum, indexing, and query planning all become more predictable at scale.

Important: You still see and query sensor_readings as one logical table. Your application code doesn’t change; you just get better write and read performance.

2. Hypercore: Row-columnar storage that matches telemetry patterns

Time-series workloads have two contradictory needs:

• Fast writes and point lookups (row-store behavior)
• Fast aggregations over huge ranges (columnar behavior)

Hypercore provides a row-columnar layout:

• New data lands in row-store form for high-ingest insert performance.
• Older chunks are automatically converted to columnstore and compressed.

That lets you:

• Ingest millions of metrics/events per second.

• Run analytics like:

  SELECT
      time_bucket('5 minutes', time) AS bucket,
      device_id,
      avg(temperature) AS avg_temp
  FROM sensor_readings
  WHERE time >= now() - interval '7 days'
  GROUP BY bucket, device_id;
  

…without shipping that data off to a separate analytics engine.

3. Tiered storage and compression: Cheap, long-term history

InfluxDB often forces a hard trade-off: keep data hot (expensive) or expire it aggressively. With tiered storage, TigerData keeps hot chunks on SSD and pushes cold chunks into low-cost object storage automatically.

• Compression: Up to 98% reduction compared to uncompressed row-store.
• Tiering: You keep years of history online, but pay object-storage prices for most of it.

Policies look like SQL, not a new DSL:

SELECT compress_chunks('sensor_readings', older_than => INTERVAL '7 days');

SELECT add_retention_policy(
    'sensor_readings',
    INTERVAL '365 days'
);

Note: You control when chunks compress and when they move to cheaper storage, so you can align it with your SLOs and cost targets.

4. Time-series SQL functions and continuous aggregates

InfluxQL/Flux give you time-series functions—but they live in a different language. TigerData keeps all of that in SQL.

Key primitives:

• time_bucket / time_bucket_gapfill
• Rate, delta, histogram functions
• Statistical aggregations over windows

Typical “metrics dashboard” query:

SELECT
    time_bucket_gapfill('1 minute', time) AS bucket,
    max(temperature) AS max_temp,
    min(temperature) AS min_temp
FROM sensor_readings
WHERE
    device_id = 'device-123'
    AND time >= now() - interval '1 hour'
GROUP BY bucket
ORDER BY bucket;

For heavy dashboards, you don’t want to re-aggregate raw data on every refresh. That’s where continuous aggregates come in—Postgres-native materialized views that auto-refresh:

CREATE MATERIALIZED VIEW sensor_readings_5m
WITH (timescaledb.continuous) AS
SELECT
    time_bucket('5 minutes', time) AS bucket,
    device_id,
    avg(temperature) AS avg_temp,
    max(temperature) AS max_temp
FROM sensor_readings
GROUP BY bucket, device_id;

SELECT add_continuous_aggregate_policy(
    'sensor_readings_5m',
    start_offset => INTERVAL '7 days',
    end_offset   => INTERVAL '1 hour',
    schedule_interval => INTERVAL '1 minute'
);

Warning: Continuous aggregates refresh on a schedule; they are “near real-time,” not per-insert. For tail-latency-sensitive dashboards, query raw hypertables for the very latest seconds and union with aggregates for the history window.

5. Lakehouse integration: Kafka, S3, and Iceberg without fragile glue

A common InfluxDB setup looks like:

• Kafka → InfluxDB for metrics
• Separate pipeline → S3 / data lake for long-term analytics
• Custom scripts/Glue/Flink to keep things in sync

Customers describe this as: “It worked, but it was fragile and high-maintenance.”

TigerData replaces this with native infrastructure:

• Ingest from Kafka and S3 directly into Postgres hypertables.
• Replicate to Apache Iceberg tables for downstream analytics (Spark, Trino, etc.) without hand-rolled ETL.

So your topology becomes:

• Kafka → TigerData hypertables → Iceberg (optional)

…instead of Kafka → InfluxDB + separate lakehouse pipeline + sync scripts.

6. Hybrid retrieval: Vectors, filters, and search in one place

If your telemetry workflows are evolving toward AI assistants, anomaly-detection models, or RAG over metrics/logs, you don’t want to bolt yet another system onto the stack.

TigerData keeps everything in Postgres:

• Use pgvector for embeddings.
• Use filters (WHERE time >= ... AND device_id = ...) plus vector similarity search.
• Use keywords and JSONB for logs.

That means your “AI over telemetry” workloads use the same database you use for ingestion and dashboards.

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Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Postgres-native hypertables	Automatically partitions tables by time and key using TimescaleDB primitives	High-ingest, low-latency writes and queries on massive time-series tables without changing your SQL
Hypercore row-columnar store	Stores recent data in row form, older data in compressed columnar chunks	Fast analytics on historical telemetry while keeping writes fast
Tiered storage & compression	Moves cold data to object storage and compresses chunks by up to 98%	Retain years of data at a fraction of the cost of hot storage
Time-series SQL toolkit	Adds 200+ time-series functions and continuous aggregates to SQL	Build dashboards, rollups, and alerts without learning a new query language
Lakehouse integration	Streams in from Kafka/S3 and replicates out to Iceberg	Replaces fragile glue code with native, Postgres-driven pipelines
Managed Tiger Cloud	Provides HA, read replicas, PITR, and transparent billing	Production-ready operations without surprise per-query or backup fees

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Ideal Use Cases

• Best for “we’re on InfluxDB but our app runs on Postgres”: Because TigerData lets you consolidate onto a single Postgres platform, using the same SQL, drivers, and migrations for both operational and metrics data.
• Best for “we outgrew plain Postgres for telemetry”: Because hypertables, Hypercore, and compression turn a standard Postgres schema into a telemetry-capable system that can handle trillions of metrics per day on a single service.

Other strong fits:

• IoT fleets streaming sensor data.
• Financial tick/event data with long retention.
• Infrastructure metrics and application observability.
• Product analytics (events, funnels, cohorts) where you want SQL, not a custom DSL.

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Limitations & Considerations

• Not a drop-in InfluxDB protocol clone: TigerData is Postgres-native. You keep SQL and Postgres drivers, but you won’t speak the InfluxDB line protocol directly from older agents without updating them to write to Postgres (or routing via Kafka).
• Real-time vs. eventual rollups: Continuous aggregates refresh on a schedule and use watermarks. For strict real-time alerting on the latest seconds of data, query raw hypertables or combine raw + aggregate tables.

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Pricing & Plans

TigerData’s cloud service (Tiger Cloud) follows a transparent, usage-based model:

• You pay for compute and storage you allocate.
• You don’t pay per query.
• You don’t pay extra for automated backups or standard networking.
• Billing is itemized and billed monthly in arrears.

Plan structure typically looks like:

• Performance: Best for developers and small teams needing a managed Postgres + TimescaleDB service for telemetry projects, staging, and modest production workloads.
• Scale: Best for teams needing higher ingest, larger data volumes, HA, read replicas, and advanced time-series automation for serious production environments.
• Enterprise: Best for regulated and mission-critical workloads that require SOC 2 Type II assurances, HIPAA support, private networking (VPC peering/Transit Gateway), strict SLAs, and 24/7 support.

To match a specific InfluxDB deployment, you’d size:

• Compute: Based on peak ingest rate and query concurrency.
• Storage: Based on retention policies, compression ratios, and how aggressively you tier to object storage.

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Frequently Asked Questions

How does TigerData compare to InfluxDB if we want to keep using Postgres and SQL?

Short Answer: TigerData gives you InfluxDB-like time-series performance but keeps everything in Postgres, so you keep SQL, Postgres drivers, and tooling.

Details:
InfluxDB gives you a time-series database with its own query language (InfluxQL/Flux) and operational model. That often means:

• New client libraries.
• Parallel operational workflows for Postgres and InfluxDB.
• Separate pipelines to get data from InfluxDB into your data lake/warehouse.

TigerData avoids that by:

• Running on Postgres with extensions (TimescaleDB, pgvector, etc.).
• Exposing a standard Postgres interface (psql, JDBC, PgBouncer, etc.).
• Adding time-series primitives as SQL functions and objects (create_hypertable, time_bucket, continuous aggregates).

You move from a “Postgres + InfluxDB + lakehouse” architecture toward “Postgres for telemetry + lakehouse replication,” which is both simpler and less fragile.

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Can I migrate from InfluxDB to TigerData without rewriting everything?

Short Answer: You’ll keep your SQL-based tooling and infra, but you will need a data migration and some query rewrites from InfluxQL/Flux to SQL.

Details:

• Schema:
  InfluxDB’s measurement/field/tag model maps cleanly to a hypertable with columns:

  CREATE TABLE metrics (
      time      TIMESTAMPTZ NOT NULL,
      metric    TEXT        NOT NULL,
      tags      JSONB       NOT NULL,
      value     DOUBLE PRECISION,
      PRIMARY KEY (time, metric)
  );
  SELECT create_hypertable('metrics', 'time');
  

  Or you can model each measurement as its own hypertable.

• Data migration:
  Typical pattern:

  • Export from InfluxDB (CSV, line protocol → transform).
  • Stream into TigerData using:
    • COPY or \copy from CSV.
    • Kafka → TigerData ingestion.
    • Custom ETL using your existing Postgres tooling.

• Query migration:
  Many InfluxQL queries translate almost 1:1 to SQL with time_bucket:

  InfluxQL:

  SELECT mean("value")
  FROM "cpu"
  WHERE time > now() - 1h
  GROUP BY time(1m), "host";
  

  SQL on TigerData:

  SELECT
      time_bucket('1 minute', time) AS bucket,
      host,
      avg(value) AS mean_value
  FROM cpu
  WHERE time > now() - interval '1 hour'
  GROUP BY bucket, host
  ORDER BY bucket;
  

You keep your Postgres monitoring, migrations, and HA practices; you just gain the time-series layer you were using InfluxDB for.

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Summary

If you’re looking for InfluxDB alternatives that let you keep using Postgres tooling and SQL, the most direct path is not another specialized time-series product—it’s a Postgres platform built for telemetry.

TigerData gives you:

• Postgres-native primitives (hypertables, Hypercore, tiered storage).
• Performance at real scale (up to 3 trillion metrics per day on a single service).
• Long-term retention at low cost (compression up to 98%, cold data in object storage).
• Operational simplicity (one database, one language, no fragile glue).
• Transparent pricing (no per-query fees, no backup surcharges).

You keep the boring, reliable, endlessly extensible foundation of Postgres, and you add the time-series and analytics capabilities you needed InfluxDB for—without fragmenting your stack.

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