With the coming end of life of Amazon Timestream for LiveAnalytics, Amazon and InfluxData are announcing Amazon Timestream for InfluxDB. For details about how to configure optimal storage and query performance, real-time query at scale, high-speed ingest, and high availability, you can learn more about AWS + InfluxDB.
InfluxDB vs Amazon Timestream for LiveAnalytics
A detailed comparison
Compare InfluxDB and Amazon Timestream for LiveAnalytics for time series and OLAP workloads
Updated June 12, 2026
Learn About Time Series DatabasesChoosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of InfluxDB and Amazon Timestream for LiveAnalytics so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how InfluxDB and Amazon Timestream for LiveAnalytics perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn't intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
InfluxDB vs Amazon Timestream for LiveAnalytics Breakdown
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| Database Model | Time series database |
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| Architecture | Cloud-native architecture available as a fully managed cloud service or self-managed on your own hardware |
Timestream is a fully managed, serverless time series database service that is only available on AWS. |
| License | InfluxDB 3 Core: MIT (open source). InfluxDB 3 Enterprise: commercial license. |
Closed source |
| Use Cases | Monitoring, observability, IoT, real-time analytics, Industrial AI, Aerospace |
IoT, DevOps, time series analytics |
| Scalability | Horizontally scalable with decoupled compute and storage; object storage reduces infrastructure costs significantly |
Serverless and automatically scalable, handling ingestion, storage, and query workload without manual intervention |
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Whether you are looking for cost savings, lower management overhead, or open source, InfluxDB can help.
InfluxDB Overview
InfluxDB is a time series database built for storing metrics, events, logs, and traces. InfluxData released the first version in 2013. It is the most widely deployed time series database in the world and consistently ranks #1 in the DB-Engines time series database category with a 21.60 score.
InfluxDB 3 is the most recent version of InfluxDB. Its architecture separates compute and storage, so each scales independently based on workload demands. InfluxDB 3 supports standard SQL and InfluxQL, a time-series-optimized query language with built-in functions for downsampling, windowed aggregations, and time-range filtering.
InfluxDB 3 is available in five deployment options:
- InfluxDB 3 Core: Open source, self-managed, MIT licensed.
- InfluxDB 3 Enterprise: Self-managed with enterprise capabilities including clustering, role-based access control, and automated backup and restore.
- InfluxDB Cloud Serverless: Fully managed, usage-based pricing, available across major cloud providers.
- InfluxDB Cloud Dedicated: Managed cloud on dedicated infrastructure for workloads requiring isolation or hardware-level configuration.
- Amazon Timestream for InfluxDB: InfluxDB fully managed by AWS, natively integrated
Amazon Timestream for LiveAnalytics Overview
Timestream for LiveAnalytics is a fully managed, serverless time series database service developed by AWS. Launched in 2020, Amazon Timestream for LiveAnalytics is designed specifically for handling time series data, making it an ideal choice for IoT, monitoring, and analytics applications that require high ingestion rates, efficient storage, and fast querying capabilities. As a part of the AWS ecosystem, Timestream for LiveAnalytics easily integrates with other AWS services, simplifying the process of building and deploying time series applications in the cloud. AWS also offers Timestream for InfluxDB which is a managed version of InfluxDB that is compatible with InfluxDB 2.x APIs and released in partnership with InfluxData.
InfluxDB for Time Series Data
InfluxDB is the right choice when the workload is time series by nature: data arrives continuously, records are rarely modified after they are written, queries span time ranges, and volume grows with the number of sources rather than user activity.
InfluxDB is purpose-built for these workloads:
- Infrastructure and application observability: server metrics, container telemetry, Kubernetes monitoring
- Machine learning and AI: High-frequency feature data, model performance metrics, and inference telemetry at the latency and scale ML pipelines require
- IoT and industrial sensor data: high-frequency writes from large device fleets
- Energy systems: smart meters, battery storage telemetry, renewable asset monitoring
- Network telemetry: gNMI streaming, SNMP at scale, NetFlow records
- Satellite and aerospace: High-frequency telemetry from satellites, launch vehicles, and ground systems where data volume is extreme and decisions are time-sensitive
- Financial time series: tick data, price feeds, OHLCV aggregations
At high data volumes, InfluxDB’s columnar storage and object storage backend compress time series data aggressively and store it at a fraction of the cost of in-memory or block storage.
Amazon Timestream for LiveAnalytics for Time Series Data
Amazon Timestream for LiveAnalytics is designed specifically for handling time series data, making it a suitable choice for a wide range of applications that require high ingestion rates and efficient storage. Its dual-tiered storage architecture, consisting of the memory Store and magnetic Store, allows users to manage data retention and optimize storage costs based on data age and access patterns. Additionally, Timestream supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.
InfluxDB Key Concepts
Columnar storage: InfluxDB stores data in a column-oriented format using both open source and proprietary standards for persistent storage and Apache Arrow as the in-memory representation. Columnar storage produces strong compression ratios and fast time-range reads.
Data model: InfluxDB organizes data into databases, measurements (equivalent to tables), tags (indexed identifiers used for filtering), and fields (the measured values). InfluxDB 3 supports unlimited tables and columns. Data models evolve without schema migrations or predefined column limits.
Query languages: InfluxDB supports standard SQL and InfluxQL. InfluxQL includes built-in time-series functions: gap filling, window aggregations, downsampling, and rate calculations from counter data.
Decoupled architecture: InfluxDB 3 separates ingestion, query compute, and storage into independently scalable components. Teams tune each layer to workload requirements rather than provisioning for peak across all three simultaneously.
Retention policies: Users configure retention policies that automatically expire data after a defined duration. No manual partition drops, retention scripts, or index rebuilds required.
Telegraf integration: Telegraf, InfluxData’s open-source data collection agent, connects to 400+ data sources out of the box and writes directly to InfluxDB. It is part of the standard telemetry collection stack for tens of thousands of teams worldwide.
Unlimited Cardinality: The InfluxDB 3 storage engine enables high-performance queries across tables with millions of columns without impacting query performance.
Amazon Timestream for LiveAnalytics Key Concepts
- Memory Store: In Amazon Timestream for LiveAnalytics, the Memory Store is a component that stores recent, mutable time series data in memory for fast querying and analysis.
- Magnetic Store: The Magnetic Store in Amazon Timestream for LiveAnalytics is responsible for storing historical, immutable time series data on disk for cost-efficient, long-term storage.
- Time-to-Live (TTL): Amazon Timestream for LiveAnalytics allows users to set a TTL on their time series data, which determines how long data is retained in the Memory Store before being moved to the Magnetic Store or deleted.
InfluxDB Architecture
InfluxDB 3 separates data ingestion, querying, compaction, and garbage collection into components that operate independently. This separation allows compute and storage to scale in different directions based on actual workload requirements.
Data written to InfluxDB flows through ingesters with millisecond-level latency and is immediately queryable. A background compactor consolidates new files and moves them to object storage. The query layer pulls seamlessly from both in-flight ingester data and object storage, so there is no gap between data arrival and query availability.
Object storage handles long-term persistence at low cost. Teams retain data at higher frequencies and for longer periods without driving up infrastructure costs on expensive storage tiers.
Amazon Timestream for LiveAnalytics Architecture
Amazon Timestream for LiveAnalytics is built on a serverless, distributed architecture that supports SQL-like querying capabilities. Its data model is specifically tailored for time series data, using time-stamped records and a flexible schema that can accommodate varying data granularities and dimensions. The core components of Timestream’s architecture include the Memory Store and the Magnetic Store, which together manage data retention, storage, and querying. The Memory Store is optimized for fast querying of recent data, while the Magnetic Store provides cost-efficient, long-term storage for historical data.
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InfluxDB Features
High-performance storage and querying
InfluxDB 3 is optimized for time series at every layer: ingestion, storage, and query execution. InfluxDB 3.10 delivers significantly faster query performance compared to prior InfluxDB 3 releases, with the most pronounced gains on single-series lookups, real-time telemetry queries, and metadata operations. Performance varies by workload.
Retention policies
InfluxDB automatically expires data after a configured duration. No external orchestration required.
Data compression
InfluxDB 3’s storage engine delivers strong compression ratios on time series data. Background compaction continuously consolidates smaller files into larger ones that are cheaper to store and faster to query.
Horizontal scaling and clustering
InfluxDB 3 Enterprise supports horizontal scaling and clustering, distributing data and query load across nodes for performance and fault tolerance.
Data tiering
InfluxDB 3 automatically moves data between hot and cold storage tiers. Recent data stays accessible for low-latency queries. Older data moves to object storage, where it remains queryable at lower cost without manual lifecycle management.
Row-level deletions
Users delete individual data points or subsets within a table without dropping entire tables or databases.
Auto-Distinct Value Caching
InfluxDB 3.10 automatically creates caches for metadata queries, making operations like SHOW TAG VALUES significantly faster without manual cache configuration.
Processing Engine
InfluxDB 3 runs Python code directly inside the database for real-time transformations, anomaly detection, and forecasting. Plugins trigger on a schedule, via HTTP requests, or on data write with no external processing layer required.
Amazon Timestream for LiveAnalytics Features
Serverless architecture
Amazon Timestream for LiveAnalytics serverless architecture eliminates the need for users to manage or provision infrastructure, making it easy to scale and reducing operational overhead.
Dual-tiered storage
Timestream’s dual-tiered storage architecture, consisting of the Memory Store and Magnetic Store, automatically manages data retention and optimizes storage costs based on data age and access patterns.
SQL-like querying
Amazon Timestream for LiveAnalytics supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.
Timestream for InfluxDB
For workloads that require near real-time queries with single millisecond latency AWS recommends using Timestream for InfluxDB rather than LiveAnalytics. Timestream for InfluxDB also provides compatibility with InfluxDB APIs for users who want an AWS managed service without having to update their code.
InfluxDB Use Cases
Monitoring and alerting
InfluxDB stores and processes time series data from infrastructure, applications, and devices at scale. Combined with visualization tools like Grafana, teams build real-time dashboards and threshold-based alerting without query latency degrading as data accumulates.
Machine learning and AI
InfluxDB stores the high-frequency feature data, model performance metrics, and inference telemetry that ML pipelines depend on. The built-in Processing Engine runs anomaly detection and forecasting models directly against live data without a separate compute layer.
IoT data storage and analysis
High write throughput and configurable retention policies make InfluxDB a fit for IoT deployments where sensors generate continuous data streams. Teams ingest at high frequency, retain what matters, and query across the full dataset with consistent performance.
Energy systems
InfluxDB manages telemetry from smart meters, grid infrastructure, battery storage systems, and renewable assets at the write rates and retention windows energy operators require. Cell-level monitoring, cross-site portfolio analytics, and long-horizon capacity planning all run on the same platform without architectural workarounds.
Real-time analytics
InfluxDB handles application performance monitoring, user behavior tracking, and financial data analysis in real time. SQL and InfluxQL support lets teams run complex aggregations and time-windowed queries without a dedicated analytics layer.
Infrastructure and application monitoring
InfluxDB handles the cardinality and write throughput that infrastructure monitoring generates at scale: millions of unique tag combinations across hosts, services, containers, and endpoints. Teams query recent and historical data spanning months or years without separate storage tiers or query engines.
Satellite & Aerospace
InfluxDB stores and analyzes high-frequency telemetry from satellites, launch vehicles, and ground systems where data volume is extreme and query latency affects operational decisions. Object storage tiering keeps years of mission data accessible without runaway infrastructure costs.
Industrial AI
InfluxDB ingests continuous signals from PLCs, SCADA systems, and industrial sensors at the frequencies predictive maintenance and process optimization models require. The Processing Engine runs detection and forecasting plugins in-database, reducing latency between sensor data and actionable output.
Data historian augmentation
InfluxDB extends legacy data historians by capturing the high-resolution, high-frequency process data that traditional historians compress, downsample, or age out. Open SQL and InfluxQL access frees that data from closed historian interfaces, while object storage tiering retains full-fidelity history at a fraction of the cost of expanding the existing system. Teams bridge plant-floor signals into modern analytics or ML pipelines and run Processing Engine plugins against live and archived data, modernizing without ripping out the historian they already depend on.
Amazon Timestream for LiveAnalytics Use Cases
IoT applications
Amazon Timestream for LiveAnalytic’s support for high ingestion rates and efficient storage makes it an ideal choice for monitoring and analyzing data from IoT devices, such as sensors and smart appliances.
Devops
LiveAnalytics can be used for general DevOps workloads like monitoring application health and utilization. For use cases that require real time monitoring with the lowest latency possible, AWS recommends using Timestream for InfluxDB.
Analytics
Amazon Timestream for LiveAnalytics can be used to track analytics data like web and application data. The built-in time series analytics functions can then be used to aggregate and analyze data to get valuable insights with increased developer productivity.
InfluxDB Pricing Model
InfluxDB offers several pricing options, including a free open source version, a cloud-based offering, and an enterprise edition for on-premises deployment:
- InfluxDB 3 Core: Free, open source, self-managed. Provides core time series database functionality on the InfluxDB 3 architecture.
- InfluxDB Cloud Serverless: Fully managed, multi-tenant cloud., pay-as-you-go. No infrastructure to manage. Available across major cloud providers.
- InfluxDB Cloud Dedicated: Managed deployment on dedicated infrastructure for workloads requiring isolation or hardware-level configuration control.
- InfluxDB 3 Enterprise: Self-managed enterprise deployment with clustering, role-based access control, automated backup and restore, and production support.
Amazon Timestream for LiveAnalytics Pricing Model
Amazon Timestream for LiveAnalytics offers a pay-as-you-go pricing model based on data ingestion, storage, and query execution. Ingestion costs are determined by the volume of data ingested into Timestream, while storage costs are based on the amount of data stored in the Memory Store and Magnetic Store. Query execution costs are calculated based on the amount of data scanned and processed during query execution. Timestream also offers a free tier for users to explore the service and build proof-of-concept applications without incurring costs.
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