InfluxDB
Open SourceAbout InfluxDB
InfluxDB is a database for time series data. It is described as the database for real-time systems powering physical AI. The product captures, analyzes, and acts on high-resolution operational data in real-time. InfluxDB can be deployed on-premises, at the edge, or in the cloud. InfluxData is the company behind InfluxDB.
Products
- InfluxDB 3 Enterprise: A self-managed, fully featured deployment for on-prem, private cloud, or edge infrastructure.
- Amazon Timestream for InfluxDB: InfluxDB 3 deployed on fully managed AWS infrastructure, billed to the user's AWS account.
- Cloud Dedicated: A single-tenant, fully managed cloud deployment operated by InfluxData.
- Cloud Serverless: A pay-as-you-go, multi-tenant cloud offering for smaller workloads and early evaluation.
- InfluxDB 3 Core: An open source, self-managed engine for local development, exploration, and prototyping.
- Telegraf Enterprise: Adds a management layer for running Telegraf at scale, including centralized configuration, agent health monitoring, a visual config builder, and RBAC.
Used for
- Capturing high-rate sensor streams for real-time diagnostics and adaptive control in aerospace.
- Feeding real-time telemetry into AI models to predict equipment failures in manufacturing.
- Managing grid volatility across distributed assets using real-time sensor streams.
- Transforming and analyzing unlimited series of data in real-time.
Who it is for
- Used in the energy and utilities industry.
- Used by developers.
From InfluxDB's own website, read 2026-10-23. How we build listings.
How to evaluate Database Platforms
This is how the CIOPages Research Team evaluates this category. It is not an assessment of InfluxDB. It comes from our Enterprise Database Platforms buyer guide.
25%
Data Model & Query Fit
Match of the engine to the workload: relational integrity and joins, document/JSON flexibility, key-value latency; transaction isolation levels and ACID guarantees; SQL standard coverage and dialect quirks; secondary indexes, full-text and vector (pgvector / Atlas Search) support; foreign keys and referential integrity where you need them
20%
Consistency, Scale & HA
Single-primary vs. multi-writer; synchronous vs. async replication and resulting RPO; read-replica and sharding story; horizontal write scale and global/multi-region behavior (strong vs. eventual consistency); failover time, achievable RTO, and how partitions are handled under load
20%
Operability & Managed-Service Experience
Quality of the managed offering (RDS/Aurora, Azure SQL, Cloud SQL/AlloyDB) vs. self-managed burden; in-place and major-version upgrade path; backup/PITR, observability, and connection pooling; autoscaling and serverless options; day-2 toil — vacuum/compaction, index bloat, rebalancing — and the depth of the DBA skills it demands
15%
Licensing & Portability
License class and its real constraints: OSI open source (PostgreSQL, MySQL, Valkey, Redis 8 AGPL) vs. source-available (MongoDB SSPL, CockroachDB enterprise/BSL) vs. proprietary (Oracle, SQL Server); cloud lock-in of proprietary engines (Aurora, Spanner, DynamoDB); wire-protocol and SQL-dialect compatibility that preserves an exit; audit exposure on proprietary metrics
12%
Security & Compliance
Encryption at rest and in transit, TLS enforcement, and key management (BYOK/HSM); row- and column-level security and fine-grained RBAC; field-level / queryable encryption for sensitive data; audit logging; data-residency controls and certifications (SOC 2, ISO 27001, HIPAA, PCI, FedRAMP) on the managed service
8%
Ecosystem & Talent
Driver and ORM maturity across your languages; migration and CDC tooling (logical replication, Debezium, DMS); breadth of extensions and integrations; size and hireability of the talent pool; quality of docs and the support path — first-party vendor vs. third-party (EDB, Percona, Crunchy) for open engines
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Quick Facts
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SubcategoryDatabase Platforms
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