By Tiger Data Team
Updated at Apr 7, 2026
This guide compares 8 managed time-series database services on the criteria that matter for production workloads: pricing model, operational overhead, query capabilities, scalability, and ecosystem fit.
The options look different in 2026 than they did a year ago. Amazon deprecated Timestream LiveAnalytics (closed to new customers as of June 2025), InfluxDB shipped a ground-up rewrite in version 3.0, and the field of hosted services has expanded. If you're evaluating managed TSDBs this year, the decision has changed.
We built this from the perspective of teams evaluating hosted services. If you don't want to run database infrastructure yourself, this guide is for you. For self-hosted comparisons, see our Best Time-Series Databases Compared guide.
One note on perspective: Tiger Data offers Tiger Cloud, our managed service. We say that upfront. The rest of this comparison is written to be useful to engineers evaluating their options, including cases where Tiger Cloud is not the right fit.
A managed time-series database is a cloud-hosted database service where the provider handles infrastructure operations on your behalf: provisioning, scaling, backups, and security patches. Also called a cloud time-series database, it is optimized for time-ordered data: sensor readings, application metrics, financial ticks, and IoT telemetry.
"Time-series optimized" means the database is built around a few core assumptions: data arrives in chronological order, queries filter by time range, older data is queried less frequently than recent data, and write throughput matters as much as read performance. To handle these patterns efficiently, managed TSDBs typically offer automatic time-based partitioning, built-in compression for historical data, and tools for pre-aggregating query results so dashboards stay fast as data accumulates.
"Managed" means provisioning, scaling, backups, high availability, and security patches are the provider's responsibility, not yours. In practice, the degree of management varies significantly between services. Some providers handle everything; others give you a hosted instance and leave configuration to you.
The decision between managed and self-hosted usually comes down to one question: is running database infrastructure a competitive advantage for your team, or a cost center? For most product teams, it's the latter.
Time-series databases require ongoing operational attention. High write volumes, continuous queries, retention policies, compaction, replication: none of these are set-and-forget. They require patching, monitoring, storage management, and careful scaling decisions.
Self-hosting a TSDB means your team owns all of that. For many organizations, the operational cost of running a TSDB exceeds the managed service premium within the first year.
Managed services shift the burden. Automated backups, high availability, scaling, and security patches are handled by the provider. The trade-off is less control over the underlying infrastructure.
Before choosing a managed service, get clear on four things:
Pricing model: Per-GB ingested, per-query, or per-node? The right answer depends on your write/read ratio and retention period.
Data egress costs: Often invisible until you're paying them.
Query language: Will your team need to learn something new? Can your existing tools connect?
Migration complexity: How hard is it to leave if you need to?
Managed TSDB selection involves different trade-offs than self-hosted evaluation. Performance still matters, but pricing transparency, operational simplicity, and ecosystem compatibility become equally important.
The biggest hidden cost difference between managed TSDBs is how they charge. Per-GB ingested, per-query, and per-node models diverge dramatically at scale.
Tiger Data published benchmarks showing a 150–220x cost advantage over Timestream LiveAnalytics for equivalent workloads. But every vendor's pricing favors their typical workload pattern. The right comparison depends on your data volume, query frequency, and retention period.
Watch for: data egress fees, storage costs for historical data, costs for continuous or materialized queries, and minimum spend tiers.
SQL compatibility is the most-debated topic in TSDB community discussions. Engineers overwhelmingly prefer SQL over proprietary languages: familiar, portable, and free from lock-in.
Tiger Cloud, ClickHouse Cloud, CrateDB Cloud, and QuestDB all support full SQL. InfluxDB 3.0 added SQL support alongside InfluxQL. Grafana Cloud uses PromQL for metrics. Azure Data Explorer uses KQL (Kusto Query Language), which is powerful but completely proprietary.
If your team already knows PostgreSQL, Tiger Cloud runs on PostgreSQL. Your existing queries, ORMs, and tools work without modification, and you avoid adding another database to your stack.
"Managed" means different things to different providers. Some handle everything: provisioning, scaling, backups, high availability. Others give you a hosted instance but leave configuration, scaling, and tuning to you.
Ask: Is scaling automatic or manual? Are backups included or add-on? Is high availability built in, or does it require a multi-node setup? What happens during maintenance windows?
The sections below compare what each service actually manages, and what you still own.
How does each service handle growth? Can it ingest millions of data points per second? What happens to query performance as data volume grows from GB to TB to PB?
Performance benchmarks for managed services are less meaningful than for self-hosted. Network latency, shared infrastructure, and autoscaling behavior matter more than raw throughput numbers in isolation.
Time-series data rarely exists in isolation. The managed TSDB needs to integrate with your existing stack: Grafana for visualization, Kafka or MQTT for ingestion, Terraform for infrastructure, and application frameworks.
PostgreSQL-compatible services (Tiger Cloud) inherit the entire PostgreSQL ecosystem. Thousands of tools, drivers, and extensions work without adaptation.
How hard is it to migrate in and out? Lock-in is a legitimate concern for managed services.
The three most common migration paths in 2026: from AWS Timestream (LiveAnalytics deprecation), from InfluxDB 1.x/2.x (version fragmentation), and from self-hosted PostgreSQL.
Database | Pricing Model | Query Language | PostgreSQL Compatible | Best Use Case | Notable Limitation |
Tiger Cloud | Usage-based (compute + storage) | Full PostgreSQL SQL | Yes | IoT, analytics, relational time-series | Not optimized for extreme write throughput (10m tuples / sec) |
InfluxDB Cloud | Per-write + per-query + per-storage | InfluxQL + SQL (3.0) | No | Metrics and observability | Version fragmentation (Serverless vs. Dedicated vs. OSS) |
AWS Timestream for InfluxDB | Instance + storage (hourly) | InfluxQL + SQL | No | AWS-native teams migrating from LiveAnalytics | AWS markup on InfluxDB; version confusion still applies |
ClickHouse Cloud | Usage-based (compute + storage, scale-to-zero) | ClickHouse SQL (+ managed Postgres offering) | ClickHouse engine: No. Managed Postgres: Yes | Analytical workloads, log analytics, ad-tech | OLAP engine not time-series-first; no continuous aggregates; upserts expensive |
QuestDB Enterprise | Enterprise/BYOC pricing | SQL (PG wire protocol) | Wire protocol only | Extreme ingest throughput | Less managed experience; enterprise engagement required |
CrateDB Cloud | Cluster-based (per-node + storage) | SQL (PG compatible) | Yes | IoT + full-text search in one DB | Expensive at small scale; smaller community |
Grafana Cloud | Active series + data points ingested | PromQL | No | Infrastructure monitoring, Kubernetes | Not for IoT, financial, or analytical time-series; no SQL |
Azure Data Explorer | Cluster-based (compute + storage + ingestion) | KQL | No | Enterprise Azure, petabyte-scale log analytics | KQL lock-in; Azure-only; high minimum spend |
Tiger Cloud is a fully managed PostgreSQL service with time-series capabilities built in: hypertables for automatic time-based partitioning, continuous aggregates for pre-computed rollups, Hypercore for hybrid row/columnar storage with automatic compression on historical data, and real-time analytics on live operational data.
Pricing: Usage-based on compute and storage. No per-query or per-ingest charges. Costs stay predictable as you scale.
Query language: Full PostgreSQL SQL. If you know Postgres, you know Tiger Cloud.
Best for: Teams that want a managed time-series database without abandoning PostgreSQL. IoT, application metrics, financial data, and real-time analytics workloads where you also need relational capabilities (JOINs, foreign keys, full-text search).
Strengths:
Full PostgreSQL ecosystem: pgvector, PostGIS, pg_cron, and thousands of other extensions work without modification
Continuous aggregates for pre-computed rollups that keep dashboards fast without batch ETL
Hypercore storage engine with up to 98% compression on historical data
Time-series and relational business data in a single database
Limitations: If your workload is purely append-only metrics with no relational requirements, a purpose-built metrics store will offer higher raw ingest throughput.
Deployment: AWS, Microsoft Azure (multiple regions each), and GCP*.
* Note: Availability on GCP is through Aiven and does not include the same feature set as Tiger Cloud (for example, no data tiering, connectors, or Tiger Lake).
InfluxDB is now in its third major version. InfluxDB 3.0 is a ground-up rewrite using Apache Arrow and DataFusion.
Version fragmentation is a real decision factor. InfluxDB Cloud Serverless (3.0-based), Cloud Dedicated, and self-hosted OSS are different products with different capabilities. InfluxDB 2.x is effectively end-of-life for cloud. Know which InfluxDB you're evaluating before you start.
Pricing: Cloud Serverless charges per write, per query, and per storage separately. Cloud Dedicated uses reserved-capacity pricing. At query-heavy scale, Serverless costs can spike unpredictably.
Query language: SQL (new in 3.0) and InfluxQL. Flux is deprecated.
Best for: High-volume metrics and observability workloads, teams already invested in the InfluxDB ecosystem via Telegraf and Grafana integrations.
Strengths:
Massive community (most GitHub stars of any TSDB)
Strong Telegraf plugin ecosystem for metrics collection
High ingest throughput optimized for metrics
Limitations: Version fragmentation creates migration confusion. Community discussions on Reddit and Hacker News consistently flag version fragmentation as InfluxDB's biggest pain point—users frequently encounter incompatibilities between 1.x, 2.x, and 3.0. SQL support is new and less mature than PostgreSQL-native solutions. No relational capabilities (no JOINs across tables), and no ACID guarantees. Pricing at scale has been a persistent community complaint.
Amazon's surviving managed time-series offering. Timestream LiveAnalytics was closed to new customers in June 2025. Timestream for InfluxDB runs managed InfluxDB instances on AWS infrastructure.
If you're researching "AWS Timestream," the original product is deprecated. Timestream for InfluxDB is the replacement, but it's a managed InfluxDB instance, not an AWS-native database product.
For a full breakdown of AWS time-series options, see our AWS Time-Series Database guide.
Pricing: Instance-based (per-hour) plus storage. Similar to RDS pricing.
Query language: InfluxQL and SQL (via InfluxDB 3.0).
Best for: Teams locked into AWS that want a managed TSDB without leaving the AWS console. Teams migrating from Timestream LiveAnalytics with minimal code changes.
Strengths: AWS integration across IAM, VPC, and CloudWatch. Single-pane management alongside other AWS services.
Limitations: You're paying AWS markup on top of InfluxDB. AWS re:Post threads show significant user confusion about the migration path from LiveAnalytics. The InfluxDB version fragmentation problem carries over here.
ClickHouse is a columnar OLAP database that handles time-series workloads through its strength in analytical queries on large datasets. ClickHouse Cloud is the managed service. ClickHouse Cloud now also offers a managed PostgreSQL service as a separate product alongside their OLAP engine.
Pricing: Usage-based on compute and storage, with autoscaling and scale-to-zero.
Query language: ClickHouse SQL (ANSI-compatible with extensions) for the OLAP engine. Standard PostgreSQL for the managed Postgres offering.
Best for: Analytical workloads where time-series is one dimension of larger datasets. Log analytics, product analytics, and ad-tech use cases that need sub-second queries on billions of rows.
Strengths:
Exceptional query performance on analytical workloads
Materialized views for pre-aggregation
Scale-to-zero reduces costs during idle periods
Managed Postgres option available if you need a PostgreSQL-compatible service alongside ClickHouse
Limitations: The core ClickHouse OLAP engine is not PostgreSQL-compatible and requires learning ClickHouse-specific SQL and tooling. Not purpose-built for TSDB patterns like continuous aggregates, automatic downsampling, or time-based retention. Upserts and updates are expensive. If you're evaluating ClickHouse Cloud's managed Postgres offering specifically, that's a different product from their OLAP engine.
QuestDB Enterprise is a purpose-built time-series database focused on extreme write performance and SQL compatibility.
Pricing: Enterprise pricing (contact sales). BYOC (Bring Your Own Cloud) model—QuestDB's ops team manages your deployment in your own cloud account.
Query language: SQL (PostgreSQL wire protocol compatible).
Best for: Teams that need the highest possible ingest throughput: IoT with millions of sensors, high-frequency trading data, network telemetry.
Strengths:
Industry-leading ingest benchmarks
SQL support via PostgreSQL wire protocol
Lower resource consumption than many alternatives
Limitations: QuestDB's self-serve managed SaaS (QuestDB Cloud) has been discontinued. The current offering is QuestDB Enterprise with BYOC deployment, which requires enterprise engagement rather than a self-serve signup. Smaller ecosystem and community. No continuous aggregates or pre-computed rollups. Limited relational capabilities.
CrateDB Cloud is a distributed SQL database built on a shared-nothing architecture, with time-series and full-text search in the same system.
Pricing: Cluster-based (per-node plus storage).
Query language: SQL (PostgreSQL compatible).
Best for: IoT platforms that need time-series storage and full-text search in a single database. Industrial IoT, fleet management.
Strengths:
Combines time-series, full-text search, and geospatial in one database
PostgreSQL compatible
Strong IoT customer base in industrial applications
Limitations: CrateDB has a smaller community than Tiger Data or InfluxDB. Cluster-based pricing is expensive at small scale. Less mature ecosystem tooling.
Grafana Cloud is a managed observability stack that includes Prometheus-compatible metrics storage (Grafana Mimir), plus logs (Loki) and traces (Tempo).
Pricing: Usage-based on active series and data points ingested.
Query language: PromQL.
Best for: Infrastructure monitoring and observability: Kubernetes, microservices, cloud-native applications. If your "time-series" workload is primarily server metrics and alerting, this is purpose-built for it.
Strengths:
The de facto standard for cloud-native monitoring
Native Grafana dashboard integration
Prometheus compatibility means existing exporters and dashboards work
Limitations: PromQL, not SQL. Not designed for IoT, financial, or analytical time-series workloads. No relational capabilities. Optimized for metrics cardinality.
Microsoft's fully managed analytics service, Azure Data Explorer (ADX) is optimized for streaming data and time-series workloads at large scale.
Pricing: Cluster-based (compute plus storage plus ingestion). Azure reserved instances available.
Query language: KQL (Kusto Query Language). Powerful but completely proprietary.
Best for: Enterprise teams on Azure with large-scale log analytics, IoT, and time-series workloads. Integrates natively with Azure IoT Hub, Event Hubs, and Power BI.
Strengths:
Enterprise-grade SLAs
Strong Azure ecosystem integration
Handles petabyte-scale data
Built-in ML and anomaly detection on streaming data
Limitations: KQL is a proprietary query language with a significant learning curve and real lock-in. Azure-only deployment. Cluster-based pricing means a high minimum spend. Notably absent from most TSDB comparison content, which results in fewer third-party integrations and less community support.
Choose Tiger Cloud if:
Your team knows PostgreSQL and doesn't want to learn a new query language
You need time-series and relational data in the same database (JOINs, foreign keys, reference tables)
You want continuous aggregates for real-time dashboards without manual ETL
Your workload spans IoT, analytics, and application data (not just metrics)
Choose InfluxDB Cloud if:
You have an existing Telegraf/InfluxDB pipeline and want managed hosting
Your workload is primarily metrics collection and monitoring
You're comfortable navigating the version differences between Serverless and Dedicated
Choose ClickHouse Cloud if:
Your primary need is analytical queries on large datasets where time is one dimension
You need sub-second queries on billions of rows for product analytics, log analytics, or ad-tech
You want ClickHouse's OLAP engine and optionally their managed Postgres service in the same platform
Choose Grafana Cloud if:
Your workload is specifically infrastructure monitoring (Kubernetes, microservices)
You're already using Prometheus and want a managed backend
You don't need to store non-metrics time-series data
Choose Azure Data Explorer if:
You're an enterprise team on Azure with petabyte-scale requirements
You're comfortable with KQL and Azure-specific tooling
You need built-in ML and anomaly detection on streaming data
Consider QuestDB Enterprise or CrateDB if:
You need extreme ingest throughput (QuestDB) or combined time-series plus full-text search (CrateDB)
Your requirements don't fit what the larger platforms optimize for
Ready to try the PostgreSQL option? Start a Tiger Cloud trial. No credit card required.
Timestream LiveAnalytics is closed to new customers as of June 2025. Existing customers should plan migration.
Your options:
Timestream for InfluxDB: Stay on AWS, get a managed InfluxDB instance. Minimal console disruption, but you inherit the InfluxDB version fragmentation problem.
Tiger Cloud: PostgreSQL compatibility means you can use standard SQL tooling. If relational queries matter alongside your time-series data, this is the cleanest path.
ClickHouse Cloud: Good fit if your Timestream workload was primarily analytical.
Tiger Data published a detailed migration guide: Farewell, Timestream.
InfluxDB 3.0 is a ground-up rewrite. Migrating from 1.x or 2.x to 3.0 is not a version upgrade. It's a platform migration.
If you're already facing that migration, it's a natural point to evaluate alternatives. Tiger Cloud accepts data via standard PostgreSQL COPY, and migration tools exist for common InfluxDB data formats. Several customers have made this move, including Messari, Waites.net, and United Manufacturing. For a broader look at your options, see 5 InfluxDB Alternatives for Your Time-Series Data.
If you're running PostgreSQL with homegrown time-series patterns (partitioned tables, cron-based aggregation, manual rollups), Tiger Cloud is the smallest migration step. Your existing SQL, schemas, and application code work with minimal changes.
Timestream LiveAnalytics is closed to new customers as of June 2025. Existing workloads continue to run, but Amazon has signaled the transition to Timestream for InfluxDB. New customers should evaluate Timestream for InfluxDB or other managed TSDBs.
AWS's replacement is Timestream for InfluxDB, which runs managed InfluxDB instances on AWS infrastructure. Many teams are using the deprecation as an opportunity to evaluate other managed TSDBs: Tiger Cloud, ClickHouse Cloud, or InfluxDB Cloud directly.
Yes. PostgreSQL handles time-series data well, especially at smaller scales. Tiger Data extends PostgreSQL with purpose-built time-series features (hypertables, continuous aggregates, Hypercore) that make it competitive with purpose-built TSDBs while maintaining full PostgreSQL compatibility.
Tiger Data (formerly Timescale) offers Tiger Cloud, a fully managed service. The open-source TimescaleDB extension can also be self-hosted on any PostgreSQL instance.
It depends on scale and query patterns. Tiger Cloud handles IoT well: high cardinality support, up to 98% compression via Hypercore, and the ability to JOIN sensor data with reference tables. For pure metrics collection, InfluxDB Cloud with Telegraf is a strong option. For Azure-based IoT architectures, Azure Data Explorer integrates natively with Azure IoT Hub.
InfluxDB Cloud Serverless charges per write, per query, and per storage separately. Costs can spike with query-heavy workloads. Tiger Cloud uses usage-based pricing on compute and storage without per-query charges. At scale, the models diverge significantly. Benchmark your specific workload before deciding.
A TSDB is optimized for append-heavy, time-ordered data: high write throughput, time-range queries, and compression. A relational database is optimized for transactional workloads with random reads and writes. Tiger Data bridges both: it's a PostgreSQL relational database with built-in time-series optimizations.
Tiger Cloud (full PostgreSQL SQL), ClickHouse Cloud (ANSI SQL with extensions), and CrateDB Cloud (PostgreSQL-compatible SQL) all offer strong SQL support. InfluxDB 3.0 added SQL, but it's non-native and less mature. Grafana Cloud uses PromQL. Azure Data Explorer uses KQL. Neither is SQL.
QuestDB's self-serve managed SaaS (QuestDB Cloud) has been discontinued. The current offering is QuestDB Enterprise with BYOC deployment. It requires enterprise engagement rather than a self-serve signup, making it a different category from Tiger Cloud or InfluxDB Cloud.
For self-hosted options, see our Best Time-Series Databases Compared guide. The main open-source options include TimescaleDB (PostgreSQL extension), InfluxDB (OSS), Prometheus, VictoriaMetrics, and QuestDB.
This Tiger Data resource, How to Choose a Database: A Decision Framework for Modern Applications, provides a deep dive into making the database decision that’s right for your company, stack, and use case.