--- title: "Tiger Cloud on Azure: Why Fully-Managed Timescaledb Beats Self-Hosting And Other Managed Options" published: 2026-09-03T13:50:14.000-04:00 updated: 2026-09-03T13:51:55.000-04:00 excerpt: "Tiger Cloud on Azure vs self-hosting TimescaleDB: skip-scan, compression, continuous aggregates, tiered storage, private endpoints, and cross-region backups—fully managed." tags: Announcements & Releases, Tiger Cloud authors: Nicole Ghalwash --- > **TimescaleDB is now Tiger Data.** Running TimescaleDB on Azure means choosing between three paths: self-hosting the Apache 2 licensed edition, using the TimescaleDB on Azure managed service, or running Tiger Cloud (fully-managed TimescaleDB) on Azure. When evaluating these three options, the question isn't just managed vs. self-hosted; it's whether you're looking to operate TimescaleDB yourself or build on a platform engineered to run it for you. If you're already running TimescaleDB as an Apache 2 extension on Azure PostgreSQL Flexible Server, you've already hit the constraint: Microsoft doesn't ship the Community Edition, which locks you out of compression, continuous aggregates, Toolkit, and skip-scan. These aren't nice-to-have features; they're the difference between a workload that scales and one that requires architectural workarounds. Over the past six months, we shipped capabilities for Tiger Cloud on Azure that shift how teams evaluate this choice. ## Self-hosting TimescaleDB on Azure incurs a hidden tax [You already know the operations tax is real.](https://www.tigerdata.com/blog/self-hosting-timescaledb-platform-ownership) Self-hosting gives you control over infrastructure, backup strategy, and security posture. For regulated industries, that control matters. But TimescaleDB adds a layer of operational complexity that compounds as you scale. Unlike vanilla Postgres, TimescaleDB demands expertise in hypertable design, continuous aggregate materialization, compression policies, and skip-scan optimization. These aren't _set-it-and-forget-it_ features. They require understanding time-series query patterns, retention policies, and workload behavior to tune correctly. As your data grows across sensor streams, financial ticks, event logs, and metrics, the tax doesn't just grow linearly. It scales with the sophistication of the system itself. On Azure, whether you choose self-hosted Apache 2 or work through a third-party managed service that doesn't specialize in time-series, you're still making these decisions yourself: When do you partition? What compression codec? Which data should tier to cold storage? How aggressive should you be with continuous aggregates? Get it wrong, and your queries slow down. Get it right, and you've spent months of engineering time tuning the database instead of building the product it supports. This is what is referred to as _the operations tax_: engineering time keeping TimescaleDB running instead of building on top of it. ## Managed TimescaleDB should handle time-series complexity, not delegate it.  The right managed platform doesn't take control away. It automates decisions with clear, right answers for time-series workloads and gives back the engineering time you'd otherwise spend tuning. Tiger Cloud on Azure is built on this principle. We built TimescaleDB, and we run it for a living. The platform makes the tuning calls your team would otherwise make by hand. This is the difference between managed Postgres (which any vendor can offer) and fully-managed TimescaleDB (which only Tiger Data can deliver). ## How Tiger Cloud removes the time-series tax on Azure  ### Cost: tiered storage optimized for time-series retention  [Tiered storage](https://www.tigerdata.com/docs/learn/data-lifecycle/storage/about-storage-tiers) moves rarely accessed data to Azure Blob Storage while keeping it queryable through the same SQL interface. It solves a specific problem for time-series workloads: you accumulate months or years of history that has to stay available for compliance, analytics, and model training, but you rarely scan it at production speeds. In self-hosted or third-party managed environments, a 5 TB table on premium SSD costs the same every month, forever. Tuning doesn't change that cost structure. With Tiger Cloud, you enable tiered storage on a service (available on Scale and Enterprise plans), then set a tiering policy that moves chunks older than the age you choose to Blob Storage on a schedule. You can also tier specific chunks by hand when you want tighter control. Hot data stays on fast SSD, and a single hypertable can span both tiers. To read tiered data, you turn on tiered reads for a query, a session, or all future sessions. [For time-series workloads, this typically reduces storage costs 2-5x](https://www.tigerdata.com/docs/get-started/news/new#tiered-storage-on-microsoft-azure) (depending on compression rates). More importantly, it removes the false choice between keeping historical data for analytics and keeping your bill manageable. ### Network security: Private endpoints for TimescaleDB on Azure VNets Self-hosted forces a binary choice: public endpoints (fast but risky) or private endpoints (secure but complex). [Tiger Cloud's private endpoint support, now generally available on Azure](https://www.tigerdata.com/docs/deploy/tiger-cloud/tiger-cloud-azure/security/azure-privatelink), connects your service directly to your Azure virtual network without exposing any public endpoint. No bastion hosts, no networking expertise required, and provisioning is available directly from the Tiger Console. Other competitors like HorizonDB offer this too. The difference is what sits behind the endpoint. You're connecting to Tiger Cloud, where compression, continuous aggregates, and skip-scan are already configured and maintained for you. You’ll find that vanilla Postgres doesn’t have access to these features and other generalist managed services don’t specialize in them.  ### Achieving operational resilience through additional supported regions, cross-region backups, and Azure Monitor Self-hosting means you own disaster recovery, monitoring, and geographic expansion as separate, ongoing projects. These aren’t _one-and-done_ projects, it’s constant operational work. However, with Tiger Cloud, we’re committed to providing operational resilience to users, and our newest updates do just that.  - **Recovery and RPO/RTO:** We now support [Germany West Central (Frankfurt) and Southeast Asia (Singapore)](https://tigerdata.com/docs/learn/tiger-cloud/regions), enabling GDPR-compliant residency for EU customers and local latency for Asia-Pacific. But the real capability is cross-region backup. Enterprise customers can store an additional backup copy in a different region from where the service runs. [](https://www.tigerdata.com/blog/self-hosting-timescaledb-platform-ownership#heading-recovery-time-objective-rto--recovery-point-objective-rpo-)That changes the two numbers you have to answer for after an outage: [how fast you recover, and how much data you lose](https://www.tigerdata.com/blog/self-hosting-timescaledb-platform-ownership#heading-recovery-time-objective-rto--recovery-point-objective-rpo-). Point-in-time recovery and failover happen automatically. In self-hosted environments, testing a cross-region restore is non-trivial. On Tiger Cloud, it's built in. - **Monitoring and observability:** Tiger Cloud integrates directly with Azure Monitor, the observability service already built into Azure. Database metrics and query performance flow into your existing dashboard instead of requiring a separate stack. That removes one of the more tedious parts of self-hosting: [standing up and maintaining your own monitoring.](https://www.tigerdata.com/blog/self-hosting-timescaledb-platform-ownership#heading-monitoring-observability) No second stack to build, no vendor sprawl, no context switching between platforms. ## What your team gets back Self-hosting gives you control of TimescaleDB, but you also own everything underneath it: provisioning, patching, backups, failover, monitoring, and capacity planning. That's a standing infrastructure and DevOps commitment, not a one-time setup cost. Third-party managed services remove some friction but still leave you owning database management. Tiger Cloud removes the operations burden entirely. Your team stops tuning indexes and starts shipping features. Your operations team stops making capacity decisions and starts working on what the business needs. Your database handles growth without degradation. You gain reliability without the tax, and speed without sacrificing control. ## The choice Self-hosting TimescaleDB on Azure still works. It's a legitimate choice for teams that want architectural control and have the engineering bandwidth to own TimescaleDB's operational complexity. Managed third-party services like Aiven reduce friction but still require you to own database management. Tiger Cloud removes this operational burden. When your primary data is time-series and you still need transactional integrity, SQL semantics, high cardinality, and high throughput, you need a database that holds up under all four, plus a team to operate it. The question is simple: do you want to run that database yourself, or build on one that's already managed? ## Next steps Start by [signing up for a 30-day free trial](https://www.tigerdata.com/go/trial), where you will have $1,000 in trial credits. Then move a test workload and start building. You’ll notice pretty quickly how much time you’ll save when certain architectural decisions are automated for you, such as the ones outlined above. To learn more about Tiger Cloud on Azure, check out these resources: - [Blog: What You’re Really Owning When You Self-Host TimescaleDB](https://www.tigerdata.com/blog/self-hosting-timescaledb-platform-ownership) - [Case study: How Companion.energy Reduced Query Latency 25x and Compressed Terabytes to Gigabytes with Tiger Cloud](https://www.tigerdata.com/blog/companion-energy-case-study-tiger-datahttps://www.tigerdata.com/blog/companion-energy-case-study-tiger-data) - [Case study: How Float achieved 99.3% data compression with TimescaleDB (where vanilla Postgres and InfluxDB fell short)](https://www.tigerdata.com/blog/how-float-runs-ai-energy-company-3-person-team-tiger-data) - [Tiger Cloud on Azure documentation](https://www.tigerdata.com/docs/deploy/tiger-cloud/tiger-cloud-azure/configuration/about-configuration)