# Multitenancy Published articles for Multitenancy. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Secure Multitenancy with OpenShift and Isovalent Networking for Kubernetes DevFeed: [Secure Multitenancy with OpenShift and Isovalent Networking for Kubernetes]() Original publisher: [Read original article]() Author: Christian Hernandez Published: 2026-04-30T07:01:05Z Content type: article Language: en Sources: [Isovalent - The latest articles covering eBPF-based Networking, Observability, and Security]() Topics: [Multitenancy](), [networking](), [Kubernetes]() Tags: [blog](), [isovalent](), [kubernetes](), [multitenancy](), [networking]() ### AI overview The article explores how Isovalent Networking for Kubernetes fits into OpenShift's multitenancy model. ### Source excerpt OpenShift has long treated multitenancy as a core platform concern and in this blog we explore how Isovalent Networking for Kubernetes fits perfectly in this model! ## Seven Years of Firecracker DevFeed: [Seven Years of Firecracker]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2025-09-18T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Firecracker](), [virtualization](), [Amazon Web Services](), [Security](), [Open Source](), [Amazon Bedrock AgentCore](), [Serverless](), [Artificial Intelligence](), [Multitenancy](), [Large Language Model](), [Python]() Tags: [ai](), [amazon-bedrock-agentcore](), [aws](), [aws-lambda](), [firecracker](), [llm](), [multitenancy](), [open-source](), [python](), [security](), [virtualization]() ### AI overview This article reflects on seven years of Firecracker, an open source technology for creating and managing small virtual machines. It describes Firecracker's role in AWS Lambda, its security and compatibility benefits compared with container and language-level isolation, its use across AWS, and its role in Amazon Bedrock AgentCore for running AI agents. ### Source excerpt Seven Years of Firecracker Time flies like an arrow. Fruit flies like a banana. Back at re:Invent 2018, we shared Firecracker with the world. Firecracker is open source software that makes it easy to create and manage small virtual machines. At the time, we talked about Firecracker as one of the key technologies behind AWS Lambda, including how it'd allowed us to make Lambda faster, more efficient, and more secure. A couple years later, we published Firecracker: Lightweight Virtualization for Serverless Applications (at NSDI'20). Here's me talking through the paper back then: The paper went into more detail into how we're using Firecracker in Lambda, how we think about the economics of multitenancy (more about that here), and how we chose virtualization over kernel-level isolation (containers) or language-level isolation for Lambda. Despite these challenges, virtualization provides many compelling benefits. From an isolation perspective, the most compelling benefit is that it moves the security-critical interface from the OS boundary to a boundary supported in hardware and comparatively simpler software. It removes the need to trade off between kernel features and security: the guest kernel can supply its full feature set with no change to the threat model. VMMs are much smaller than general-purpose OS kernels, exposing a small number of well-understood abstractions without compromising on software compatibility or requiring software to be modified. Firecracker has really taken off, in all three ways we hoped it would. First, we use it in many more places inside AWS, backing the infrastructure we offer to customers across multiple services. Second, folks use the open source version directly, building their own cool products and businesses on it. Third, it was the motivation for a wave of innovation in the VM space. In this post, I wanted to write a bit about two of the ways we're using Firecracker at AWS that weren't covered in the paper. Bedrock AgentCore Back in J ## Web Analytics Architecture for Multitenancy and AI DevFeed: [Web Analytics Architecture for Multitenancy and AI]() Original publisher: [Read original article]() Author: Víctor Ramírez, Alberto Romeu Published: 2025-08-13T14:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [Web](), [Multitenancy](), [Artificial Intelligence]() Tags: [ai](), [analytics](), [architecture](), [i-built-this](), [multitenancy](), [web]() ### AI overview The article describes the architecture of a web analytics platform that uses multitenancy and AI to serve thousands of customers from one platform. ### Source excerpt Web analytics with multitenancy and AI serves thousands of customers from one platform. Here's the architecture behind it. ## Databases will be free DevFeed: [Databases will be free]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2024-11-19T00:00:00Z Content type: opinion Language: en Sources: [Turso Blog]() Topics: [Databases](), [Multitenancy](), [Turso](), [Provisioning](), [Artificial Intelligence](), [DRIVE]() Tags: [agents](), [ai-agents](), [centralization](), [databases](), [multitenancy](), [provisioning](), [turso]() ### AI overview The article argues that database costs will eventually fall close to zero, enabling plentiful individual and ephemeral databases. It connects this shift to a microdatabase model, AI agents requiring disposable databases for agentic memory, and Turso's efforts toward massive multitenancy and lower-cost provisioning. ### Source excerpt In the near future, databases will be free and plentiful. What does that change for you? ## Is Postgres RLS for Everything and Everyone? DevFeed: [Is Postgres RLS for Everything and Everyone?]() Original publisher: [Read original article]() Author: David Gomes Published: 2024-11-15T17:32:58Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Authorization](), [Multitenancy](), [Per-user Database](), [SQL](), [Security](), [Drizzle](), [Object-relational mapping](), [Architecture & Design]() Tags: [authorization](), [blog-post](), [database](), [drizzle](), [multitenancy](), [orm](), [postgres](), [security](), [sql](), [testing]() ### AI overview The article examines whether PostgreSQL Row-Level Security (RLS) should express an application's entire authorization model. It recommends a project-per-user pattern for multitenancy when possible, while describing RLS as a way to prevent cross-tenant access in a shared database. It discusses RLS's dense syntax, recursion errors, policy behavior, security settings, optimizer considerations, and testing difficulties, and notes that Drizzle ORM can simplify some TypeScript usage. ### Source excerpt In Neon, we recommend using a project-per-user pattern for multitenancy whenever possible. If this doesn't work for you and you're placing all your tenants within a single Postgres database, you should at least use RLS to prevent cross-tenant access--but expressing your entire aut... ## Adaptive Computer chooses Turso ephemeral databases to power AI agents that build and modify software DevFeed: [Adaptive Computer chooses Turso ephemeral databases to power AI agents that build and modify software]() Original publisher: [Read original article]() Author: Mike Soylu Published: 2024-10-21T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [AI Agent](), [Platform API](), [Multitenancy](), [SQLite]() Tags: [ai-agents](), [multitenancy](), [platform-api](), [sqlite](), [turso]() ### AI overview Adaptive Computer describes using Turso to provision and branch databases for AI-agent changes to applications, validate those changes, and roll them back when needed. The company says it had 40,000 Turso databases in September while still in alpha. ### Source excerpt How Adaptive Computer uses Turso multitenancy to create tens of thousands of databases that power AI agent actions ## How Drizzle Leverages Turso for Its Products and Services DevFeed: [How Drizzle Leverages Turso for Its Products and Services]() Original publisher: [Read original article]() Author: Aleksandr Blokh Published: 2024-10-17T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Drizzle](), [Turso](), [Object-relational mapping](), [SQLite](), [TypeScript](), [Multitenancy](), [Replication](), [Latency]() Tags: [drizzle](), [latency](), [multitenancy](), [orm](), [performance](), [replication](), [sqlite](), [turso](), [typescript]() ### AI overview Drizzle explains how it uses Turso in production for data-centric applications, including Drizzle Studio themes, web analytics, an internal analytical back office, and a game. The article highlights Turso's SQLite foundation, schema capabilities, vector support, transparent pricing, replication, recovery, multi-region support, and scalability. ### Source excerpt Drizzle ORM is a longtime user of Turso to power various products and services it offers. Here's how they use it. ## Databases have traditionally been expensive. What if we could change that? DevFeed: [Databases have traditionally been expensive. What if we could change that?]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2023-12-15T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [Databases](), [SQLite](), [cloud-infrastructure]() Tags: [cloud](), [databases](), [devices](), [embedded](), [infrastructure](), [multitenancy](), [offline](), [per-user-database](), [processes](), [scale](), [servers](), [sqlite](), [sync](), [turso]() ### AI overview The article describes using Turso to create many databases for multitenant SaaS applications, including a separate database for each tenant, user, or location. It presents deployment across infrastructure, including embedded deployments, while noting that Turso Cloud supports querying databases over the network and syncing them with devices and servers for offline use. ### Source excerpt Build multitenant SaaSes (with a database per tenant, user, location) and deploy databases anywhere including embedded in your own infrastructure. This and more is what having a legendary amount of databases can do for you. ## Speeding up a Remix website with Turso's embedded replicas hosted on Akamai's Linode DevFeed: [Speeding up a Remix website with Turso's embedded replicas hosted on Akamai's Linode]() Original publisher: [Read original article]() Author: James Sinkala Published: 2023-11-30T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [Embedded Replicas](), [Remix](), [Linode]() Tags: [akamai](), [embedded-replicas](), [linode](), [multitenancy](), [remix](), [turso]() ### AI overview An older post describes using Turso's embedded replicas to speed up a Remix website hosted on Akamai's Linode. The accompanying note explains that Turso has since been rewritten and now supports querying databases over the wire or syncing them with devices and servers for offline use. ### Source excerpt Using Turso's embedded replicas to speed up a Remix website hosted on Akamai's Linode ## Creating a multitenant SaaS service with Turso, Remix, and Drizzle DevFeed: [Creating a multitenant SaaS service with Turso, Remix, and Drizzle]() Original publisher: [Read original article]() Author: James Sinkala Published: 2023-10-19T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [Drizzle](), [Remix](), [Software as a service]() Tags: [database](), [drizzle](), [integration](), [multitenancy](), [remix](), [saas](), [turso]() ### AI overview This article describes building a multitenant SaaS website in which each user receives a separate Turso database, using Turso, Remix, and Drizzle. The supplied text notes that the post covers an older version of Turso and briefly describes Turso Cloud's database access, device and server synchronization, offline support, and file-based behavior. ### Source excerpt Building a multitenant SaaS website where each user gets their own Turso database by leveraging Turso's numerous database offering. ## Surprising Scalability of Multitenancy DevFeed: [Surprising Scalability of Multitenancy]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2023-03-23T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Multitenancy](), [Scalability](), [Amazon S3](), [Cloud]() Tags: [architecture](), [cloud](), [cost](), [economics](), [multitenancy](), [performance](), [s3](), [scalability](), [storage](), [systems]() ### AI overview The article explains how multi-tenancy improves the economics and scalability of cloud systems by reducing the overall peak-to-average traffic ratio. Using Amazon S3 as an example, it describes how distributing customer workloads across many storage devices can support high individual workload peaks without making the overall system disproportionately expensive. ### Source excerpt Surprising Scalability of Multitenancy When most folks talk about the economics of cloud systems, their focus is on automatically scaling for long-term seasonality: changes on the order of days (fewer people buy things at night), weeks (fewer people visit the resort on weekdays), seasons, and holidays. Scaling for this kind of seasonality is useful and important, but there's another factor that can be even more important and is often overlooked: short-term peak-to-average. Roughly speaking, the cost of a system scales with its (short-term1) peak traffic, but for most applications the value the system generates scales with the (long-term) average traffic. The gap between "paying for peak" and "earning on average" is critical to understand how the economics of large-scale cloud systems differ from traditional single-tenant systems. Why is it important? It's important because multi-tenancy (i.e. running a lot of different workloads on the same system) very effectively reduces the peak-to-average ratio that the overall system sees. This is highly beneficial for two reasons. The first-order reason is that it improves the economics of the underlying system, by bringing costs (proportional to peak) closer to value (proportional to average). The second-order benefit, and the one that is most directly beneficial to cloud customers, is that it allows individual workloads to have higher peaks without breaking the economics of the system. Most people would call that scalability. Example 1: S3 Earlier this month, Andy Warfield from the S3 team did a really fun talk at OSDI'23 about his experiences working on S3. There's a lot of gold in his talk, but there's one point he made that I think is super important, and worth diving deeper into: heat management and multi-tenancy. Here's the start of the relevant bit on heat2 management: Andy makes a lot of interesting point here, but the key one has got to do with the difference between the per object heat distribution, the per aggregat