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After that, it gets {ts:135} noticeably more expensive, costing multiple times more per user versus Superbase. So, if you plan to have many {ts:143} users and earn little per user, then personally, I wouldn't use Clerk since the pricing is quite high once you go {ts:150} above that free tier. Now in terms of the developer experience and real world feedback, Superbase has an integrated {ts:157} back end and is powerful and flexible, especially with the rowle security, but you may need to build your own UI {ts:164} components and handle authentication workflows manually. Clerk on the other hand offers plug and play components for {ts:171} login, sign up, profiles, and sessions. It's smooth and quick to set up. One review mentions that it is practically {ts:179} the only solution that took less than a few minutes to integrate. Now, downsides include less control over authentication {ts:187} logic, being tied to a third party, and limited customization unless you are on a pay tier, which again is quite

Poor user management can stand between users and products. From forgotten passwords and failed account de-duplication, to broken and out-of-sync vendor integrations — there's a lot that can go wrong. Clerk is a software solution that aims to solve user…Read more

Poor user management can stand between users and products. From forgotten passwords and failed account de-duplication, to broken and out-of-sync vendor integrations — there's a lot that can go wrong. Clerk is a software solution that aims to solve user…Read more

WorkOS always touts their 1,000,000 free users for Authkit...but you need to pay $100 for a custom domain. You're going to be paying for some of the features well before you get to 1,000,000 users. ... AuthKit never has any WorkOS branding. Clerk puts "Powered by Clerk" on your login page unless you pay. This feels gross. Imagine if Heroku/Vercel were injecting ads into your app?! AuthKit has free MFA. I believe everyone should get secure auth. Clerk charges to enable MFA. They also charge for passkeys and features like impersonation. Why? Custom domains cost us $ to run (we pay Cloudflare) so we charge for this. It's also designed for commercial apps. The authkit.app is great for any hobby app. ... Also, the SSO connectors being $125 per month per connection, rules out my target market. That is a lot in my market and it doesn't ease off as I grow, it's a fixed base cost. As I grow to 20-30 customers I'd be better off hiring a developer to implement the same features. ... There's no miracles here, just complex engineering and solving a thousand edge cases. If you decide to use open source, make sure you quickly update dependencies so you're always running latest. Ruby-SAML had a major vulnerability disclosed last month and thousands of apps were affected: https://workos.com/blog/ruby-saml-cve-2024-45409 Splitting hairs, but the authkit.app domain basically is an ad no? Yeah, I agree on the MFA and Passkeys. Impersonation is a toss up for me, I understand where they're coming from but also would be nice if it was in the free tier. Looking at the authkit docs, unless I'm using Next or Remix... I need to store the refresh token, manage refreshing the access token, verify the access token, manage revoking the session and deleting the cookies. Clerk does all that for me so that's a win in my book (I understand you folks are working on more SDKs, so that'll be cool). …

So let’s jump into it. This is a really cool quote: “It took weeks to integrate billing with Clerk’s auth.” That sounds horrible, right? Like, why would we be promoting this as how great Clerk Billing is? Well, that’s because that’s what customers said to us in the past. They said, “You know what, Clerk is awesome, but it’s really hard to integrate billing.” And so we said, “That’s a good idea. Why don’t we build something to solve that?” So some of the things that they said to us was that writing code to sync webhooks is difficult. If anyone’s ever built a SaaS integration before, you know that can be tough and no fun. There’s a bunch of downsides to doing this yourself. Mostly it’s just pure frustration and pain, right? So what we wanted to do is build a solution that really takes minutes and takes advantage of the whole Clerk ecosystem. No webhooks necessary. So if you’re writing code and you’re building an app, you don’t have to build in any code to sync webhooks. … ... This is how Clerk knows, “Hey, here’s how we’re going to authenticate you and know which app you are trying to run here.” ... So what we could do is something like this, where we say, “has Platinum” or “has Gold.” And this would do it, right? I would be able to save this, and I’d be able to see that I have access to that special message. But this isn’t maintainable. You don’t want to keep adding a bunch of conditionals. So what can you do? Well, it’s pretty common when you’re building an app to build in this concept of features, where you could have a basic plan that has a list of features, and the pro plan has that same but more, right? … And so this is more like what you would actually build yourself, right? You’ve got a bunch of features. And so I’m still not able to access this thing. So let’s fix that. So instead of looking for a plan, let’s look for a feature. Let’s look for Widgets because, if you remember, both the Gold plan and the Platinum have access to Widgets. So I just save that and look at that. Now I have access again. And so this is a way easier, way, more maintainable way to control access to your app using Features. … Boom, got the environment keys changed. ... So orgs are now turned on. So if I go back here, well, what’s different? Well, there’s really nothing different. I have my user profile management stuff. So how can we make this good? ... And that would work. But again, that’s not super maintainable. I have access again, but it’s not maintainable. So what can we do here, right? Well, we could use the plan features, as I showed you before, but there’s something else with orgs, right? Typically, when you do RBAC, you have a role, and then you add permissions to it. … And so you can imagine in your own apps, as you build out a much deeper integration, how simple and easy this can be to set up. ... And what we’re doing right here is we’re giving Clerk—that’s us, that’s those people—we’re giving Clerk all the drudgery, the stuff that we don’t want to deal with, to Clerk, and Clerk is making it super easy for us to do. So that’s the end of my demo.

Also, the clerk service has layered integrations, powered by an http layer. We have customers using each part of the layer for varied integration types. That being said, the SDKs for the spa frameworks are the easiest to use. ... But the main idea is that we wanted most apps to cost ~$25/mo - $100/mo, and, if you're building a B2B SaaS, you're going to have far fewer MAUs, and so we wanted the base cost to be higher at ~200/mo. ... Banning users is still currently on the $25/mo tier which feels wrong, it should be in the free tier. We're due for a pricing revamp again quite frankly to make these pricing options more attractive. The tricky thing with the MAU costs is that a lot of folks seems to think they have a monster on their hands and forecast for like 1M MAUs or something, which is so far from reality. It's tough to balance all of these competing priorities -- and if we don't have enough revenue, we can't keep building and investing in the platform for which we have pretty big ambitions.

One of the biggest reasons for its popularity is the **generous free tier**, which now includes **50,000 Monthly Retained Users (MRUs)** per application. This is significantly higher than most competing identity providers. However, like most authentication platforms, costs can increase quickly as applications scale. This guide explains: - Clerk’s latest pricing structure ... # Hidden Costs to Watch While Clerk’s pricing page looks straightforward, teams often discover additional costs as their applications grow. … # When Clerk Makes Sense ... # When Costs Can Become a Problem Costs may grow faster when: - Applications exceed **50K users** - B2B SaaS products create many organizations - Multiple enterprise SSO connections are required In these scenarios, teams often evaluate alternatives with more predictable pricing models. # Final Thoughts Clerk offers one of the best developer experiences in the authentication ecosystem. Its generous free tier and modern SDKs make it a compelling choice for startups. However, the pricing model introduces several cost drivers: - per-user billing - per-organization fees - enterprise connection pricing Before adopting Clerk, teams should model long-term growth and compare alternatives.

Refer to the Backend API and Frontend API reference docs for questions about object structures, requests, and responses. Are you looking for a place to get started?

## Scalability Issues in Real-time Monitoring Adopt sharding for high-ingest pipelines: segment metric and log flows by tenant, service, or function to distribute load efficiently across processing nodes. For context, when Datadog increased their global traffic by 10x between 2023 and 2024, they shifted from a monolithic aggregation system to a horizontally partitioned one. Horizontal partitioning yielded latency reductions of 22% and dropped resource saturation incidents by 35% compared to previous architectures. … **Implement traffic shaping and rate-limiting controls:** Without these, metadata spike events triggered by bursty microservice deployments can inflate queue sizes by 400% within minutes. Adaptive throttling ensures pipeline throughput remains predictable and guardrails prevent silent data loss during anomalous surges. *Failure to account for compounding data volumes leads to missed alerting windows, budget overruns, and degraded user experiences. Integrating proactive scaling, pre-ingestion filtering, and payload reduction safeguards system uptime and data accessibility during exponential growth phases.* … - Schedule routine audits of asset maps against live environments, especially when using spot instances or serverless functions. - Integrate deployment hooks to trigger toolchain updates on each change, mirroring approaches seen across both full time vs part time student scheduling and elastic resource management. - Monitor third-party and custom integrations closely after changes, referencing failure rates–Gartner noted a 21% higher incident rate post-major infrastructure shifts without dedicated integration audits. Respond faster to shifting environments by building cross-functional response teams. Distributed responsibility models, as supported by DevOps, cut incident response times by 50% while accommodating rapid infrastructure scaling and migration. … - Integrate schema evolution tools and strong versioning practices to keep the pipeline operational during structural updates, decreasing downtime risk by 60% compared to ad hoc migrations. - Monitor storage growth per topic or service; auto-scale and partition as thresholds are hit to avoid bottlenecks under sudden workloads. - Review data access patterns: Precompute high-frequency metrics using rollup jobs while keeping raw logs accessible for compliance or sporadic investigation. … ## Tool Integration and Compatibility Concerns **Prioritize standardized interfaces and robust APIs during system design.** Over 68% of enterprise outages in 2024 were traced to inadequate cross-tool communication and mismatched agent versions. Consistently audit connector versions and enforce regular compatibility checks across your pipeline. Avoid closed-format logs–adopt open telemetry or similar protocols for trace continuity across all integrations. … ### Ensuring Compatibility with Diverse Monitoring Tools Standardize message formats with open protocols such as OpenTelemetry and StatsD to decrease integration effort across over 68% of enterprise environments. Choose metrics serialization (for example, JSON or Protocol Buffers) compatible with the most widely adopted collectors–Prometheus exporters handle over 83% of observed metric pipelines in distributed cloud setups. Avoid proprietary data models; maintain backward compatibility with legacy agents, since 39% of organizations operate hybrid infrastructure, blending on-premise collectors and cloud-native services. Routinely perform integration testing using container orchestration clusters (Kubernetes, Docker Swarm) configured with multiple plugin versions, as mismatches with agent APIs account for 27% of reported ingestion failures. Document exact protocol versions and authentication requirements in a public repository to support seamless interoperability between new and legacy pipelines. Employ configuration abstraction layers to map disparate tool-specific labels or tags, reducing translation issues by up to 44% in multi-vendor deployments. … |Principle|Reason|Implementation Tip| |--|--|--| |Versioned Endpoints|Reduce breaking changes|/v1/resources, /v2/resources| |Secure Auth (OAuth 2.0)|Increase security, ease rotation|Use refresh tokens, avoid static keys| |Rate Limiting & Backoff|Prevent blacklisting/API bans|Exponential backoff, use 429 retry headers|

From performance issues to security vulnerabilities, there are a myriad of obstacles that developers must overcome to deliver high-quality software on time and within budget. One common challenge that developers face is monitoring the performance of their applications in real-time. Without the proper tools and metrics, it can be difficult to identify bottlenecks, pinpoint errors, and optimize performance. This is where Datadog comes in. ... One of the biggest challenges that developers face is identifying the root cause of performance issues. In a complex, distributed system, performance problems can arise from a variety of sources, including network latency, database queries, and third-party services. Without the right monitoring tools, developers may spend hours or even days troubleshooting issues that could have been easily resolved with the help of Datadog. … ### Challenges of Implementing Datadog One common challenge developers face when implementing Datadog is the initial setup and configuration of the service. Datadog offers a wide range of features and integrations, which can be overwhelming for first-time users. Additionally, developers may struggle with defining custom metrics and alerts that are specific to their applications. Another challenge is the cost associated with using Datadog. While the service offers a free tier for small teams and startups, the pricing can quickly escalate as your usage grows. This can be a deterrent for some developers who are looking for cost-effective monitoring solutions. Finally, integrating Datadog into existing workflows and processes can be a challenge. Developers may need to make changes to their codebase or infrastructure in order to fully leverage the features of Datadog, which can be time-consuming and disruptive. ### Solutions to Overcoming Challenges To address the challenge of setup and configuration, developers can take advantage of Datadog's extensive documentation and tutorials. ... ### The Challenges Faced by Developers Developers today are tasked with building and maintaining complex applications that run on a variety of platforms and environments. This complexity can lead to a number of challenges, including: - Performance issues - Scalability concerns - Security vulnerabilities - Resource utilization inefficiencies These challenges can be daunting for developers, but with the right tools and solutions in place, they can be overcome. … Yo, as a developer using Datadog, I gotta say that real world challenges can be a pain. Like trying to monitor app performance across multiple servers can get overwhelming real quick. I feel you, man. Especially when you're dealing with a massive amount of data and you're trying to make sense of it all. It's like looking for a needle in a haystack.

But one thing comes up again and again in engineering communities, Slack channels, and Reddit threads: the bill. Teams report receiving invoices that were 3x, 5x, even 10x what they budgeted, not because they misunderstood the product, but because Datadog's pricing model has layers of complexity that only reveal themselves at scale or during unexpected traffic spikes. … ## The Core Problem: Multi-Dimensional Pricing Most SaaS tools charge you for one thing - seats, API calls, or storage. Datadog charges for many things simultaneously, each with its own pricing metric, allotment structure, and overage calculation. You're not buying one product; you're buying a bundle of sub-products that each generate their own line items. This creates a situation where forecasting your monthly bill requires understanding a dozen interrelated variables. A configuration change, a new service deployment, or a temporary traffic spike can silently trigger significant cost increases that only appear on next month's invoice. If you’re trying to estimate or reduce these costs, try our pricing calculator to see how much you could save by switching to OpenObserve. ... ### 1. Per-Host Billing in a World of Dynamic Infrastructure Datadog prices its core Infrastructure Monitoring and APM products on a per-host basis. In a world of containerized microservices and auto-scaling Kubernetes clusters, this model creates a structural mismatch between how you run software and how you get billed for monitoring it. Infrastructure monitoring starts at **$15 per host/month**. APM with continuous profiler starts at **$31 per host/month**. The definition of a "host" is deliberately broad: a VM, a Kubernetes node, an Azure App Service Plan, or an AWS Fargate task can all count. … #### The Container Trap This issue is amplified in containerized environments. The intended setup is one Datadog Agent per Kubernetes node. But if the agent is mistakenly deployed inside every pod, each pod is billed as a separate host. A misconfiguration on a 50-node cluster running hundreds of pods can multiply your bill by 10x or more overnight. ### 2. Why Custom Metrics Become Expensive at Scale This is frequently cited as the most unpredictable part of a Datadog bill. Datadog charges a premium for "custom metrics" any metric that doesn't come from a native Datadog integration. That includes virtually every application-level metric you create yourself, and critically, **all metrics sent via OpenTelemetry are billed as custom metrics**. … #### Metrics Without Limits™: A Complex Workaround Datadog's answer to cardinality costs is a feature called **Metrics Without Limits™**, which lets you control which tag combinations are indexed. But it adds another billing layer: - **Indexed metrics:** billed at the standard overage rate - **Ingested metrics:** a separate fee of **$0.10 per 100 metrics** for *all* data sent before filtering … To cut costs, you might index only 20% of your logs. But that means 80% of your data is invisible during an incident precisely when you need full visibility most. This pricing structure creates a perverse incentive: the teams that most need comprehensive logging are punished most heavily for it. Budget constraints lead to strategic under-logging, which leads to longer incident resolution times. … **The Configuration Trap** The "opt-out" is not a simple toggle in the Datadog UI. Because the billing is triggered by the *presence* of specific metadata in your OTel spans: 1. **Default Ingestion:** By default, the Datadog Agent and OTLP intake will process any recognized GenAI attributes. 2. **Manual Suppression:** To avoid these charges, engineers must manually configure their **OpenTelemetry Collector** or **Datadog Agent** to drop or mask GenAI-specific attributes (e.g., using a transform processor) before the data reaches Datadog's servers. … ## The Bottom Line Datadog is a powerful platform with deep integrations and strong brand recognition. ... But for teams running modern cloud-native architectures with auto-scaling, OpenTelemetry instrumentation, LLM-powered features, and cost sensitivity Datadog's pricing model creates friction at every layer. The per-host model discourages architectural flexibility. The custom metric tax penalizes comprehensive instrumentation. The log indexing structure forces a trade-off between cost and visibility.

3. This article covers some common errors Datadog users face and shows how to fix them. … - Cost: Datadog's comprehensive features come at a price. The cost of using Datadog can be a significant factor for smaller organizations or startups. - Learning Curve: While Datadog's interface is user-friendly, setting up complex monitoring and alerting can be challenging for beginners. - Limited Free Tier: Datadog offers a free tier with limitations. Users may need to upgrade to a paid plan to access all the platform's features. - Common Errors: Datadog users often encounter common errors when getting started, such as issues with hostname detection and API key configuration. These can disrupt monitoring and require troubleshooting. Below, we’ll dive deeply into some of the errors Datadog users experience. ## Common Datadog Errors and their Solutions ### Hostname detection issues Hostname detection in Datadog can sometimes present challenges for users. One common issue is when dynamically assigned hostnames change frequently, leading to difficulty in accurately tracking and analyzing metrics and logs. To address this problem, Datadog offers solutions like agent-based hostname tagging, allowing users to define custom tags that remain consistent even when hostnames change. Another issue can arise when multiple services run on a single host, making it challenging to differentiate between them in the monitoring system. Datadog allows custom hostnames to be set and auto-detection rules to ensure each service is labelled correctly. Overall, Datadog's flexibility and customization options help users overcome hostname detection issues, providing accurate and meaningful insights from their monitoring and observability data. ### Agent not configured for proxy. Several issues may arise when the Datadog agent is not configured to work through a proxy. First and foremost, it can lead to communication problems between the agent and the Datadog cloud service, resulting in missed or delayed data collection and monitoring. Additionally, without proxy configuration, the agent might struggle to access external resources and endpoints, potentially impacting its ability to provide comprehensive insights into your infrastructure. Configuring the Datadog agent for proxy usage is essential to address these issues. You can ensure uninterrupted data transmission and monitoring by setting up the agent to work with your proxy server. This involves adjusting the agent's configuration file to include proxy server details, such as the server address and port, and any necessary authentication credentials. Doing so enables seamless and secure communication between the Datadog agent and the Datadog platform, ensuring that your monitoring and analytics efforts remain consistent and effective. ### The Datadog API key is not set up in your config file. When a DataDog API key is not configured correctly in your system, it can lead to various potential issues. DataDog relies on this key to authenticate and authorize access to monitoring and analytics services. Without a valid API key, you may encounter authentication errors, preventing you from sending or receiving data from DataDog. This can disrupt essential monitoring and alerting functionalities, making tracking the performance and health of your infrastructure and applications challenging. … 1. Locate your DataDog API key: If you don't already have a DataDog API key, you must sign in to your DataDog account and generate a new API key. 1. Update your configuration file: Identify the configuration file used to store your DataDog settings (e.g., datadog.yaml, datadog.conf, or a similar file). … 1. Save the configuration file: After making the necessary changes, save the configuration file. 1. Restart your application or service: In some cases, you may need to restart the application or service that relies on DataDog for monitoring to apply the changes made to the configuration file. Verify the configuration: Double-check the configuration to ensure the API key is correctly set. … ### The Datadog API key does not correspond to the account. Using an incorrect API key with Datadog can lead to various issues and disruptions in your monitoring and analytics workflow. The most immediate problem is the inability to authenticate and access your Datadog account, rendering the API useless. This can result in missing or delayed data collection, hampering your ability to track and analyze performance metrics, troubleshoot issues, or set up alerts effectively. … ## Conclusion Datadog is a popular choice for monitoring, offering a comprehensive platform with powerful features. However, it's not without its challenges, such as cost, a learning curve, and common errors during setup. By being aware of these issues and the provided solutions, Datadog users can enhance their monitoring experience.