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Apptrail – SaaS audit trails as a Service

Hacker News

Apptrail – SaaS audit trails as a Service

Hi Hacker News! We're Samrose and Shaeq from Apptrail ( https://apptrail.com ). We let B2B SaaS companies easily add customer-facing audit trails to their products. It's currently too hard to build and consume SaaS audit logs. Organizations use audit logs to access and monitor the activity coming from their SaaS tools for security and compliance reasons. For example, a security admin at an enterprise company would use Slack audit logs to see messages sent and what devices and IP addresses they were sent from. Many SaaS companies don’t offer audit trails to their customers, which results in a lack of insight for the SaaS user (for example, they have to make a support ticket every time they need information). For SaaS companies, adding audit logs to their products is a daunting task that often gets delayed because audit logs are full of hidden complexity. Designing a multi-tenant audit trail solution involves careful consideration around scalability, availability, durability, verifiable immutability, configurable data retention, and guaranteed delivery, to name a few requirements. The audit logs need to be viewable through a UI, searchable & filterable, accessible programmatically via a REST API, and ideally support streaming delivery to consumers. SaaS companies are overwhelmed by the complexity, and often implement subpar solutions, which results in more work for their customers to actually consume the audit logs. As an example, SaaS companies are often unable to support extended data retention times (7-10 years is common for larger customers) because their systems aren’t designed for long-term storage. At AWS, we worked on the infrastructure that allows Amazon to easily bake audit logs into their services. Whether it’s S3 or Sagemaker, every AWS service needs to offer audit logs to all customers for it to launch. We realized that enterprise & security-conscious customers have the same needs when using SaaS tools, but SaaS companies are left entirely to figure out building customer-facing audit logs themselves. Apptrail is a fully managed service that enables any B2B SaaS company to easily add full-featured audit trails to their product and deliver audit logs to their end customers' destinations (data lakes, SIEM, etc.) in near real time. Check out a short demo here: https://watch.mmhmm.app/z_JQJ5sqVvIWtkYhrwIkAf . The way it works is SaaS companies record user and API activity using our language native SDKs, and Apptrail takes care of everything else. Apptrail automatically aggregates and indexes audit data in the cloud and surfaces it to SaaS customers through a self-service portal UI and REST API that we host on your behalf. There's full support for analytical queries and fast data retrieval while keeping audit logs in S3 for durability and optimal scaling. Apptrail offers audit log delivery as a first-class feature using "trails", which allows audit log consumers to add rules to filter audit logs based on their content and configure streaming delivery to destinations like S3 or Splunk. We’re built entirely on AWS, using services like S3, SQS, Kinesis, and ECS on EC2 extensively, with good ol’ JVM powering the application logic. We’ve built Apptrail to scale horizontally, so it can ingest an unlimited number of audit logs. Apptrail is also completely replicated in independent cloud regions, so you can use our regional endpoints to keep audit log data in a specified region (currently we’re launching with US West - Oregon as our first region). We have a usage-based pricing model and charge for each audit log sent and delivered. Our extensive always free tier allows 100k events to be sent for free every month forever. You can sign up for and try Apptrail today. We offer a no credit card required free trial. We would love to hear your thoughts about what we’re building or your experiences with SaaS audit logs in general. Feel free to also reach out to us at founders@apptrail.com

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Indie HackersFits the IH revenue-focused audience · Strong signals: organizations · Missing: supports, reddit linkedin, podcasting
96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, slack, user · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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