I was at Atlassian during the OpsGenie acquisition and part of that process. Honestly, the biggest surprise to me was how long it took to shut down OpsGenie as a standalone product.
The broader trend here is the shift from unbundling to consolidation. Over the past decade, many “features” were created as standalone SaaS products, but that era is winding down. We’re now seeing the pendulum swing back, with more consolidation across the industry.
In my opinion, OpsGenie should have been a built-in feature of Jira Service Management from day one of the acquisition.
It’s a tricky balance. It would be easy for Atlassian to add settings. But every setting adds complexity. And I think we can all agree that we don’t want Jira to be more complex.
It takes a short time to adjust to UI changes, but settings live on forever.
The product suite differs significantly from 2015. Put aside new and acquired products like Trello and OpsGenie, the existing products have significantly expanded their capabilities and added Premium and Enterprise tiers.
Revenue in 2015 was $320M,
In 2021 it was $2B. That kind of growth requires growth in Supporting functions and infrastructure teams, not just in product.
> Funny thing is jira is such a mess Atlassian introduced jira service desk more focused on tickets while the former can also do that.
Not quite. Service Desk (aka Service Management today) has a number of capabilities that differ quite significantly from Jira Software. The most obvious is the end-user facing help portal. But how users are managed is quite different, it has SLAs, different reporting, a knowledge base, etc. Just like Jira Software is flexible but tailored to software teams with boards, backlogs and sprints, Jira Service Management is tailored to IT teams and requirements specific to that market.
Great take. I worked on Confluence for a few years and have a bit of insight.
Search has been an area of focus on and off for the most part of the last 15 years. It actually has gotten a lot better and Atlassian has an entire team focused on improving the search experience across their suite of products (they started with Confluence). And from what I hear, they are focusing on all the right things.
To your point, no search system can be a good fit for every possible use-case. Confluence has a number of different use-cases, but let's just pick "documentation" and "intranet" as an example here.
Intranets are, to a large degree, about keeping up with what's new in a company. Therefore recent content is likely more relevant than older content.
When used for documentation, recency doesn't matter at all. If a document was written 2 years ago, but the content is still accurate, it's just as relevant as it was on day one.
That means no single relevance configuration will work well for all use-cases. Leveraging ML is essential. But even a single ML model across an entire Confluence instance is not going to work as different spaces are used for different use-cases. What's really required here is to build different models for different spaces to create a tailored relevancy for each space. It's not an easy problem to solve, but I'm confident they will get there with time.
Seeing the challenges with Search at Atlassian, despite having a large, dedicated team of engineers working on the problem, is what motivated me to join http://sajari.com. We've been doing a lot of work on reinforcement learning and Neural Search. Our focus right now is on public content websites and e-commerce, but eventually we will get around to enable products like Confluence to create a great search experience without the need for an entire team. Search is a hard problem, but there is so much opportunity to improve the experiences that are available today. Exciting times.
I was the head of product for the developer tools at Atlassian in 2012. We thought long and hard about taking Bitbucket cloud and packaging it in a VM (which is what GitHub did at the time) or leveraging the platforms we’ve already built for Confluence and Jira that would give us access control and a plug-in system from day 1. It was a tough call.
Ultimately we’ve decided to build on top of our server platforms and target companies with 1000+ employees from day one. That decision had a huge impact on how we approached performance and what features we prioritised. The hierarchy of projects and permissions associated with them as well as the way we designed Pull Requests are good examples of that.
It was the right decision at the time, even if the product happened to be different in cloud and server, which did lead to some confusion. But Stash customers were really happy with the product.
Neural search in combination with learning algorithms and traditional keyword searches are clearly the future. They vastly outperform traditional search engines.
At sajari.com we have been working on an experiment that uses a 1 cpu machine on cloud run to serve a neural network generated, hash based index of an old BestBuy catalog (25k products). Retrieval uses an approximate nearest neighbour (ANN) look up which typically takes ~1msec. Speed and relevancy are already pretty good.
But we have also learned that there is no one silver bullet and we have seen the best results when combining neural search with traditional keyword search and reinforcement learning.
Every search company will be expected to have machine learning capabilities. However, if you simply bolt them on top instead of considering the fast volume of additional data as part of the core engine, the experience will suffer greatly and the results will be subpar.
To be fair, Algolia's marketing is great. They did a great job with the announcement. Looking forward to so how well it actually works and what the pricing is going to be. I don't think OpenAI have announced pricing for the GPT-3 service (which this is based on) yet, but I'm certain it won't be cheap to start with.
We've been looking for an easy and affordable way to share "what's new" with our customers. Finally a pricing that doesn't make this prohibitive for smaller startups.
It's not supposed to sound attractive, but investors will largely care about the YoY growth of 121% for Q2.
They spend a ton of money on Sales & Marketing (293,577k in the last fiscal year). That's what's driving a lot of their growth I assume and it's a lever they can pull back to increase profitability.
In particular for ecommerce sites it pays to invest in search. A large percentage of transactions is initiated by search queries. You are correct that fuzzy matching will likely get you decent results. However, finding the right product 90% of the time or 98% of the time makes a huge difference if you have a high volume of transactions. Synonyms and a better spelling model can massively improve your results and the resulting conversion.
But search relevancy is only one part of the equation. Think about a physical store and how the milk is usually in the back and a few high margin items are close to the counter or strategically placed. The same can be true for an ecommerce store. If the search engine has the ability to take business metrics like revenue and margins, or customer data like loyalty programs or brand affinity into account, you can much better optimise for your desired business outcome.
We just recently switched one of the largest ecommerce retailers in Australia over from Algolia to Sajari by doing the above and increasing their conversion rate by 10%.
Interesting change. Disclaimer, I joined one of Algolia's competitors (http://sajari.com) 6 months ago and we are about to release a new product and change our pricing, so I've been thinking about this a lot.
Having worked at Atlassian before, I understand how important simple pricing can be. My personal (and probably biased opinion) is that this is a move in the wrong direction. It appears simpler on the surface, but the concept of a unit and understanding all the disclaimers associated with it make the pricing more complicated than before. If I have to read the faq to understand what I'm getting, it's too complicated IMHO. Also, it seems other features, like analytics retention, crawler, have moved into add-ons, which requires you to contact sales to find out how much you'll pay.
More granular pricing does provide more flexibility, but it also reduces predictability, which can be important especially in small to mid-sized businesses. I'm curious to understand how people feel about "usage pricing" vs. "tiered pricing" where you know exactly what you are paying each month? Which ones do you prefer? We are still finalising our own pricing, any feedback would be very much welcome.
Hey B! Funny seeing you here. I'm now running product at http://sajari.com
You will find that most tools provide document level permissions to some degree by storing user/group IDs on the document and adding filters to the query. However, it generally requires custom implementation work to integrate it into your systems and prevent spoofing of the filters.