2. 10M/month with yearly commitment = $46895/year 100M/month with yearly commitment = $165895/year
1,042 karma · joined December 13, 2012
2. 10M/month with yearly commitment = $46895/year 100M/month with yearly commitment = $165895/year
The previous pricing was based on indexing operations + search operations. The new pricing is only based on search request and in a lot of situation N search operations = 1 search request (disjunctive faceting, federated search, etc.). At the end, for the big majority of free users (> 99%) they have as many or more search request in the 10 free units than in the old plan.
On this example: - 1M queries per month $847 in full pay-as-you-go - 1M queries per month with yearly commitment cost $7200 per year ($600 per month)
We now only count search request and we did a simulation on our existing free plan base, offering 10 units cover all of them.
Btw, we keep offering more quota for opensource projects.
We released this new pricing only because we are convinced this is better for customers.
Thanks for your feedback, we have some more work to make our pricing page clear. We need to add a simulator on this page.
The reason of this indirection is that we still have to deal with data/record. It is unfortunately not possible to pay only for searches, you can imagine a use case that push 100GB of data and perform only a few searches. The unit gives access to 1k searches request and 1k records. For the majority of users, they will pay per searches.
For SaaS use case, we have a different pricing where we price per GB with volume discount.
There is a volume discount, so the more units you consume, the cheaper they are. And if you commit to a year, the volume discount applies on your your yearly capacity. This give you a significant discount if you commit to a year. This is how you can have overages. Of course if you stay on a month-to-month play, there is no overages.
When we have a platform, it is very hard to have a pricing that works perfectly well for all industries and that is simple!
Btw, for your use case we designed a different pricing that we call OEM pricing that is simply based on the GB used and not the numbers of searches/records.
Also you can keep your existing plan, we force no-one to move to the new pricing.
* we have never discontinued a feature in the API since the launch
* We never broke our API clients, we proposed a new version when a new feature required a big change but we kept the previous version (and this happened only on two API client in 5 years)
For the support, this is our engineer's team working on the product that does the support and we put a lot of effort to make sure all our customers are satisfied and get the relevant answers.
Then if you got the same customer experience with a $40 machine, you have probably not used all the feature/power of the engine. I am sad to see such a feedback and you can make me accountable to make sure we will do everything we can to satisfy all our users
We cannot use any local network IP or load-balancer as we distribute a cluster on several providers with different autonomous systems. This is how we are able to offer SLA of up to 99.999% with a big refund strategy: https://blog.algolia.com/for-slas-theres-no-such-thing-as-10...
Textual relevance is a very complex domain and the Postgres's built in text search features is a simple keyword matching engine compared to Algolia engine that contains a lot of alternative matching. The mesure of textual relevance is also very different of what you have in the Postgres text search.
At the end, this is not only about speed but mainly about relevance
As with any engine, there is a lot of customization possible to handle complex use cases and it won't be handled out of the box.
You can read this list of 10 tips to have a great relevance, all of them are doable with Algolia: https://blog.algolia.com/algolias-top-10-tips-to-achieve-gre...
Algolia comes with a lot of pre-defined tuning that are good for most use cases (see https://blog.algolia.com/inside-the-algolia-engine-part-3-qu... for more details).
That said there is always some tuning to have perfect result for a specific use case. There is no engine that provide perfect results out of the box without any tuning.
The default one should link to HN discussion/article
Feel free to give us your dream list of improvements :)
ChangeLog:
2015-01-19
- Custom date range picker
- Responsive design
- New "comments[<>=]X" syntax to filter by number of comments
- If the story doesn't have any URL, the link goes to the HN discussion
- Here and there cosmetics improvements (less padding, removed useless "upvote" arrow, ...)
2015-01-12 - Full reimplementation based on Angular.js
- Performance improvements
- Minimal design to improve readability
- Customizable sort order (by date/popularity, by date range)
- New sharing features (Twitter & Facebook compliant)
- Cloudfront-based assets delivering
- Experimental UI
- Inlined comments
- Advanced filtering/refinements capabilities
- ThumbnailsYou can have an idea of a website with few data that use us here: http://skyscrapercenter.com/
You should give us a try :)