Founded in 2012, pganalyze provides actionable insights into Postgres. Specializing in PostgreSQL database monitoring and optimization, pganalyze gives automatic insights into Postgres query plans, helps improve Postgres query performance with its Index Advisor and VACUUM Advisor, and lets the user perform query drill-down analysis, observe per-query statistics and conduct trend analysis in a platform that integrates with both self-managed Postgres servers as…
$149
per month for 1 database server
ScienceLogic SL1
Score 8.9 out of 10
Enterprise companies (1,001+ employees)
ScienceLogic is a system and application monitoring and performance management platform. ScienceLogic collects and aggregates data across and IT ecosystems and contextualizes it for actionable insights with the SL1 product offering.
N/A
Pricing
pganalyze
ScienceLogic SL1
Editions & Modules
Production
$149
per month for 1 database server
Scale
$399
per month for up to 4 database servers ($100/mo for each additional billable server)
Enterprise Cloud
Contact us
per year per database server
Enterprise Server
Contact us
per year per database server
No answers on this topic
Offerings
Pricing Offerings
pganalyze
ScienceLogic SL1
Free Trial
Yes
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Required
Additional Details
Scale plan offers additional database servers at a cost of $100/month for each additional billable server.
In general, each 1 Postgres server that is running (i.e. one parent postgres process) is considered 1 billable server. In the case of cloud providers these are often called "instances".
Depending on the subscription plan you are on, you may have a special price for replica servers (also called "readers", or "followers"), which is implemented by multiplying the replica count with a multiplier (e.g. 0.5), with the total rounded up.
Annual plans are available and come at a discount compared to the monthly plans
Contact us for custom requirements
ScienceLogic SL1 offers four tiers:
SL1 Advanced – Application Health, Automated Troubleshooting and Remediation Workflows
SL1 Base – Infrastructure Monitoring, Topology & Event Correlation
SL1 Premium – AI/ML-driven Analytics, Low-Code Automated Workflow Authoring
SL1 Standard – Infrastructure Monitoring – with Agents, Business Services, Incident Automation, CMDB Synchronization, Behavioral Correlation
To get pricing for each tier, please contact the vendor.
More Pricing Information
Community Pulse
pganalyze
ScienceLogic SL1
Features
pganalyze
ScienceLogic SL1
AIOps Features
Comparison of AIOps Features features of Product A and Product B
pganalyze works great with the postgresql and gives a good alternatives to the existing producsts available out there like Slick or basic as microsoft SQL server manaagement studio. We have used in our projects of data migration and integration of enterprise data warehouseing and reporting . And it has provides us satisfactory results.
Appropriate if you are setting up a monitoring suite in new Infrastructure Environment. Definitely NOT suited for Migration Projects. ScienceLogic SL1 cannot cater to a lot of monitoring requirements which already would have been configured in old monitoring suite. Plus, limited support for customizations and having to go to "Feature Requests" route makes in extremely complicated.
Creating powerpacks from scratch for new devices may be straightforward but will rarely be easy. Rewarding when completed, but not easy.
Developer documentation needs a rethink. While the information may be there (it isn't always) it is not easy to find. This is not helped by using different terms for the same things.
A developer console/dashboard for monitoring data collection from powerpacks instances without having to switch webpages or have to monitor multiple webpages.
We migrated away from our 20-year-old homegrown solution and have no back-tracking capability. ScienceLogic is demonstrating new capabilities that we would not have been able to do on our own using our legacy system. We understand the capabilities of competitors based on our bake-off selection where ScienceLogic won on capabilities and future near-term potential (expandability, platform growth). We know that those competitors are not really close to where we have been able to push ScienceLogic (as a partner).
We use ScienceLogic SL1 in our organization to serve effective monitoring solutions to our external customers. Our customers depend upon us for critical events/alerts related to their IT infrastructure gears and using SL1, we're able to provide them with a proactive monitoring solution that resolves an issue before an impact is noticed by the customer. There are very few monitoring solutions that can cater to a variety of Cloud platforms like Public Cloud (AWS, Azure) and private cloud simultaneously and SL1 addresses this business problem very well
Science Logic SL1 provides the option of Distributed deployment where multiple instances of each appliance can be deployed to manage the load and availability. SL1 provides a High Availability feature for Database Servers and Data Collection. If one of the Data Collectors in the collector group fails, it will automatically redistribute the devices from the failed Data Collector among the other Data Collectors in the Collector Group. The high availability feature for the Database server ensures that SL1 performs failover automatically to another server without causing the outage to the application.
The performance is entirely dependent on the complexity of the environment/network being used to host the platform. Outside of those factors, the platform runs very efficiently and quickly out of the box. We have integrations with other platforms and neither seem to take a hit from our moderate API usage. Any issues with performance would be experienced by choices made in infrastructure or complexity of things built by the customer to display in the GUI (overly complicated and cluttered dashboards for example)
So far, it's good as part of my overall experience, except for a couple of use cases. The support team is well knowledgeable, has technical sound, and is efficient. When support escalates to engineering, the issue gets stuck and takes months to resolve.
When I joined our company, I did not know about the in person training at firts. Logging onto the SL University, I realised that there were different sessions being held at different times throughout the year. The training itself was good, but being in a different time zone, made it difficult to attend, but the sessions that I attended was great!
There are a lot of educational materials and courses on the SL1 training site (Litmos university). However the recording quality is sometimes not very good - screen resolution is low. There is a lack of professional rather than user-oriented documents and there are mistakes in documentation and education is not well structured.
Along with the purchase of the solution, we purchased a statement of work with their Professional Services organization to meet our outcomes and fill our critical gaps. The PS team was outstanding, very professional and allowed us to screen share while they built our integrations. In many cases they would teach us how they did certain things within the platform.
It is better in terms of results provided. We were using Microsoft SQL Server Management Studio so far and relying only on that but some one recommend to us to use pganalyze to do the query analysis and we started using it for one project and we liked it much that we have started recomended to all projects teams ..
We evaluated a couple of other competitive products in the IT infrastructure observability domain; however, we found that ScienceLogic has a slight edge over the others for us. We encountered a cost barrier, as managing too many customers with an MSP setup was a costly affair, and several solutions did not offer an MSP solution at that time.
Our deployment model is vastly different from product expectations. Our global / internal monitoring foot print is 8 production stacks in dual data centers with 50% collection capacity allocated to each data center with minimal numbers of collection groups. General Collection is our default collection group. Special Collection is for monitoring our ASA and other hardware that cannot be polled by a large number of IP addresses, so this collection group is usually 2 collectors). Because most of our stacks are in different physical data centers, we cannot use the provided HA solution. We have to use the DR solution (DRBD + CNAMEs). We routinely test power in our data centers (yearly). Because we have to use DR, we have a hand-touch to flip nodes and change the DNS CNAME half of the times when there is an outage (by design). When the outage is planned, we do this ahead of the outage so that we don't care that the Secondary has dropped away from the Primary. Hopefully, we'll be able to find a way to meet our constraints and improve our resiliency and reduce our hand-touch in future releases. For now, this works for us and our complexity. (I hear that the HA option is sweet. I just can't consume that.)