The era of legacy logging is over.
Creating the logging layer for the AI Era.

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We’re Noel and Trevor, co-founders and co-CEO’s of Bronto - where we are on a mission to reinvent logging from end to end for the AI era.

We have been building SMB, Mid-Market and Enterprise facing technology companies in both the US and Europe from the ground up for over 20 years now. We’ve spanned a very wide array of categories and technologies from ‘Email’ to ‘Logs’ to ‘eCommerce Analytics’ to ‘Voice-AI’ to ‘Customer Intelligence’ to ‘Digital Healthcare’, ‘Search’ and more.

We’ve been very fortunate to work with incredible people and build amazing teams – many of whom we’ve worked with again and again on multiple businesses. We’ve also been very fortunate to originate, develop, scale and sell our companies to the likes of Rapid 7, Apple, Publicis and many more of the world’s largest technology companies.

On our journey to date we have been founders, co-founders, investors (both private and institutional VC), dirt-road CEOs, Chairman – both exec and non-exec and very often we’ve worn multiple hats at the same time. To date, we have always focused on creating enterprise value up to US $300m and building companies from zero revenue to $30m + and we’ve always been exit-focused from the start. Until now that is.

Our team has 150 years+ of log-domain specific expertise — this is our fourth time building a logging platform. And along the way we’ve had lots of industry firsts:

• We built the first real time logging architecture as part of IBM’s on-prem solution back in the early 2000's
• We founded Logentries, one of the first SaaS log management platform companies, and we were the first to provide 'live tail' capabilities to the world (our board hated the name!)
• First to introduce bloom filters as an indexing approach (vs. inefficient inverted indexes)
• Plus decades of research papers, patents and logging innovation

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Our logging journey started when building an on-prem logging platform for IBM - as part of our post doctoral research - in the mid 2000s. We then started Logentries in 2010, which attracted thousands of SMB and mid-market customers, before being acquired by NASDAQ-listed cybersecurity leader Rapid7 in 2015. From 2016 to 2022 we built our third logging platform as part of Rapid7's InsightIDR security platform. We scaled to thousands of mid market and enterprise customers, built a petabyte scale technology that was ultra efficient and ultra reliable. InsightIDR grew from being a brand new product when we joined to becoming the engine that powered a US $5 billion business at its peak.

In late 2023 when we started to look closely again at the observability space a few things that were readily apparent felt very off -

• The market was comprised of vendors that all came to market pre 'separation of storage and compute’, never mind pre the AI era.
• Almost $70bn of market cap was accounted for by just two companies, Splunk and Datadog - companies founded in the early 00’s and early 10’s respectively.
• Datadog’s pricing was exorbitant and confusing and ‘justified’ by endless features nobody cared about.
• New Relic and Sumo had been taken over by private equity firms so we could assume that value and innovation creation would be replaced by a focus on growing topline and cutting costs before selling on these companies to the ‘next guy’ in the food chain. 
• Honeycomb’s CEO was trumpeting a spend on observability of 20% to 30% of your Infrastructure costs…...for real!!
• Nobody was innovating on behalf of the user and customer.

As we started to dig deeper, we were astonished to see that logging alone was in fact in a much worse state than when we started Logentries a decade earlier, and every tech organization seemed to be afflicted by this in some way. Months and months of diligence including conversations with Mid-market and Enterprise users and customers, across all industries and geographies confirmed everything we believed about how companies were logging in 2024….it was fundamentally broken and nobody was addressing it at a fundamental level. 

Bronto was thus born. We are now on a mission to build the logging layer for the AI era, which will involve fundamentally reinventing logging from end to end. We envisage this being a 10-20 year journey, but as a team we are motivated by the challenge and committed to seeing it through. As a founding team we are at a stage in our lives where we are motivated to do it right and take the long view. We are super excited for this journey.

• Everything can be built from a log…logs, traces and metrics.
• If something goes wrong or breaks engineers and SREs need logs to understand what happened.
• If there are compliance or audit requirements compliance teams need logs (maybe for the last 18 months, maybe going back years)
• If there are security breaches you need logs to see who did what and when (most security breaches go unnoticed for 300 days +)
• If a customer complains about slow APIs or page loads your customer support team need logs to validate their complaint and attach log events to tickets/bugs to be fixed by engineers
• And so on…

Logging, and observability in general, are fundamentally broken. This is a universal problem that every tech organisation is grappling with today.

The immediate problem

Logging is shockingly expensive. By default, most organisations go with one of the well-known logging/observability vendors (think Splunk, New Relic, Data Dog etc.). 

But the high costs of such platforms mean they have to tightly manage how much data they can 'fit' into them. This quickly creates a coverage problem as organizations reduce the retention period down to as little as 3, 7 or 15 days, and high volume logs are simply too expensive to even consider capturing.

Faced with a lack of coverage and blackspots organisations turn to sub-par logging solutions that on the surface look cheaper, but when total cost of ownership is baked in can work out even more expensive than the big vendors whose egregious pricing they are trying to solve for. Typically companies use some of the cloud platforms logging tools with sub-optimal experiences (Cloudwatch, Google Cloud Logging, Azure Log Monitor) etc or start to roll their own using open source (ELK, Clickhouse etc) as well as sending data to object store and using tools like Athena when analysis is required. What results is a hodge podge of logging infrastructure that is not fit for purpose, creates a painful management overhead and introduces needless complexity. In our experience the internal logging infrastructure is usually hacked together from at least 5, and quite often over 7, different solutions and technologies.

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At Bronto, we think there’s a better way. We imagine, and are building for, a world where…

• You have a single logging layer for your entire organization
• That is cost effective (orders of magnitude cheaper), gives you full coverage, removes the hodge podge of logging infrastructure within orgs today, is full SAAS and requires no management
• Is easy to use and has a business model aligned with the value you derive from the service not the merely the amount of data you store
• Is fit for the purpose for the AI era in terms of capabilities, user experience and cost model
• Does not require the use of grok or regex to parse your logs
• Does not require the user to create lists of PII rules for data scrubbing
• Does not penalize customers for overages they can not control
• Provides log hygiene and automatic volume controls
• Provides value attribution capabilities…. i.e. clear and transparent billing and usage information (hard to believe but it almost impossible today to figure out what is driving your logging bills)
• Provides lightening fast search across TB, PB of data and beyond
• Does not rely on vendor specific/complex query languages
• Does not provide a 15 year old user interface from the Splunk-era but instead provides an experience in line with the AI era
• Does not require management of hot and cold data
• Does not require the dropping of high volume logs, or reducing retention to 3 days
• Does not require management of complex retention policies
• Does not require rehydration of logs and managing of object storage (retention policies, access control)
• Is enabled for the AI era and integrates with your LLM of choice and the Agentic world
• Forms a core part of your data pipeline and integrates with all the tools you use (e.g. incident management, security, analytics etc)

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Imagine a single logging layer within your organisation that “just works”…….