Enables team to spend 99% of time on customers and product
Scales from zero to thousands of queries per second
Delivers sub-second performance with serverless infrastructure
Tracks infrastructure costs down to individual customers
Generates significant revenue through Google Cloud Marketplace
Alvin automates BigQuery optimization, cutting compute spend by up to 60% while freeing teams to focus on higher-value work.
Alvin automates BigQuery optimization, cutting compute spend by up to 60% while freeing teams to focus on higher-value work.
Helping data teams spend less time optimizing infrastructure
As organizations scale their use of data, getting the best balance of performance and cost from increasingly complex workloads becomes an important—and often highly specialized—job.
Drawing on decades of combined experience building data platforms, the Alvin team recognized a common frustration. Most optimization tools looked backwards—analyzing yesterday’s workloads, producing long lists of recommendations, and leaving engineers to implement them manually.
For data teams, acting on those recommendations could mean spending valuable engineering time evaluating pricing models, tuning workloads, and continuously monitoring usage alongside their core work. That was the challenge CTO and Co-founder Martin Sahlen and his team set out to solve.
“Data teams are busy—we wanted to build something that quietly generates value in the background,” Sahlen explains. “Google Cloud was a natural foundation for that as its serverless service gives us the scale and cost efficiency we need without adding infrastructure for our own team to manage.”
By drawing on BigQuery’s flexible consumption and pricing models, Alvin can make those cost and performance decisions automatically, query by query, and in real time.
Rather than just helping organizations understand what has already happened, Alvin analyzes BigQuery workloads instantaneously, automatically selecting the most cost-effective way to execute queries while maintaining performance.
For Sahlen, whose team has spent more than a decade working with BigQuery, Google Cloud offered the combination of native integration, serverless infrastructure, and fully managed services needed to deliver that automation at scale.
Balancing BigQuery cost and performance at scale
For customers, Alvin is designed to require remarkably little effort. A data team grants metadata-only access to its BigQuery environment, allowing Alvin to analyze query patterns without accessing the underlying data. Within around 30 minutes to an hour for a typical environment, Alvin can identify potential savings based on the previous 30 days of activity. Once connected, it sits between customers’ tools and BigQuery, automatically selecting the most cost-effective way to execute queries as they run.
We’re a cost-optimization company, so we have to practice what we preach. With Google Cloud serverless services, we can support massive scale very cost-efficiently and spend almost all our time on customers and product rather than provisioning infrastructure.
Martin Sahlen
CTO and Co-founder, Alvin
Behind that simple experience is a serverless-first architecture built entirely on Google Cloud. Cloud Run handles data processing and real-time interactions with customer queries, scaling with demand and billing by the second. BigQuery, Cloud Storage, and Firestore form a tiered caching architecture that gives Alvin fast access to the metadata it needs to balance performance and cost in real time and deliver sub-second responses at scale.
Rather than maintaining capacity for peak demand, Alvin can scale resources up for spikes and back down when they’re no longer needed. It can even understand infrastructure costs at the individual customer level and balance cost against latency according to different workload requirements.
“We’re a cost-optimization company, so we have to practice what we preach. With Google Cloud serverless services, we can support massive scale very cost-efficiently and spend almost all our time on customers and products rather than provisioning infrastructure,” Sahlen explains.
Turning BigQuery savings into room to innovate
For Alvin customers, the impact can be immediate: BigQuery compute costs can fall by 45% to 60%, without sacrificing the performance their teams depend on. “Customers value the savings, but also realize they don’t have to become BigQuery optimization experts. Building that expertise can take months, and staying current as BigQuery rapidly evolves takes continuous effort. Alvin handles that complexity, so data teams can focus on creating value for their business,” says Sahlen.
Increasingly, Alvin is also helping customers think beyond cost alone. Some overnight workloads can run more slowly if that makes them cheaper, while customer-facing analytics may demand maximum speed. By understanding those trade-offs at the workload level, Alvin automatically balances cost and performance according to each workload’s needs, rather than simply minimizing spend.
Acting as an extension of its customers’ data teams, Alvin applies specialist BigQuery expertise automatically and at a scale, helping organizations take advantage of BigQuery’s flexibility and extending capabilities without having to continually build that expertise in-house.
As organizations invest more in AI, making data workloads more efficient can also free budget for strategic initiatives such as Gemini for Google Cloud. AI agents themselves are also becoming a new source of unpredictable BigQuery queries, increasing the value of automated cost and performance management as technology environments evolve. Alvin doesn’t use AI agents to make optimization decisions itself; instead, its deterministic approach keeps those decisions consistent, provable, and auditable.
Meanwhile, Alvin’s relationship with Google Cloud supports its growth. As a BigQuery validated solution partner, Alvin has undergone technical validation of its integration with BigQuery. Separately, its availability on Google Cloud Marketplace helps Alvin reach more customers and enables eligible organizations to purchase its services against existing cloud commitments. Marketplace has become a significant channel for Alvin, supporting its growth while simplifying procurement for customers.
For Alvin, the opportunity is to help more organizations get greater value from BigQuery, while giving their data teams more time and budget to focus on what comes next.
Customers don’t just tell us they’re happy about the savings; there’s also this realization: ‘we don’t have to think about this anymore.’ Instead of spending time learning how to optimize BigQuery, they can focus on the work that actually creates value for their business.
Martin Sahlen
CTO and Co-founder, Alvin
Alvin helps organizations get more from BigQuery by automatically optimizing workloads for cost and performance, freeing data teams to focus on creating value from their data.
Industries: Data Management and FinOps, Startup
Location: Estonia
Products: BigQuery, Cloud Run, Cloud Storage, Firestore, Google Cloud
About Google Cloud partner – Devoteam
Devoteam is a Google Cloud partner helping organizations use cloud, data, and AI technologies to transform operations, accelerate innovation, and drive sustainable growth.
