Snowflake, Anthropic ink $200M deal🤝

Also, The juxtaposition of Databases, Platform Engineering and Site Reliability

Author

Gladstone Benjamin
December 15, 2025

What’s in today’s newsletter:

Also, check out the weekly Deep Dive - Databases, Platform Engineering and Site Reliability.

SNOWFLAKE

TL;DR: Snowflake and Anthropic expanded their $200M partnership to embed safe, autonomous Claude AI models into Snowflake’s platform, enabling smarter enterprise AI applications and advancing ethical, agentic AI adoption.

  • Snowflake and Anthropic expanded their partnership with a $200 million deal to integrate agentic AI into Snowflake’s platform.

  • Anthropic's Claude AI models will be embedded in Snowpark, enhancing enterprise AI applications with safety and reliability.

  • The collaboration aims to deliver intelligent, autonomous AI systems that improve data workflows and decision-making for businesses.

  • This deal reflects industry trends toward ethical AI deployment and closer ties between data infrastructure and AI innovators.

Why this matters: This $200M partnership accelerates enterprise AI by merging Snowflake’s data platform with Anthropic’s safe, autonomous AI models, enabling smarter workflows and ethical AI use. It exemplifies a pivotal shift towards integrating agentic AI for innovation, efficiency, and responsible deployment in business operations.

IBM

TL;DR: IBM will acquire Confluent for $11 billion, integrating real-time Apache Kafka streaming to enhance hybrid cloud, accelerate AI capabilities, boost analytics, and drive faster decision-making across industries.

  • IBM will acquire Confluent for approximately $11 billion to boost hybrid cloud and AI capabilities.

  • Confluent’s Apache Kafka-based platform enables real-time data streaming crucial for industries like finance and retail.

  • The deal aims to accelerate IBM’s hybrid cloud growth and enhance AI through continuous, fresh streaming data.

  • IBM’s acquisition strengthens its cloud competitiveness with improved real-time analytics and faster business decision-making.

Why this matters: IBM’s $11 billion acquisition of Confluent signals a significant shift toward real-time data streaming, boosting hybrid cloud and AI capabilities. This enhances competitiveness in data-intensive industries by enabling faster decisions and continuous insights, crucial for digital transformation and innovation in finance, retail, and beyond.

VECTOR DATABASE

TL;DR: AWS upgraded S3 with scalable, low-latency vector search supporting billions of vectors, enabling real-time AI similarity searches directly on existing data lakes without migration, boosting enterprise AI application development.

  • AWS enhanced S3 with vector search capabilities supporting billions of vectors for real-time, large-scale similarity searches.

  • Optimized indexing and query mechanisms reduce latency and improve throughput for complex AI and machine learning applications.

  • Integration with S3 enables users to run vector searches on existing data lakes without data migration, simplifying workflows.

  • This update facilitates easier adoption of AI-driven applications like recommendations, fraud detection, and multimedia retrieval at scale.

Why this matters: Amazon’s upgraded S3 vector search empowers enterprises to efficiently analyze massive datasets in real time, streamlining AI-driven innovations like recommendations and fraud detection. By integrating with existing data lakes, this reduces complexity and costs, accelerating adoption of advanced machine learning applications across industries.

DATABRICKS

TL;DR: Former Databricks AI chiefs launched a startup raising $475M at $4.5B valuation to revolutionize computing with novel AI hardware/software, aiming to improve speed, efficiency, and scalability beyond traditional models.

  • Former Databricks AI leaders launched a startup focused on redesigning computing with unconventional AI approaches.

  • The company raised $475 million, attaining a $4.5 billion valuation to fund innovative AI hardware and software development.

  • Their new AI systems aim to surpass traditional models by improving speed, energy efficiency, and scalability.

  • This venture could drive significant breakthroughs in AI, influencing industries and inspiring broader disruptive innovation.

Why this matters: This startup's radical approach to AI computing promises to overcome current speed, energy, and scalability bottlenecks, potentially revolutionizing multiple industries dependent on machine intelligence. The strong funding and leadership signal a shift toward more foundational AI innovation beyond incremental upgrades, shaping the future tech landscape.

EVERYTHING ELSE IN CLOUD DATABASES

DEEP DIVE

Databases, Platform Engineering and Site Reliability.

  • Wyatt Earp

  • Doc Holliday

  • Jesse James

  • Billy the Kid

  • Hoss Cartwright 

  • Little Bill Daggett

  • The Lone Ranger

  • Rip Wheeler

What do these names have in common? They are all famous, cowboys. Before you question whether you are reading the right newsletter, hear me out.

I bring up these names in order to get you thinking about how things are done in a lot of shops without controls (i.e. the Wild West). I am not going to be sanctimonious and say that I have never worked in such a place.

What I have noticed is that there is a push towards Platform Engineering and Site Reliability practices, and relating them to database operations. Think accepted practices and proper governance

Here is my technical overview of Databases with Site Reliability and Platform Engineering:

Databases in Platform Engineering

Platform engineering focuses on building Internal Developer Platforms (IDPs) that provide self-service tools, workflows, and abstractions to boost developer productivity while enforcing standards, security, and compliance. Databases play a central role here because they are a common dependency for most applications—developers frequently need to provision, configure, scale, or migrate databases without waiting on specialized teams.

Key ways databases integrate with platform engineering:

  • Database as a Service (DBaaS) in the IDP: Platform teams often expose databases through self-service portals or APIs. Developers can request a new database (e.g., PostgreSQL, MySQL, MongoDB) via a UI, CLI, or GitOps workflow, and the platform automates provisioning using tools like Crossplane, Terraform, or cloud-native operators (e.g., on Kubernetes). This turns databases into "golden paths"—standardized, pre-approved configurations that include backups, monitoring, and security policies.

  • Abstraction and self-service: Instead of developers managing raw infrastructure (e.g., VMs or cloud instances), the platform hides complexity. For example:

    • Ephemeral databases for testing/CI/CD.

    • Production-grade instances with high availability, encryption, and scaling.

    • Integration with CI/CD pipelines for schema migrations (using tools like Flyway or Liquibase).

  • Examples in practice:

    • Managed services like Amazon RDS, Azure SQL, or Google Cloud SQL are wrapped into the IDP.

    • Kubernetes-based platforms (e.g., with Portworx or Nutanix) offer unified DBaaS across clouds.

    • Tools like Backstage or Port.io include software catalogs where databases are modeled as resources developers can "check out."

This shifts database management from a bottleneck (ticket-based DBA requests) to an enabler of faster development cycles.

Databases in Site Reliability Engineering (SRE)

Site Reliability Engineering applies software engineering principles to operations, emphasizing reliability, scalability, and automation. Databases are often the most critical (and fragile) component in a system's reliability stack—they handle stateful data, have low tolerance for downtime, and can become single points of failure.

Key relations:

  • Database Reliability Engineering (DBRE) as a SRE specialization: Many organizations have dedicated DBRE roles or teams that apply SRE practices specifically to databases. DBREs treat databases like distributed systems, focusing on:

    • Defining Service Level Objectives (SLOs), Indicators (SLIs), and error budgets for database availability, latency, and durability.

    • Automation of backups, failovers, scaling, and patching.

    • Performance tuning, query optimization, and capacity planning.

  • Core SRE practices for databases:

    • Monitoring and observability: Track golden signals (latency, errors, saturation, traffic) plus database-specific metrics (e.g., replication lag, lock contention, redo logs).

    • Incident response and postmortems: Databases are common outage sources; SREs run blameless postmortems and implement safeguards like chaos engineering for failovers.

    • High availability and disaster recovery: Multi-region replication, automated backups, and point-in-time recovery.

    • Change management: Safe schema migrations, canary deployments, and rollback strategies to avoid downtime.

  • Examples:

    • Companies like GitLab and Google have DBRE teams working alongside general SREs.

    • Tools like Prometheus for metrics, or specialized ones (e.g., DBmarlin) for query analysis.

In essence, SRE ensures databases don't undermine overall system reliability, often evolving traditional DBA roles into more proactive, code-driven ones.

Why This Matters for Modern Systems

Databases bridge these concepts: In platform engineering, they're a self-service product that empowers developers. In SRE, they're a reliability hotspot requiring rigorous engineering. Together, they enable scalable, resilient applications—think microservices architectures where apps depend on multiple databases, all managed through paved roads (platform) while meeting strict reliability targets (SRE).

Gladstone Benjamin