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Ashish

Ashish Kakran

PARTNER

Ashish Kakran is a Partner at Sierra Ventures, where he leads early-stage investments in AI, cloud infrastructure, and cybersecurity.  Before Sierra, Ashish spent seven years in venture, most recently as a Partner at Thomvest Ventures, where he invested in companies including Cohere, Harness, Isovalent (acquired by Cisco), Opaque, and Exaforce.

Ashish is known for working closely with technical founders. Prior to venture, he spent nearly a decade in engineering and product leadership roles at early-stage infrastructure startups, giving him firsthand insight into company building from the ground up.

He holds a BS in Computer Science from IIT and an MBA from the Kellogg School of Management. Ashish has been recognized by VC Journal’s “40 Under 40” and Business Insider’s “Rising Stars in VC.” 

Focus

Ask Ashish

What are the most promising areas within AI software development that you're currently tracking?

Foundation model infrastructure, data-centric AI tools, and verticalized AI applications that embed directly into enterprise workflows are particularly compelling right now.

How do you evaluate the potential of early-stage AI startups, especially those building foundational models or developer tools?

I look at technical depth, team credibility, defensibility of approach, and how closely their product ties into real-world usage patterns or developer needs.

What are the key trends in cloud infrastructure that startups should align with to stay competitive?

The shift to multi-cloud, Kubernetes-native environments, and serverless architectures is creating new opportunities for automation, observability, and security tooling.

How do you assess the maturity and scalability of a startup's MLOps capabilities?

Startups with clear CI/CD pipelines for ML, robust data versioning, and real-time monitoring of models in production stand out—they’ve moved beyond experimentation.

What aspects of the modern data stack are most critical for enterprise adoption, and how should startups position themselves accordingly?

Seamless data integration, low-latency query performance, and governance features are top of mind for enterprise buyers. Clear interoperability helps accelerate adoption.

What common pitfalls do you see AI startups encounter when scaling their infrastructure and teams?

Over-engineering too early, lack of GTM alignment, and skipping user feedback loops often lead to misfires. Engineering rigor must be paired with customer insight.

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Cohere
Harness
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Opaque
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