Company profile: Weights & Biases
1.1 - Company Overview
Company description
- Provider of a developer-first MLOps platform offering performance visualization tools for machine learning, helping companies turn deep learning research into deployed software by enabling teams to track models, visualize performance, and automate training and model improvement.
Products and services
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Key contacts
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Financial details
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1.2 - Competitors and similar companies to Weights & Biases
Markable AI
HQ: United States
Website
- Description: Provider of an AI visual discovery shopping engine that empowers influencers with their own social shops and enables visual discovery shopping on photos and videos across social platforms and devices. Offers tools including AI Collage, Auto DM Reply, Bio Link, Deeplink for direct links to specific content, Viral Products to highlight trending items, and AI Caption for social post captions.
- Rationale: This competitor operates in a highly similar market segment with overlapping features.
- Company type: Private company
- Employees: ●●●●●
- Total funding raised: $●●●m
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Safaba
HQ: United States
Website
- Description: Provider of enterprise machine translation solutions enabling high-volume, high-velocity content localization and enterprise translation for leading global enterprises.
- Rationale: This competitor operates in a highly similar market segment with overlapping features.
- Company type: Private company
- Employees: ●●●●●
- Total funding raised: $●●●m
- Backers: ●●●●●●●●●●
- Acquisitions: ●●
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Affectiva
HQ: United States
Website
- Description: Provider of Emotion AI software and services, including an SDK for real-time emotion sensing in apps, in-cabin sensing AI for occupant monitoring, safety and personalized mobility experiences, media analytics for ad testing, entertainment content and qualitative research, and a behavioral research platform with iMotions integrating emotion recognition and biometric sensors.
- Rationale: This competitor operates in a highly similar market segment with overlapping features.
- Company type: Private company
- Employees: ●●●●●
- Total funding raised: $●●●m
- Backers: ●●●●●●●●●●
- Acquisitions: ●●
View full Affectiva company profile →
Anthropic
HQ: United States
Website
- Description: Provider of AI research and products focused on safety and alignment, including the Claude model family (Claude 3, Opus, Sonnet, Haiku) for analysis, content creation, multilingual processing, and custom experiences. Offers an API for developer integration and enterprise-grade security (SOC II Type 2, HIPAA). Supports tasks from fast simple requests to complex multi-step workflows.
- Rationale: This competitor operates in a highly similar market segment with overlapping features.
- Company type: Private company
- Employees: ●●●●●
- Total funding raised: $●●●m
- Backers: ●●●●●●●●●●
- Acquisitions: ●●
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FotoNation
HQ: United States
Website
- Description: Provider of computational imaging and computer vision solutions for consumer and enterprise applications, developing technologies for handsets and cameras and serving the automotive, surveillance, security, and augmented reality markets.
- Rationale: This competitor operates in a highly similar market segment with overlapping features.
- Company type: Private company
- Employees: ●●●●●
- Total funding raised: $●●●m
- Backers: ●●●●●●●●●●
- Acquisitions: ●●
View full FotoNation company profile →
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2.M&A buyers
2.1 Potential strategic acquirers in the sector
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2.2 - Strategic buyer groups for Weights & Biases
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Buyer group 2: ████████ ████████
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Key Products: ████████, ████████, ████████, ████████
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Buyer group 3: ████████ ████████
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Description: ████████ ████████ ████████ ████████ ████████ ████████ ████████ ████████ ████████ ████████
Key Products: ████████, ████████, ████████, ████████
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3. Investors and private equity firms
3.1 - Buyout funds investing in similar companies to Weights & Biases
2.2 - Growth funds investing in similar companies to Weights & Biases
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4 - Top valuation comps for Weights & Biases
4.2 - Public trading comparable groups for Weights & Biases
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