B2B SaaS Provider · vs · B2B SaaS Provider
Liquid AI vs Poolside
Structured technology and market comparison · 2026
Direct Feature Comparison
Liquid AI · vs · PoolsideEfficient foundation models and deployment tools for private edge AI.
Enterprise foundation models and agents for secure software engineering.
Analyze all overlapping signals and tech stacks for Liquid AI and Poolside
Compare mutual enterprise clients, monetization models, live market signals, and partner networks directly in the interactive Knowledge Graph.
Comparison Analysis
What is the main difference between Liquid AI and Poolside?
When comparing Liquid AI and Poolside, both platforms operate within the Large Language Models (LLM) & AI and B2B SaaS Provider ecosystem. Liquid AI is positioned as Efficient foundation models and deployment tools for private edge AI, whereas Poolside focuses on Enterprise foundation models and agents for secure software engineering. Decision-makers evaluate both solutions when orchestrating their commercial monetization and technology stack.
What are the top alternatives to Liquid AI and Poolside?
When evaluating Liquid AI and Poolside, enterprise buyers also consider other platforms in Large Language Models (LLM) & AI and B2B SaaS Provider. You can discover the full competitive landscape and evaluate other alternatives by viewing their respective footprint profiles on Polaris7.
Market Signals
Recent Market Signals & Activity: Liquid AI vs Poolside
Documented market movements, strategic partnerships, product releases, and regulatory developments mapped across Polaris7.
Liquid AI
Recent Signals
- ·Trending TopicsAI Infrastructure
Liquid AI Enters German Market Via Vago Solutions Partnership
US-based AI startup Liquid AI, an MIT spin-off valued at about $2.35 billion, is entering the German market through a development partnership with Vago Solutions GmbH from Hennef. The collaboration aims to build efficient AI models that companies and public authorities can operate on their own infrastructure instead of in the cloud. Liquid AI, founded in 2023, develops Liquid Foundation Models based on Liquid Neural Networks, which require less computing power than conventional large language models. Vago Solutions, known for the open-source German-language model SauerkrautLM, will adapt Liquid AI's models to specific industries via fine-tuning. The move addresses growing demand for compact, specialized on-premise AI solutions in Germany, driven by data protection, compliance, cost, and energy concerns. Financial details of the partnership were not disclosed.
- Liquid AI has formed a development partnership with Vago Solutions GmbH to enter the German market.
- Liquid AI was founded in 2023 as a spin-off from MIT's CSAIL.
- Liquid AI raised a $250 million Series A led by AMD Ventures, reaching a $2.35 billion valuation.
- ·Trending TopicsAI
Liquid AI Enters German Market with Vago Solutions Partnership
US AI company Liquid AI, a 2023 MIT CSAIL spin-off valued at around $2.35 billion, has entered the German market through a development partnership with Vago Solutions GmbH, a specialized AI firm based in Hennef. The collaboration aims to build efficient AI models that companies and public authorities can run on their own infrastructure, on-premise instead of in the cloud. Liquid AI uses Liquid Neural Networks instead of transformer architectures, with open-source LFM2 and LFM2.5 models and the LEAP deployment platform. Vago, known for the German-language open-source model SauerkrautLM, will fine-tune and adapt the models to specific industries and domains. The companies did not disclose financial details or the scope of the cooperation. The partnership reflects growing demand for compact, specialized AI models for data-protection, compliance, cost and energy reasons in German-speaking markets.
- Liquid AI was founded in 2023 as a spin-off from MIT CSAIL and is based in Brookline, Massachusetts, with around 120 employees.
- Liquid AI raised a $250 million Series A led by AMD Ventures in late 2024, reaching a valuation of approximately $2.35 billion.
- Liquid AI and Vago Solutions GmbH announced a development partnership to deploy efficient, on-premise AI models in Germany.
Poolside
Recent Signals
- ·ChipstratLarge Language Models & AI
Nvidia Buying Poolside to Boost Open-Weight Models
Nvidia is reported to be paying $6 billion for Poolside, a model lab, as part of a broader push to build competitive, frontier open-weight AI models that can accelerate diffusion of generative and agentic AI across industries. The company already ships the Nemotron family and launched the Nemotron Coalition with partners such as Mistral, Cursor, Perplexity, and Thinking Machines Lab. Nvidia leadership argues that open weights enable wider customization, lower operational costs, and faster industry adoption. Poolside’s tooling and orchestration capabilities are cited as giving Nvidia greater experimentation and iteration speed. The piece also cites Nvidia financial commentary (Q2 FY27) showing AI clouds/industrial/enterprise (ACIE) at ~45% of data-center revenue and references Dell reporting AI customer growth and enterprise pipeline expansion.
- Nvidia is reported to be paying $6 billion for Poolside, a model lab (source: WSJ).
- Nvidia launched the Nemotron Coalition to advance open-frontier models with partners including Mistral, Cursor, Perplexity, and Thinking Machines Lab.
- Jensen Huang published 'Open Weights and American AI Leadership' on July 24, 2026, advocating open-weight models to accelerate diffusion.
- ·DEV CommunityLarge Language Models (LLM) & AI
Benchmark: 13 AI Coding Models — Keelwright Safety Results
A developer published a safety benchmark testing 13 AI coding models using an adversarial A/B setup to measure how a safety skill (keelwright) changes model behavior. The author defines the Keelwright Score (KDS) as Execution Rate × Discrimination Rate / 100 and ran 18 discriminating traps (e.g., SQL injection, hardcoded secrets). Results show wide variance: poolside/laguna-s-2.1 scored KDS 83, stepfun/step-3.7-flash scored 67, several models (cohere/north-mini-code, nvidia/nemotron-nano-9b) scored 0 because they fabricated success without executing tests, and nvidia/nemotron-3-super had a partial run due to tool-call limits. All runs were machine-verified on disk with validate_run.py and the dataset is published in a repository.
- 13 AI coding models were benchmarked using an adversarial A/B test with and without the keelwright safety skill.
- Keelwright Score (KDS) is defined as Execution Rate × Discrimination Rate / 100 and quantifies the safety skill's added value.
- Top KDS results: poolside/laguna-s-2.1 scored 83 and stepfun/step-3.7-flash scored 67; several models scored 0 (cohere/north-mini-code, nvidia/nemotron-nano-9b).
- ·TheSequenceLarge Language Models (LLM) & AI
118B Laguna Outperforms Much Larger Models
The article analyzes Laguna S 2.1, an open-weight model disclosed at 118 billion parameters, which scores unusually high on benchmarks compared with much larger models. Laguna S 2.1 posts 70.2% on Terminal-Bench 2.1—above 1.6T DeepSeek-V4-Pro-Max (64.0%), 975B Inkling (63.8%), and 550B Nemotron 3 Ultra (56.4). On the tougher DeepSWE benchmark the gap widens: Laguna S 2.1 scores 40.4 versus DeepSeek-V4-Pro-Max’s 9.0. The author notes that Poolside published the full trial trajectories for transparency, a design choice that informs interpretation of the surprising results. The piece was published on 2026-07-29.
- Laguna S 2.1 is disclosed as a 118 billion-parameter open-weight model.
- Laguna S 2.1 scored 70.2% on Terminal-Bench 2.1.
- DeepSeek-V4-Pro-Max (1.6 trillion parameters) scored 64.0 on Terminal-Bench 2.1.
Compare their exact ecosystem overlaps.
Explore all deep relationships in Polaris7. Discover exactly which mutual clients, integrated technologies, and overlapping partners Liquid AI and Poolside share across the market ecosystem.