How to get the best deals on Liquid AI — pricing breakdown, savings tips, and alternatives
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Don't overpay for features you won't use. Here's our recommendation based on your use case:
Most AI tools, including many in the ai infrastructure & training category, offer special pricing for students, teachers, and educational institutions. These discounts typically range from 20-50% off regular pricing.
• Students: Verify your student status with a .edu email or Student ID
• Teachers: Faculty and staff often qualify for education pricing
• Institutions: Schools can request volume discounts for classroom use
Most SaaS and AI tools tend to offer their best deals around these windows. While we can't guarantee Liquid AI runs promotions during all of these, they're worth watching:
The biggest discount window across the SaaS industry — many tools offer their best annual deals here
Holiday promotions and year-end deals are common as companies push to close out Q4
Tools targeting students and educators often run promotions during this window
Signing up for Liquid AI's email list is the best way to catch promotions as they happen
💡 Pro tip: If you're not in a rush, Black Friday and end-of-year tend to be the safest bets for SaaS discounts across the board.
Test features before committing to paid plans
Save 10-30% compared to monthly payments
Many companies reimburse productivity tools
Some providers offer multi-tool packages
Wait for Black Friday or year-end sales
Some tools offer "win-back" discounts to returning users
If Liquid AI's pricing doesn't fit your budget, consider these ai infrastructure & training alternatives:
cloud platform for open-source model inference, fine-tuning and training
Starting at $0.02/1M tokens
Google's flagship AI assistant combining real-time web search, multimodal understanding, and native Google Workspace integration for productivity-focused users.
Free tier available
✓ Free plan available
Liquid AI provides Liquid Foundation Models, a library of efficient multimodal AI models intended for on-device, cloud, and hybrid deployment. The website describes the company as building models optimized for CPUs, GPUs, and NPUs, with use cases that include privacy-critical, low-latency, and security-critical applications. The listed model catalog includes 20 models across text, vision-language, audio, and nano categories. This makes Liquid AI more of an AI infrastructure and model provider than a simple chatbot product.
The provided website schema lists several model offers at a price of $0 USD, including entries such as LFM2-350M, LFM2-700M, LFM2-8B-A1B, LFM2-24B-A2B, and LFM2.5-1.2B-Base. However, the scraped content does not include a complete pricing page with all commercial tiers, enterprise support pricing, usage-based API rates, or deployment fees. For this directory entry, pricing should be treated as free for listed model offers and custom for broader enterprise usage. Organizations should confirm licensing, hosting, support, and production terms directly with Liquid AI.
Liquid AI says its models are optimized for CPUs, GPUs, and NPUs. That is important because many AI deployments depend on non-cloud environments such as laptops, phones, embedded systems, vehicles, or enterprise-controlled hardware. The website positions the models for on-device, cloud, and hybrid deployment rather than only centralized GPU inference. Teams should still test the exact model size, memory usage, and latency on their target hardware before committing.
The website schema lists 20 Liquid Foundation Models in the complete library. Examples from the provided content include LFM2-350M, LFM2-700M, LFM2-8B-A1B, LFM2-24B-A2B, and LFM2.5-1.2B-Base. The catalog spans text, vision-language, audio, and nano models, which suggests Liquid AI is building a model family rather than a single flagship model. This variety is useful for teams that need to match model size and modality to device constraints.
Liquid AI is most relevant for teams that need efficient models deployed close to the user or inside controlled infrastructure. If the priority is privacy-critical, low-latency, or security-critical inference on CPUs, GPUs, or NPUs, Liquid AI fits better than a cloud-only assistant workflow. OpenAI, Anthropic, and Gemini may be better choices for teams that primarily want mature hosted APIs, broad ecosystem tooling, or general-purpose assistant capabilities. Based on our analysis of 870+ AI tools, Liquid AI should be evaluated as deployment-focused model infrastructure rather than a general productivity assistant.
Check out their current pricing and look for seasonal promotions
Get Started with Liquid AI →Pricing and discounts last verified March 2026