Liquid AI Doesn't Have a Free Plan — Here's What It Costs

⚡ Quick Verdict

No free plan. The cheapest way in is Listed model offers at $0 USD. Consider free alternatives in the ai infrastructure & training category if budget is tight.

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Who Should Pay for This

👤

Best For

✓Established business
✓Budget for premium tools
✓Need ai infrastructure & training features
✓Professional use case
✓Want official support

What Users Say About Liquid AI

👍 What Users Love

✓Liquid AI was founded on 2023-12-06 as an MIT spin-out, giving it a clear research-oriented origin rather than being a generic model wrapper.
✓The published model library lists 20 Liquid Foundation Models spanning text, vision-language, audio, and nano models for on-device, cloud, and hybrid deployment.
✓The website explicitly states optimization for CPUs, GPUs, and NPUs, which is valuable for teams deploying AI outside standard cloud GPU environments.
✓Several listed models, including LFM2-350M and LFM2-700M, show $0 USD offers in the website schema, making experimentation more accessible where those model terms apply.
✓The model lineup includes specific compact and efficient options such as 350M, 700M, 1.2B, 8B-A1B, and 24B-A2B, giving developers concrete size choices for different hardware budgets.
✓Liquid AI is positioned for privacy-critical, low-latency, and security-critical applications, making it a strong fit for regulated or edge-heavy deployments.

👎 Common Concerns

⚠The provided website content does not show a complete public pricing table for enterprise, cloud, or support plans, so budgeting may require contacting sales.
⚠Liquid AI is relatively young, with a founding date of 2023-12-06, so buyers may want to validate production references and long-term support maturity.
⚠The website emphasizes model infrastructure rather than an out-of-the-box end-user assistant, so teams may need engineering resources to integrate and deploy it.
⚠Although the model library lists 20 models, that is still narrower than the model and tooling ecosystems around larger providers such as OpenAI, Anthropic, Google, or Together AI.
⚠The scraped content does not provide public benchmarks, latency numbers, supported context lengths, licensing terms, or deployment SLAs for every model, which may slow procurement and technical evaluation.

Frequently Asked Questions

What does Liquid AI actually provide?

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.

Is Liquid AI free to use?

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.

What hardware can Liquid AI models run on?

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.

How many models does Liquid AI offer?

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.

Who should consider Liquid AI instead of OpenAI, Anthropic, or Gemini?

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.

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Last verified March 2026

What Users Say About Liquid AI

👍 What Users Love

✓Liquid AI was founded on 2023-12-06 as an MIT spin-out, giving it a clear research-oriented origin rather than being a generic model wrapper.
✓The published model library lists 20 Liquid Foundation Models spanning text, vision-language, audio, and nano models for on-device, cloud, and hybrid deployment.
✓The website explicitly states optimization for CPUs, GPUs, and NPUs, which is valuable for teams deploying AI outside standard cloud GPU environments.
✓Several listed models, including LFM2-350M and LFM2-700M, show $0 USD offers in the website schema, making experimentation more accessible where those model terms apply.
✓The model lineup includes specific compact and efficient options such as 350M, 700M, 1.2B, 8B-A1B, and 24B-A2B, giving developers concrete size choices for different hardware budgets.
✓Liquid AI is positioned for privacy-critical, low-latency, and security-critical applications, making it a strong fit for regulated or edge-heavy deployments.

👎 Common Concerns

⚠The provided website content does not show a complete public pricing table for enterprise, cloud, or support plans, so budgeting may require contacting sales.
⚠Liquid AI is relatively young, with a founding date of 2023-12-06, so buyers may want to validate production references and long-term support maturity.
⚠The website emphasizes model infrastructure rather than an out-of-the-box end-user assistant, so teams may need engineering resources to integrate and deploy it.
⚠Although the model library lists 20 models, that is still narrower than the model and tooling ecosystems around larger providers such as OpenAI, Anthropic, Google, or Together AI.
⚠The scraped content does not provide public benchmarks, latency numbers, supported context lengths, licensing terms, or deployment SLAs for every model, which may slow procurement and technical evaluation.