Sold by: FriendliAI
Deployed on AWS
Unleash the power of your generative AI with Friendli Engine, the fastest LLM serving engine on the market! Friendli Suite offers three options to suit your preferences: Friendli Container, Friendli Dedicated Endpoints, and Friendli Serverless Endpoints.
4.4
Overview
Friendli Suite is our SaaS service powered by Friendli Engine, the fastest LLM serving engine on the market. Friendli Suite offers three options to suit your preferences:
Friendli Container: Deploy the engine on your own infrastructure for ultimate control. Friendli Dedicated Endpoints: Run any custom generative AI models on dedicated GPU instances in autopilot. Friendli Serverless Endpoints: No setup required, simply call our APIs and let us handle the rest. The Friendli Engine cuts LLM inference serving costs by 40-80% while providing low latency and high throughput during LLM serving. Friendli Engine employs various specialized optimizations, including our groundbreaking iteration batching (a.k.a. continuous batching) technology, protected by patents in the United States and Korea. Experience the extraordinary performance of LLM serving on the Friendli Suite!
Highlights
- Innovation for high speed and low cost
- Effortless deployment
- Automatic management
Details
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
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Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
$5,000 | $5,000 Friendli Suite Plan | $5,000.00 |
$10,000 | $10,000 Friendli Suite Plan | $10,000.00 |
$20,000 | $20,000 Friendli Suite Plan | $20,000.00 |
Dimension | Description | Cost/unit |
|---|---|---|
Usage Fee | Additional Usage Fee | $0.01 |
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Legal
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Support
Vendor support
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AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
Kimberly A.
Affordable, Clean Platform with Diverse AI Models and Easy API Key Management
Reviewed on Oct 17, 2026
Review provided by G2
What do you like best about the product?
Frendli Ai offers a diverse selection of AI models. It makes API key management easy, and it’s pretty affordable, although the cost depends on the model you choose. The interface is clean and, overall, the platform is straightforward to use.
I haven’t needed to contact support because getting started was simple, and everything has performed fine so far. It also lets me integrate the AI models easily into my business workflow. Frendli doesn’t have AI built into the platform itself in the sense of a standalone AI tool, but it does provide access to the models, as I mentioned earlier.
I haven’t needed to contact support because getting started was simple, and everything has performed fine so far. It also lets me integrate the AI models easily into my business workflow. Frendli doesn’t have AI built into the platform itself in the sense of a standalone AI tool, but it does provide access to the models, as I mentioned earlier.
What do you dislike about the product?
I don’t like the way they organize AI models. Sometimes it’s hard to find the specific one I used last time if I can’t remember its name. Adding members is also difficult. At times, the person I’m trying to add doesn’t receive the invite email, which is really frustrating. Because of that, I mostly use Frendli by myself and keep my members on another similar platform.
What problems is the product solving and how is that benefiting you?
Having access to so many AI models is uncommon, and with the number of models available, it basically solves any problem I run into within my workflow.
Vedika C.
Cost Efficient LLM Inference That Scales Personalization Without Breaking Budget
Reviewed on Oct 17, 2026
Review provided by G2
What do you like best about the product?
The fast and cost efficient LLM inference has been a real advantage for our team because we can generate and test large volumes of ad copy variations and personalized messaging at a speed that was simply not possible before and I really appreciate how straightforward the API is to work with because our team was able to integrate it into our existing campaign workflows without needing significant technical support and the token efficiency means we are getting more output for less cost which has had a noticeable positive effect on our AI spend across the quarter.
What do you dislike about the product?
The range of available model options could be expanded a little further particularly for teams looking to experiment with more specialized or domain specific models for niche marketing use cases and broader model support would make the platform even more versatile.
What problems is the product solving and how is that benefiting you?
Generating personalized content at scale used to be one of our biggest bottlenecks because the tools we had were either too slow or too expensive to run at the volume our campaigns required but now Friendli AI lets us produce high quality personalized messaging across multiple channels at a fraction of the previous cost and the efficiency gains have been significant enough that we have been able to scale our personalization efforts without increasing our overall AI budget.
Computer & Network Security
Flexible Multi-LLM Support with Clear Endpoints and a Helpful Usage Dashboard
Reviewed on Oct 16, 2026
Review provided by G2
What do you like best about the product?
As a Research Associate, I find Friendli AI’s support for multiple LLMs extremely useful. It gives me the flexibility to integrate different models into my research applications and compare their outputs more easily. I also appreciate how the endpoint configuration and the usage dashboard help keep the model-serving workflow clear and well organized.
What do you dislike about the product?
One limitation is that there’s no direct terminal access in the dedicated GPU environment, which can limit low-level debugging and deeper customization. Stronger debugging tools and better GPU-level visibility would make it easier to manage technical workflows and handle troubleshooting more conveniently.
What problems is the product solving and how is that benefiting you?
Integrating LLMs into research applications can be complicated, especially when you have to manage separate serving infrastructure. Friendli AI helps reduce that complexity by offering endpoint-based access, so I can spend less time on infrastructure setup and more time evaluating model outputs and building research applications.
Ryuto K.
Developer-Friendly LLM Deployment with Fast, Efficient Inference
Reviewed on Oct 16, 2026
Review provided by G2
What do you like best about the product?
What I like best about Friendli AI is how easy it makes deploying and serving large language models without having to manage a lot of the underlying infrastructure myself. The platform feels developer-friendly, and I especially appreciate the focus on performance and efficient inference. It makes it much easier to experiment with different models and move from development to production without spending too much time on infrastructure setup.
What do you dislike about the product?
One thing I dislike about Friendli AI is that the platform can feel a little overwhelming at first, especially if you are not already familiar with LLM deployment and inference concepts. Some parts of the documentation and setup process could be more beginner-friendly, with clearer examples and step-by-step guidance. Once you get used to it, the experience is much smoother, but there is still some room to improve the onboarding experience.
What problems is the product solving and how is that benefiting you?
Friendli AI solves a lot of the complexity around deploying and serving large language models efficiently. Instead of spending time setting up infrastructure, optimizing inference, and managing scaling, I can focus more on building and testing the actual application. This has helped me iterate faster, reduce the operational overhead of working with LLMs, and experiment with different models more easily.
Tawseef K.
Fast, Endpoint-Based LLM Testing That Keeps Research Workflows Simple
Reviewed on Oct 15, 2026
Review provided by G2
What do you like best about the product?
As a research scholar, I find Friendli AI’s approach genuinely useful, particularly for experimenting with and evaluating different large language models (LLMs) without having to spend excessive time managing infrastructure. The endpoint-based setup is especially helpful for work like organizing scientific information, analyzing technical text, and exploring AI-assisted research workflows. I also value its emphasis on inference performance, since fast response times matter a lot when I’m repeatedly testing prompts throughout the research process. In addition, being able to configure endpoints and monitor usage through a dashboard makes the overall workflow much easier to handle. It helps me stay focused on the research itself and on evaluating model outputs, instead of getting bogged down in deployment details.
What do you dislike about the product?
I think it would be very helpful to have a detailed, side-by-side comparison of performance, GPU configurations, and estimated costs across the different models. That kind of overview would make it much easier to choose the right model and configuration before starting a research experiment. I’d also appreciate more comprehensive troubleshooting documentation, along with clearer ways to identify and diagnose issues related to endpoint configuration. Overall, the platform’s usefulness really comes down to how well its deployment options, performance, and pricing match the specific needs of a particular research project.
What problems is the product solving and how is that benefiting you?
As a research scholar, one of my biggest challenges is experimenting with different AI models without spending too much time on deployment and the underlying technical infrastructure. Friendli AI helps with this by making LLM deployment and inference more accessible through an endpoint-based approach. This is especially useful for tasks like working with scientific literature, analyzing technical information, and testing prompts for research-focused use cases. It also simplifies usage monitoring, so I can spend more time evaluating model outputs instead of dealing with infrastructure details. Overall, it makes AI-assisted research workflows more manageable and helps me explore different models based on my project requirements.