

Find out what your peers are saying about Microsoft, Mendix, OutSystems and others in Low-Code Development Platforms.
If a task would require an hour, it can now be done in seconds.
This results in cost saving and time saving, as whenever you save some time, that is equal to cost saving.
It was time-saving because the APIs were faster than our in-house solution.
If I buy my own hardware for my organization and I don't need to spend hundreds of thousands of dollars to do that, I'm going to be able to get my ROI back pretty quickly.
We achieve tasks that we are supposed to do in a week within a day.
Time saving is implemented by using AI.
I have been impressed by the voice agent, the feedback the team requests, and the developments they have implemented so far.
Whenever you have questions, you get an instant answer.
I already understood how to use user input, the LLM model, and templates without needing guides.
We have a contract with the client in order to manage scalability, durability, and continuity of service.
Because Private AI is deployed by our company, the support is also very good.
The customer support is great.
Rather than using a cloud-hosted platform, using a self-hosted platform means there can be scalability issues.
Dify's scalability is good and it handles growth or increased workloads effectively, depending upon the resources available.
We did the performance testing for around 50 users, and Dify was complying properly.
If I brought in an LLM and it starts phoning home to an IP address that's not within my organization, I would certainly hope my IT team would be monitoring that.
There are very good frameworks available for scaling it up, such as DLRM.
If you have a good partner that can provide the hardware and move fast on this, it should be a good point to start.
It is not as stable as a cloud architecture.
Private AI is more stable than even the foundation models because I'm in charge.
For EU customers, adding more documentation about how Dify processes the data when starting to use Dify would be really beneficial for companies in Europe to get started with Dify.
We currently use OpenAI Agents SDK, which requires you to build everything by code, but the observability is really good.
The only improvement would be if Dify provided an SMTP server that could be connected to automate Dify workflow management, as that would be a great option.
The performance of models is affected by quantization type, MTU, token predictions, KV cache type, and cache size.
Deploying a large language model of 70 billion parameters is not an easy task on an on-premise architecture because you have to provide many GPUs.
On the edge, I'm very limited on how much GPU memory I have compared to OpenAI that's got racks and racks of million-dollar servers with terabytes of space.
My experience with pricing, setup cost, and licensing is that it was free to use.
Dify is free to use and has a free license from GitHub under a Dify open-source license based on Apache 2.0.
The experience with pricing is that it is quite reasonable.
Currently, I believe the cost is quite high.
We have software licenses for our software components.
Initially, we had about two weeks of time to implement the whole thing, but that was cut down to two days of time through using Dify.
Dify has positively impacted the organization because accuracy has been improved, and the time and complexity in flows that were manual are now automated.
Dify stands out to me because it is compliant with GDPR, and it is 100% compliant with GDPR rules.
What makes the open-source models stand out for me compared to frontier models is that I could not afford running jobs 24/7 with proprietary solutions, which fundamentally changes my workflow.
In terms of problem-solving velocity, we have sped up about 2,000 times before using this technology while also looking for specific types of activities to enhance operator growth.
Security features impact my day-to-day work and my confidence in using the tool because I work for the insurance industry, which is a regulated industry that needs to comply with many guidelines and rules, and one of the most important is security and privacy.
| Product | Mindshare (%) |
|---|---|
| Dify | 0.9% |
| Microsoft Power Apps | 7.0% |
| OutSystems | 4.8% |
| Other | 87.3% |
| Product | Mindshare (%) |
|---|---|
| Private AI | 2.4% |
| Accenture AI services | 3.0% |
| Slalom | 2.7% |
| Other | 91.9% |

| Company Size | Count |
|---|---|
| Small Business | 5 |
| Large Enterprise | 4 |
| Company Size | Count |
|---|---|
| Small Business | 5 |
| Midsize Enterprise | 2 |
| Large Enterprise | 7 |
Dify provides seamless integration and innovative features that optimize business processes for enhanced productivity. Its user-friendly design accommodates diverse use cases, making it a versatile choice for organizations seeking efficiency.
Dify revolutionizes workflow management with its intuitive platform. Designed with adaptability in mind, it serves businesses across industries by streamlining operations and facilitating collaboration. Dify's ecosystem allows for the integration of different tools, enhancing productivity and reducing the need for multiple systems. By offering a centralized hub for task management, it helps teams achieve their goals with relative ease and efficiency.
What are the key features of Dify?Dify's implementation strategy varies across industries. In retail, it connects inventory management systems to improve accuracy and reduce waste. In healthcare, it streamlines patient data processes for better caregiving. In manufacturing, it's used to optimize supply chain logistics, cutting down on lead times and boosting efficiency.
Private AI safeguards sensitive data by offering advanced privacy protection tailored to today's digital landscape. It integrates seamlessly with existing systems while ensuring compliance with privacy regulations.
Designed for organizations prioritizing data privacy, Private AI provides a dynamic approach to protecting user information. Using cutting-edge techniques, it identifies and redacts personal data in datasets, ensuring data remains confidential throughout processes. With a focus on adaptability, it fits diverse needs while maintaining efficiency. Its deployment enhances data protection strategies, providing comprehensive coverage without compromising workflow fluidity.
What key features does Private AI offer?Private AI's implementations span industries like healthcare and finance, where privacy is critical. In healthcare, it protects patient data without hindering service efficiency, while in finance, it secures client information, maintaining compliance and integrity across platforms.
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