One of the things that could improve Fireworks AI is the cost, which I think is really expensive. It is very much more expensive than Groq, which I generally use. Also, there is no free tier, which is another issue. I only got around five to six credits when I signed up. A free tier would be advisable. Additionally, I think the number of models that were available for image generation and video was very less, which can be improved. I would add that the image-video generation of Fireworks AI is pretty weak. As I have already mentioned, it supports fewer image models. I do not remember exactly, but it is very less compared to others. I think it has zero video generation capabilities, making it really hard for someone wanting to make a visual AI project. In my organization, I had to do one where I had to use image generation and its processing, and I could not use any model here. Additionally, it does not support the full ML cycle, such as data preparation and feature engineering. I cannot do it here and would need a separate tool or app for that.
Fireworks AI is an extremely strong tool in inference performance. However, initially, Fireworks AI's platform and tooling require some learning, especially for teams transitioning from traditional cloud infrastructure or self-hosted model serving. While Fireworks AI simplifies deployment significantly, understanding the settings and model configuration still requires some familiarity and a learning period. Another challenge I would address is broader integrations and workflow tooling around advanced fine-tuning pipelines, which would be a great addition to Fireworks AI. Fireworks AI's core platform is excellent, but some surrounding ecosystems are still evolving compared to more mature cloud platforms. While Fireworks AI supports open-source models very well, some custom-wise deployment might still require additional engineering work, which could have been better. Another pain point would be the pricing at scale. While Fireworks AI is excellent at the price point it offers, inference-heavy workloads with large-volume requests can become expensive over time, especially for teams starting out or for startups operating with a limited budget.
Ai스페셜리스트매니저 at a tech vendor with 501-1,000 employees
Real User
Top 20
May 2, 2026
In the current function calling, if Fireworks AI could be added as part of our RAG system not only with the function calling we are using now but also with a variety of other connections, then an even better situation would be possible. Fireworks is based on tool calling, so it needs to add more different kinds of connections to enable faster data retention and optimization. Although multiple optimal optimization or measurement methodologies for using LLMs are being discussed, when using them inside enterprises, the main thing is actually measuring work handling capability or work processing speed. Based on that, and also through what might be called interviews with business-side staff, we measured the speed improvements in a somewhat indirect manner.
When exploring the flexibility or ease of use of Fireworks AI, I find that it is too early to say, but I can say that it is easy to understand and integrates easily by following the given steps. Based on my exploration so far, I find that it is too early to judge any improvements or negative aspects of Fireworks AI, as I am still in the exploration phase.
Senior Software Development Engineer at a tech services company with 1-10 employees
Real User
Top 10
Nov 6, 2024
Returning the values charged for each event generation would improve Fireworks AI. When using the API, it does not return information about the charges for image generation, which would be useful for our solution.
Fireworks AI uses advanced technologies to streamline operations and enhance user experience, catering to industry-specific requirements and driving innovation.
Fireworks AI integrates cutting-edge tools for data processing, offering seamless automation in managing complex workflows. It addresses industry needs through scalable solutions adaptable to personalized requirements. Fireworks AI ensures optimized performance, enhancing decision-making efficiency across businesses.
What...
One of the things that could improve Fireworks AI is the cost, which I think is really expensive. It is very much more expensive than Groq, which I generally use. Also, there is no free tier, which is another issue. I only got around five to six credits when I signed up. A free tier would be advisable. Additionally, I think the number of models that were available for image generation and video was very less, which can be improved. I would add that the image-video generation of Fireworks AI is pretty weak. As I have already mentioned, it supports fewer image models. I do not remember exactly, but it is very less compared to others. I think it has zero video generation capabilities, making it really hard for someone wanting to make a visual AI project. In my organization, I had to do one where I had to use image generation and its processing, and I could not use any model here. Additionally, it does not support the full ML cycle, such as data preparation and feature engineering. I cannot do it here and would need a separate tool or app for that.
Fireworks AI is an extremely strong tool in inference performance. However, initially, Fireworks AI's platform and tooling require some learning, especially for teams transitioning from traditional cloud infrastructure or self-hosted model serving. While Fireworks AI simplifies deployment significantly, understanding the settings and model configuration still requires some familiarity and a learning period. Another challenge I would address is broader integrations and workflow tooling around advanced fine-tuning pipelines, which would be a great addition to Fireworks AI. Fireworks AI's core platform is excellent, but some surrounding ecosystems are still evolving compared to more mature cloud platforms. While Fireworks AI supports open-source models very well, some custom-wise deployment might still require additional engineering work, which could have been better. Another pain point would be the pricing at scale. While Fireworks AI is excellent at the price point it offers, inference-heavy workloads with large-volume requests can become expensive over time, especially for teams starting out or for startups operating with a limited budget.
In the current function calling, if Fireworks AI could be added as part of our RAG system not only with the function calling we are using now but also with a variety of other connections, then an even better situation would be possible. Fireworks is based on tool calling, so it needs to add more different kinds of connections to enable faster data retention and optimization. Although multiple optimal optimization or measurement methodologies for using LLMs are being discussed, when using them inside enterprises, the main thing is actually measuring work handling capability or work processing speed. Based on that, and also through what might be called interviews with business-side staff, we measured the speed improvements in a somewhat indirect manner.
When exploring the flexibility or ease of use of Fireworks AI, I find that it is too early to say, but I can say that it is easy to understand and integrates easily by following the given steps. Based on my exploration so far, I find that it is too early to judge any improvements or negative aspects of Fireworks AI, as I am still in the exploration phase.
Returning the values charged for each event generation would improve Fireworks AI. When using the API, it does not return information about the charges for image generation, which would be useful for our solution.