Sold by: Fireworks AI
Deployed on AWS
Fireworks.ai offers a generative AI platform as a service. We optimize for rapid product iteration building on top of gen AI as well as minimizing cost to serve.
4.1
Overview
Experience the fastest inference and fine-tuning platform with Fireworks AI. Utilize state-of-the-art open-source models, fine-tune them, or deploy your own at no additional cost. Access a diverse library of models across various modalities - including text, vision, embedding, audio, image, and multimodal - to build and scale your AI applications efficiently.
- Blazing fast inference for 100+ models
- Fine-tune and deploy in minutes
- Building blocks for compound AI systems
Start in seconds and pay-per-token with our serverless deployment. Or Use our dedicated deployments, fully optimized to your use case.
Highlights
- Instantly run popular and specialized models, including DeepSeek R1, Llama3, Mixtral, and Stable Diffusion, optimized for peak latency, throughput, and context length. Fireattention custom CUDA kernel, serves models four times faster than vLLM without compromising quality.
- Fine-tune with our LoRA-based service, twice as cost-efficient as other providers. Instantly deploy and switch between up to 100 fine-tuned models to experiment without extra costs. Serve models at blazing-fast speeds of up to 300 tokens per second on our serverless inference platform.
- Leverage the building blocks for compound AI systems. Handle tasks with multiple models, modalities, and external APIs and data instead of relying on a single model. Use FireFunction, a SOTA function calling model, to compose compound AI systems for RAG, search, and domain-expert copilots for automation, code, math, medicine, and more.
Details
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Delivery method
Deployed on AWS
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Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
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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/12 months |
|---|---|---|
Enterprise | Unlimited deployment models | $500,000.00 |
Dimension | Description | Cost/unit |
|---|---|---|
additionalusage | Additional Usage | $1.00 |
Vendor refund policy
All fees are non-refundable and non-cancellable except as required by law.
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.
Resources
Vendor resources
Support
Vendor support
Email support services are available from Monday to Friday.
support@fireworks.ai
AWS infrastructure support
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.
Updated weekly
By Fireworks AI
By NI SP - High-End Remote Desktop and HPC
By Karini AI
Top
10
In Finance & Accounting, Research
Top
10
In Summarization-Text, Generation-Text
Top
10
In Procurement & Supply Chain
AI generated from product descriptions
High-Performance Inference Optimization
Fireattention custom CUDA kernel serves models four times faster than vLLM, achieving inference speeds up to 300 tokens per second on serverless infrastructure.
Cost-Efficient Fine-Tuning
LoRA-based fine-tuning service that is twice as cost-efficient as other providers, with ability to deploy and switch between up to 100 fine-tuned models without additional costs.
Multi-Modal Model Library
Access to diverse library of 100+ models across multiple modalities including text, vision, embedding, audio, image, and multimodal capabilities.
Compound AI System Architecture
FireFunction SOTA function calling model enables composition of compound AI systems supporting multiple models, modalities, and external APIs for RAG, search, and domain-specific applications.
Flexible Deployment Options
Serverless pay-per-token deployment model or dedicated deployments fully optimized to specific use cases, with support for popular models including DeepSeek R1, Llama3, Mixtral, and Stable Diffusion.
Model Quantization Support
Support for 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization enabling inference on GPUs with 16GB or 24GB memory
Inference Engine
Llama.cpp inference with plain C/C++ implementation without dependencies, supporting interactive and server mode operations
GPU and CPU Hybrid Processing
Capability to run inference simultaneously on GPU and CPU, allowing execution of larger models when GPU memory is insufficient
Multi-framework Support
Integration with llama-cpp-python for OpenAI API compatibility, Open Interpreter for code execution, and Tabby coding assistant for IDE integration
No-Code Application Development
Visual interface with built-in connectors and large language models enabling generative AI application deployment without coding requirements.
Multi-Model Support and Comparison
Access to latest large language models with prompt playground functionality for model comparison and evaluation across different LLM options.
Enterprise Security and Governance
Secure credentials management, personally identifiable information masking, data encryption, and role-based access controls for enterprise-level compliance.
Observability and Cost Management
Operational dashboards providing visibility into model spending, performance metrics, usage patterns, and trends for cost tracking and optimization.
Trust and Safety Controls
Content filtering mechanisms to reduce noise, block harmful content, and include relevant citations with ground truth comparison capabilities using LLM as a judge.
Standard contract
Customer reviews
Hussain Gagan
Gaining faster, flexible AI workflows has made our team ship reliable features with confidence
Reviewed on Apr 20, 2026
Review from a verified AWS customer
What is our primary use case?
Our main use case for Fireworks AI is running LLM-based APIs for things like summarization and internal search. We didn't want to rely fully on a closed model, so Fireworks AI helped us run an open-source model with decent performance. It fits well for production APIs where latency matters.
We also experimented with embeddings and some lightweight fine-tuning in Fireworks AI. Not everything made it to production, but it was useful for testing different models quickly. It's good for teams that want flexibility rather than a fixed model.
What is most valuable?
The best features Fireworks AI offers are speed and control over models. You can pick different open-source models and switch fairly easily. Additionally, the API layer feels developer-friendly.
The API layer in Fireworks AI is developer-friendly because its consistency is a major factor. It follows standard OpenAI-compatible endpoints, which meant we could swap out models or integrate new ones without rewriting our entire service layer. For example, when we wanted to test a new Llama 3 variant against our existing deployment, it was literally just a one-line change in our configuration.
The fine-tuning and customization options in Fireworks AI are useful, even though we didn't go very deep into them. The ability to experiment with multiple models in one setup is underrated. It saves time when comparing outputs. Fireworks AI has positively impacted our organization by making our AI features feel more production-ready instead of experimental. Teams became more confident shipping AI-based features, which also reduced dependency on a single vendor.
Since we started using Fireworks AI, we've seen around a 20 to 30% improvement in latency for some endpoints. Cost-wise, we've achieved approximately 15 to 25% savings depending on the model we use. Nothing extraordinary, but definitely meaningful.
What needs improvement?
Fireworks AI could be improved, as documentation could be clearer in some areas, especially around advanced configs. Additionally, debugging model behavior isn't always straightforward. Sometimes we have to guess what's going wrong.
Needed improvements for Fireworks AI would be better examples in documentation, especially for real-world use cases. Debugging tools could be more visual instead of just logs. Some edge cases take longer to troubleshoot than expected.
Another improvement for Fireworks AI is that documentation could be clearer, especially around advanced configs. Better examples in documentation would help.
For how long have I used the solution?
I've been using Fireworks AI for around six to eight months now, mainly in back-end services for AI-powered features. Overall, it's been pretty solid, especially for inference-heavy workloads. The setup was quicker than I expected.
What do I think about the stability of the solution?
Fireworks AI is pretty stable overall in my opinion. We didn't face any major outages, just occasional slowdowns. Nothing critical occurred.
What do I think about the scalability of the solution?
In terms of scalability, Fireworks AI scales very well from what we have observed. We tested it with moderate traffic and it handled very well. It's clearly built for production workloads.
How are customer service and support?
I didn't interact heavily with Fireworks AI's customer support, but when we did, responses were decent. Responses were not super fast, but helpful enough.
Which solution did I use previously and why did I switch?
We were mostly using hosted APIs from bigger providers before using Fireworks AI. We switched mainly for cost control and flexibility with models. I also wanted better performance for certain use cases.
How was the initial setup?
Setup was fairly quick, maybe a day or two to get something running. Fine-tuning took longer to understand.
What was our ROI?
The return on investment with Fireworks AI has been decent. We've experienced faster iteration and slightly lower costs, as well as reduced engineering time spent managing infrastructure ourselves. The savings are not huge, but definitely worth it.
Which other solutions did I evaluate?
Before choosing Fireworks AI, we looked at things such as Together AI and some direct cloud GPU setups. We also briefly considered sticking with OpenAI APIs. Fireworks AI felt like a good middle ground.
What other advice do I have?
My advice regarding using Fireworks AI would be to go in with a clear use case instead of just experimenting randomly. Additionally, spend time understanding model selection, as that makes a big difference. Don't expect everything to work perfectly out of the box.
Fireworks AI is a good option if you want more control over your AI stack without managing everything yourself. Fireworks AI is not perfect, but definitely practical for real-world use. I found Fireworks AI to be a valuable tool in streamlining our workflows. I would definitely recommend exploring its capabilities for businesses looking to enhance their operations. I rated this review an eight overall.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amar-Kumar
Chatbot exploration has enabled personalized product and offer recommendations for users
Reviewed on Apr 07, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for Fireworks AI is to build a chatbot and recommendation engine to recommend products to users of my application. Since I work in a QSR-based domain, I want to give recommendations such as showing potato fries as an option if a burger is added to the cart, which is the type of automation I want to achieve with Fireworks AI .
I envision the chatbot working for my users by handling common queries and focusing on product suggestions. As a core technical person, I explore everything about AI products, and I am currently using Fireworks AI to understand what we can achieve with our chatbot for queries such as 'Where is my order?' or 'Give me the list of products under happy hour offers.'
I am focusing on the chatbot and recommendation engine, which are the major use cases I am exploring, including other AI options, not only Fireworks AI.
What is most valuable?
Based on my exploration so far, I find that Fireworks AI offers a platform where I can run and build my own AI models, which I consider to be the best feature. Fireworks AI has positively impacted my organization by fulfilling my use cases to some extent, and I definitely want to explore more as it is close to addressing my needs.
What needs improvement?
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.
For how long have I used the solution?
I have been using Fireworks AI for a few days in the exploration phase only, and I have not implemented it yet.
What do I think about the stability of the solution?
Fireworks AI is stable from what I have seen so far, and based on my exploration, it is stable.
What do I think about the scalability of the solution?
Regarding scalability, Fireworks AI is showing itself as a stable product based on my exploration.
How are customer service and support?
I have not had the chance to contact or connect with Fireworks AI customer support.
What other advice do I have?
My advice for others looking into using Fireworks AI is that if you have a use case where you need to build or run your pre-existing model or a model provided by Fireworks AI, then you should go with it. You can build your own chatbot and provide a personalized experience. For example, in the entertainment industry, similar to a Jio application, I can recommend videos as per user preferences, such as suggesting cartoon videos for children based on their age while ensuring the content is informative for both parents and children.
I rate Fireworks AI an eight out of ten based on my exploration. I chose eight out of ten because I explored it for the chatbot and recommendation engine, which align with my use case, and this rating may change in the future.
Liraz A.
One Stop AI Model Shop
Reviewed on Nov 14, 2024
Review provided by G2
What do you like best about the product?
So many AI models to choos from... Love the option of the playground
What do you dislike about the product?
pretty hard to get started. they really need a quickstart guide.
and beacuse the site is so full of featurs - a tour would be nice.
and beacuse the site is so full of featurs - a tour would be nice.
What problems is the product solving and how is that benefiting you?
helping me choose the right model for my day to day use.
reviewer2588646
Enhanced text-to-image creation with solid API and fine-tuning support
Reviewed on Nov 06, 2024
Review provided by PeerSpot
What is our primary use case?
We primarily use Fireworks AI for text-to-image generation. We are developing a platform for artists to sell their art styles, where the system helps them tune a model and then sell images generated from their signature.
How has it helped my organization?
Fireworks AI has helped our organization by enabling us to create a platform for artists to sell their art styles. I am not the user of the solution. I'm the developer. It helps me do my job effectively.
What is most valuable?
Fireworks AI has a solid API and is quite easy to interact with. It has better documentation and logs, which are important for me as a developer. Additionally, it has a bigger infrastructure and provides nice support for fine-tuning the Flux AI model.
What needs improvement?
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.
For how long have I used the solution?
I have been using Fireworks AI for about four months.
What do I think about the stability of the solution?
Fireworks AI is pretty stable, and I have not encountered any problems.
What do I think about the scalability of the solution?
Fireworks AI offers a very complete API, and its scalability is impressive.
Which solution did I use previously and why did I switch?
I previously used Okta. It was discontinued, so we opted for Fireworks AI.
How was the initial setup?
The initial setup was fairly easy. It took about eight to ten days, including integrating it into our solution, testing, and moving from scratch to production.
What's my experience with pricing, setup cost, and licensing?
I cannot comment on pricing or setup cost since others handle that aspect. As a developer, I primarily use the API.
Which other solutions did I evaluate?
I have evaluated SAL as an alternative solution.
What other advice do I have?
I'd rate the solution ten out of ten.
Pratiksh S.
Review for Fireworks AI
Reviewed on Sep 05, 2024
Review provided by G2
What do you like best about the product?
They have categorised the models according to users requirements and user have to pay for the products they use. No extra costing.
What do you dislike about the product?
They need to use more dependable parameters. And should increase their serverless model limits.
What problems is the product solving and how is that benefiting you?
AI is the booming condition in the industry and with the Fireworks it feels easy to deploy models to organisational servers. Additionally they use Meta Llama.