The specialized intelligence platform

Specialized intelligence gives organizations ownership of their AI economics and intelligence, beyond the generalized models their competitors can rent. Fireworks is the first specialized intelligence platform, bringing together everything teams need to run, route, and train open models—with support from frontier researchers. Start by serving an open model. Add routing to lower cost per task. Then train on your unique data and expertise to surpass closed frontier models on the tasks that matter to your business, all within the same platform and account. More than 10,000 companies run on Fireworks today.

Model selection, routing, and specialization work better as one system. Give employees and applications access to the latest state-of-the-art models, then route requests to meet each workload’s quality, latency, and cost requirements. For core workloads that encode your intellectual property, evaluate models against your own benchmarks and train specialized models to outperform general-purpose alternatives on the tasks that matter to your product.

A shared platform connects those decisions to execution. Routing can account for inference state, including cache availability, to improve cache reuse and reduce redundant computation. Training and serving stay aligned on numeric formats, kernels, and MoE expert selection, reducing discrepancies that can distort RL learning signals or change production outputs. Fireworks uses Router Replay to preserve MoE expert choices and measures train-inference KL divergence to verify alignment.

The result is a continuous path from evaluating frontier models to routing production traffic and deploying specialized checkpoints, all on the same platform.

For a growing share of production work, open models offer a good replacement for closed models. With post-training, open models can even beat closed models on your specific task in accuracy, performance, and/or cost per task. As an example, Gumloop moved its company-wide agent from Opus to an open model on Fireworks. Its employees didn’t notice the difference, and costs fell up to 72%. Open models aren’t better at everything, which is why Fireworks Nexus offers intelligent routing to keep your closed model for the hardest tasks.

Fireworks offers flexible pricing to match how your team builds and scales. Start quickly with serverless inference, with Fast and Priority options for workloads that need more speed or reliability. Use batch processing to lower costs on work that can run asynchronously. As usage grows, Reserved Throughput gives you predictable capacity and spend.

For training, choose a path and pricing model that fit your team’s expertise and goals, from getting started with guidance to running your own training workflows. Nexus offers flexible plans that help teams reduce model costs across everyday collaboration and coding.

For more information, visit the Fireworks pricing page.

Your data stays yours. Fireworks does not use your data to train its own models. Retention depends on the features you use, with controls to manage stored data and enforce zero data retention for supported workloads.

Across inference, training, and Nexus, we help teams protect sensitive work and meet enterprise security and compliance requirements. For organizations with stricter policies, account-level controls help admins govern how data is handled.

For more information, visit the Fireworks Trust Center.

Yes. Fireworks offers regional controls for enterprise accounts to help meet data residency requirements and internal policies. Availability varies by region and workload. Our team can help you find the right configuration for your needs. Contact us to discuss regional availability and supported features.

Yes. You retain ownership of your fine-tuned weights and can take them to another platform, subject to the underlying model’s license. You can also bring compatible models and adapters trained elsewhere to Fireworks. You control where your models run and when they’re updated.

Deploy directly from your training checkpoint, no weight export or platform migration required. Promote a checkpoint to a model in your account, then choose a supported deployment option for your workload.

Fireworks optimizes how trained models serve. A single LoRA can merge into the base weights for inference at roughly base-model speed, while multiple adapters can share GPU capacity to make per-customer fine-tunes more efficient. For RL workflows, updated weights can load into a running deployment, with compressed transfers reducing synchronization overhead. The result is a shorter path from training to production. Customers have gone from proof of concept to production in a matter of weeks.

Fireworks Lab embeds ML researchers and forward-deployed engineers with your team to build a specialized model. Each engagement opens with a diagnosis that sets the capability, baseline, success criteria and scope, then runs a time-boxed build planned back from your target date. You bring domain expertise, data and a view of what good looks like. We design the evals together. You keep everything we build: the model, the eval harness, the data pipelines, the training loop and the recipes.

Yes. Fireworks Sovereign brings our serving stack to enterprise infrastructure in your own cloud account or data center. You retain control of your infrastructure and network policies, while Fireworks manages deployment, scaling, performance tuning, and upgrades. The familiar Fireworks API helps teams use the same integrations across environments. Contact our team to discuss availability, infrastructure requirements, and supported workloads.

Fireworks brings world-class lab researchers and engineers together to help organizations make their own frontier. Serving, training, routing, and forward-deployed research services typically come from separate vendors, with varying depth and quality. We’ve co-designed and co-optimized them on one platform, starting with an inference engine built by the creators of PyTorch and optimized from kernels to cache. That difference shows up in what globally-leading organizations achieve with Fireworks: higher-quality model performance, better AI economics, faster time to market, and a durable competitive advantage.