Fireworks

Paid

Fireworks is an AI infrastructure platform for developer teams. It handles training and inference for open-source and custom machine learning models.

You get APIs to deploy, fine-tune, and serve large language models. Three deployment modes cover different needs: serverless pay-per-token, on-demand dedicated instances, or reserved capacity with guaranteed quotas.

The team behind it built PyTorch at Meta. They focus on speed and cost for production workloads, not on tools for non-technical users.

Open it if you have engineers who can build on raw infrastructure. Skip it if you need ready-made business integrations or hands-on support.

It fits when you have the team to build on top of it.

• AI engineers building custom applications: You write API code and stitch pieces together. There are no pre-built workflows or business tool connectors.
• Teams with dedicated engineering resources: You need high-performance inference and can handle the setup yourself. Support is slow when things break.
• Organizations that fine-tune models: You need custom capabilities without full retraining. LoRA support works well for this.
• Developers prototyping with open-source models: You want Llama, Gemma, or Mistral fast. Over 100 models are available.
• Teams needing containerized deployments: You run inside your own VPC. This keeps data in your control.
• Users prioritizing inference speed and cost: You want 4x throughput and 50% lower latency versus standard open-source setups. Token costs start around $0.20 per million for Llama 3.
• Organizations migrating from OpenAI or Anthropic: The API format matches what you already use. Switching takes less code change.
• Teams running high-throughput production workloads: You need latency under 200ms for LLMs. Cursor and Notion use it for this.

These are what you notice once you are running.

• Fast text inference: The FireAttention engine pushes past 150 tokens per second. You get structured outputs and function calling without waiting.
• Aggressive token pricing: Serverless Llama 3 runs about $0.20 per million tokens. Batch jobs get a ~50% discount on top.
• Fine-tuned models at base rates: You pay the same for custom fine-tunes as off-the-shelf models. No surcharge for the work you put in.
• Full training control: You choose guided runs, managed configuration, or direct control over training logic. This scales from simple fine-tuning to custom reinforcement learning loops.
• Real compliance stack: SOC 2 Type II, HIPAA, and GDPR coverage. This matters for regulated industries that still want speed.

Skip it when these limits block your path.

• You need images or video: Only five image models exist. No video generation at all. Visual AI feels bolted on, not built in.
• You count on stable model availability: Models and endpoints disappear without warning. Production workloads break when this happens.
• You need responsive support: Trustpilot shows slow responses and deflection. Rate limits hit without review of your actual usage.
• You want predictable costs: Pay-as-you-go pricing jumps around. Reserved capacity requires sales contact. Region-restricted deployments cost 1.5x standard rates.
• You need surrounding infrastructure: No bundled vector database, storage, or agent tooling. You manage all of that separately.