Hebbia chose Fireworks over Lambda
Fireworks AI customer at Hebbia on serving state-of-the-art models with unified APIs
This reveals that managed inference and raw GPU cloud solve different jobs, even when they both sit on NVIDIA hardware. Hebbia needed a provider that could expose new open models through OpenAI style endpoints, hit concurrency targets, and show token and latency metrics without the team building serving, autoscaling, or observability themselves. In practice, that made Fireworks a direct alternative to Bedrock, while Lambda sat one layer lower in the stack.
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Hebbia used Fireworks for inference only, not for fine tuning or custom serving. The team wanted to drop a model like DeepSeek or Llama into an existing model router, route traffic immediately, and keep one API shape across OpenAI, Anthropic, Gemini, and open models.
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The real work avoided by skipping Lambda was operational. With raw GPU infrastructure, a team still has to decide how to host checkpoints, schedule GPUs, set autoscaling rules, monitor long tail latency, and manage token throughput under bursty chat and batch workloads. Fireworks packaged those primitives into a service level product.
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Lambda fits better when the workload itself demands low level control, especially training, fine tuning, or cluster orchestration. In adjacent research, Lambda customers describe using reserved GPU clusters for large synchronous training jobs, which is a very different buying decision from Hebbia's need for fast model deployment and stable inference APIs.
The boundary between these categories is likely to blur as GPU clouds add managed endpoints and inference platforms add more workflow control. But the split remains clear for now. Teams shipping product features fast will buy packaged inference, and teams shaping models or saturating clusters will keep moving closer to raw compute.