Phylo brings frontier AI to more scientists with open models on Fireworks
Phylo has integrated open-weight frontier AI models via Fireworks to power its Biomni Lab, an agentic environment for biological research. This shift addresses high inference costs and usage limits associated with proprietary models, enabling longer and more complex scientific workloads. The transition has resulted in a 60% reduction in costs and a doubling of month-on-month user growth.
- ▪Biomni Lab supports long-horizon agentic runs that execute for hours or days across multiple machines and high-performance computing clusters.
- ▪Phylo adopted open-weight models on Fireworks to lower inference costs, which previously capped the amount of scientific work users could perform.
- ▪The company achieved day-zero access to new state-of-the-art open models, allowing them to be deployed in production within 24 hours.
- ▪Open models now handle the majority of traffic, with time to first token roughly halved compared to previous configurations.
- ▪Biomni Lab originated from the open-source Biomni project at Stanford and is currently used by over 50,000 scientists globally.
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| Original publisher | Fireworks AI |
| Canonical URL | https://fireworks.ai/blog/phylo-brings-frontier-ai-to-more-scientists-with-open-models-on-fireworks |
| Publication time | Sun, 20 Sep 2026 05:38:09 +0000 |
| Retrieval time | 2026-09-20T05:43:47.241Z |
| Last seen | 2026-09-20T05:43:47.241Z |
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| Summary source text | contentText |
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| Cluster | G8gqWtuItgyQ · 1 stories |
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| Publisher visit | Yes — open original |
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At-a-glanceIndustryLife sciences research and AI for scienceUse caseAgentic integrated biology environment supporting the scientific community in research planning, execution, and analysisWorkloadLong-horizon agentic inference. Hundreds of tool calls per run, executing for hours or daysModelsFrontier open-weight models, with proprietary models retained for a subset of tasksDeploymentFireworks fast path serverless endpoints with zero data retentionChallenge•Token-hungry workloads. Long-horizon agentic runs execute for hours or days, scaled across multiple machines.•Cost capped the science. Inference spend set the usage quota, which limited how much work a biologist could do before hitting a paywall.•Rapid adoption, rising load.
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