Run and fine-tune models. Deploy custom models. All with one line of code.
Open Source Agent Evals & Observability
Stage
Not given
Established
Founded
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Based in
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DE
Pricing model
usage-based
freemium
Pricing
Most models billed by time based on hardware used (CPU $0.000100/sec, T4 GPU $0.000225/sec, L40S GPU $0.000975/sec). Some models billed by input/output tokens or generated items. Private custom models billed for all uptime; fast-booting fine-tunes billed only for active processing.
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Key features
Run thousands of community-contributed open-source models
Fine-tune models with custom data to create specialized versions
Deploy custom models using Cog, an open-source packaging tool
Automatic scaling based on demand
Pay only for compute time used
Image generation from text
Hierarchical traces capture every LLM call, tool invocation, and retrieval step
LLM-as-a-judge, heuristic functions, or human review evaluations
Prompt management with one-click deployments and rollbacks
Playground to test prompts on real production inputs and compare models
Experiments with test cases and side-by-side comparison
Human annotation and collaborative human-in-the-loop workflows
Cost and latency monitoring with dashboards and alerts
REST APIs and Query SDK for data access
Langfuse Assistant to automate the AI engineering loop
What makes it different
One-line code interface for running models without ML expertise
Automatic infrastructure scaling without manual management
Open-source community with thousands of production-ready models
Fine-tuning capability to customize models for specific tasks
MIT licensed open source platform with no data lock-in
Enterprise scale architecture handling billions of monthly events
Works with any language and framework with no framework lock-in
Async by default so tracing never blocks your application