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Commercial Orchestrator operation turns the node into paid infrastructure for Gateways and applications. The business case shifts from inflation-first participation to fee-driven service delivery. Use this page to decide whether that step fits your operation. It explains how fee-driven operation differs from inflation-first participation, what Gateways care about, and what changes when the goal is recurring service revenue instead of opportunistic earnings. For the mechanics of how both revenue streams work, see . For pricing configuration, see .

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Hobbyist vs Commercial

The Livepeer Orchestrator ecosystem supports both models, but they reward different behaviour. One is optimised for lower-risk participation and base rewards. The other is optimised for being chosen, trusted, and paid for by Gateways running user-facing products. Neither model is inherently better. They serve different operator goals. Many of the strongest nodes eventually run a hybrid, using inflation as the base layer and service fees as the part that scales.

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Why Service Fees Scale

Commercial operators care most about ETH service fees because traffic, pricing, and uptime drive them directly. That is the core shift in the business case: the upside comes from being part of an application’s serving path instead of relying on larger bonded LPT alone. A large-stake Orchestrator earns a fixed percentage of the round’s inflation regardless of how many jobs it processes. An Orchestrator serving high-volume AI inference workloads earns ETH proportional to every pixel processed and every model inference returned. For an Orchestrator actively serving a high-volume Gateway - a streaming platform, an AI product, or a live video application - monthly ETH fee income from job processing can exceed LPT inflation income by a substantial margin.
Commercial fee income is variable and depends on Gateway demand, job mix, and market pricing conditions. Inflation rewards are predictable by stake. Most commercial operators treat inflation as the base layer and fees as the upside.

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What Commercial Operation Requires

This is where many otherwise capable operators self-select out. Commercial service is not about running the same stack a little harder. It means meeting operational expectations that Gateways can depend on in front of their own users.

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Uptime and reliability

A Gateway operator building a product on Livepeer’s network needs the Orchestrators it routes to to be consistently available. If an Orchestrator fails mid-session, the Gateway must failover - introducing latency and degraded user experience. Repeated failures result in the Orchestrator being deprioritised in the Gateway’s selection algorithm. Commercial Orchestrators target 99%+ uptime. This requires:
  • Automated monitoring with immediate alerts on node failure
  • Automated restart and recovery
  • Stable, redundant connectivity (not shared home broadband)
  • Consistent power supply (UPS or colocation)
  • Hardware health monitoring (GPU temperatures, VRAM utilisation)

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Model warm-up management

For AI inference workloads, cold model starts (loading a model from disk into VRAM on first request) introduce latency that breaks user-facing SLAs. Commercial AI Orchestrators pre-load all advertised models at startup and keep them warm. The practical implication: the VRAM requirements for commercial AI operation are determined by the sum of all models that must be simultaneously loaded, not only the largest single model.

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Latency targets

Gateways rank Orchestrators by response latency, uptime history, and job success rate. Consistently slow responses
  • even within acceptable job completion time - affect long-term selection probability.
Commercial Orchestrators optimise for:
  • Network proximity to high-volume Gateways
  • Low GPU scheduling latency (dedicated GPU, not shared)
  • Fast storage for model weights (NVMe preferred over SATA)

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Working with Gateways

Anonymous discovery is enough to get started. It is rarely enough to build durable commercial traffic. Commercial operators still need to be competitively discoverable, but they also work deliberately to become a reliable option for specific Gateway needs.

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Per-Gateway pricing

The -pricePerGateway flag allows Orchestrators to set different prices for specific Gateway addresses. This is the primary tool for commercial Gateway relationships:
per-gateway pricing
# Set a negotiated rate for a specific high-volume Gateway
-pricePerGateway='{"0xGatewayEthAddress": 800, "0xOtherGateway": 950}'
A high-volume Gateway that commits consistent traffic in exchange for a preferred rate is a commercially valuable relationship. Per-Gateway pricing formalises that arrangement at the protocol level without requiring any off-chain contract.

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Capability signalling

Gateways discover AI capabilities through the capability manifest returned during session negotiation. Commercial Orchestrators ensure their declared capabilities are accurate and stable - advertising a model that is slow to load or frequently unavailable damages the Gateway’s product and the Orchestrator’s selection score. Practical discipline for commercial capability management:
  • Declare only models that are loaded and warm at startup
  • Remove capability declarations for models that are not being actively served
  • Use -aiModels to specify exactly which pipeline/model combinations to load on startup
  • Monitor model load times and remove slow-start models from the active set

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Building Gateway relationships

Active commercial relationships with Gateways typically develop through:
  • Consistent performance history visible on the Livepeer Explorer
  • Participation in the #orchestrators channel on the Livepeer Discord
  • Direct outreach to Gateway SPEs and ecosystem partners
  • Demonstrated capability support for pipelines that specific Gateways need
Gateways serving AI products actively look for Orchestrators with specific capability profiles. An Orchestrator running a pipeline that a Gateway cannot currently source from the network can justify stronger pricing in those conversations.

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How to Position for Commercial Workloads

The shift from passive inflation earner to active commercial operator usually means narrowing focus, not broadening it. The goal is to become reliably good at the workloads and service levels that a Gateway actually wants to buy. Use tools.livepeer.cloud/ai/network-capabilities to check current routed pipelines and prices before narrowing your capability set.
Commercial operators do not win by listing everything. They win by being reliably good at work that Gateways are already trying to source. Check current network demand at tools.livepeer.cloud/ai/network-capabilities to see which pipelines are being routed and at what prices.Prioritise:
  • Pipelines with few available Orchestrators and active demand
  • High-VRAM models that exclude commodity GPU competition
  • Cascade AI pipelines if hardware supports it - higher per-job value
Commercial pricing is part market positioning and part relationship management. It requires:
  • Understanding the Gateway’s maxPricePerUnit ceiling for each pipeline
  • Setting prices that are competitive but not floor-level (under-pricing signals low quality to some Gateway operators)
  • Using -pricePerGateway to offer volume discounts to specific Gateways
  • Using -autoAdjustPrice carefully - automatic adjustment can undercut commercial relationships
See for configuration guidance.
Commercial operations typically require infrastructure changes that hobbyist setups do not:
  • Colocation or cloud GPU instead of home hardware, for reliability and connectivity
  • Dedicated GPUs with no competing workloads (mining rigs sharing GPUs with Livepeer introduce unpredictable latency)
  • Redundant connectivity with failover (not a single home ISP connection)
  • UPS or colocation power for uptime targets above 99%
Hardware investment for commercial operation should be planned against projected service fee income, not against inflation rewards alone. The break-even analysis is different.
Commercial uptime targets require monitoring that catches problems before a Gateway notices them. go-livepeer exposes a Prometheus metrics endpoint (port 7935 by default). Connect this to an alerting stack (Grafana, PagerDuty, or equivalent) to detect:
  • Node offline or unreachable
  • GPU memory pressure (model eviction)
  • Reward call failures
  • Unusual session failure rates
Manual monitoring (periodic log checks) is insufficient for commercial SLA targets. See for setup guidance.

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The Commercial Operator Landscape

Commercial operation does not look the same across the network. The common thread is fee revenue, but the operating model changes depending on who owns the GPUs, who manages stake, and how traffic is sourced. Pool operators manage the Orchestrator registration, on-chain staking, and reward calling for a fleet of GPU workers. Workers register under the pool’s Orchestrator address; the pool earns a margin on their job income. Pool operators function as GPU infrastructure businesses, combining the service fee model with a managed-Orchestrator offering. Enterprise GPU operators run dedicated fleets serving specific AI application workloads. These operators serve Gateways that power user-facing AI products and require SLA-level commitments. Their hardware is typically data-centre grade with redundant connectivity. Dual-workload operators run both video transcoding and AI inference from the same infrastructure, earning fees from both streams. This is the natural next step for video Orchestrators who invest in high-VRAM GPUs.
The Livepeer Forum and the #orchestrators Discord channel are the best current sources for tracking active commercial operators and the workloads they are serving.

Operating Rationale

Financial evaluation - costs, revenue streams, and the decision matrix for choosing your path.

Pricing Strategy

How to configure competitive prices for video and AI workloads, including per-Gateway rates.

Working with Gateways

The technical and operational details of the Gateway-Orchestrator relationship.

Operator Impact

Why operating an Orchestrator matters beyond earnings - governance weight, network stewardship, and protocol influence.
Last modified on March 16, 2026