Definition
Metered AI credits
Metered AI credits are consumption-based pricing for AI features: on top of a per-seat fee, a team buys or is granted a pool of credits that each AI action draws down. It became the dominant 2026 pattern because model inference has a real marginal cost. The tradeoff is that credits, denominated in a vendor unit, make spend harder to predict than a flat fee.
Key takeaways
- Metered AI credits are consumption-based AI pricing on top of a seat fee, because model inference has a real marginal cost.
- It became the dominant 2026 pattern by aligning price with usage rather than charging everyone a flat AI fee.
- The tradeoff is honest: credits are hard to forecast, burn unevenly, and can throttle heavy users — transparency is the differentiator.
- Planoda meters usage through a visible per-workspace cost ledger and budget gate, not an opaque balance.
Pure seat-based pricing breaks when a single user can trigger arbitrarily expensive AI work, so vendors moved to a hybrid: a base subscription plus metered AI credits that meter the variable part. Each agent run, generation, or large context call consumes credits proportional to its cost, which aligns price with actual model usage rather than charging everyone the same regardless of how much AI they use.
The honest accounting of the tradeoff matters. Credits introduce real friction: the conversion from credits to dollars is often opaque, different actions burn wildly different amounts, heavy users hit caps and get throttled, and finance can't easily forecast the bill. None of this makes metered pricing wrong — inference genuinely costs money — but it does mean transparency is the differentiator. A credit system you can't see into is the worst version; a metered system with a visible cost ledger is the better one.
Planoda treats metering as a transparency problem: AI usage is metered, but through a per-workspace cost ledger and budget gate rather than an opaque balance — so a team gets consumption-aligned pricing without losing the ability to see, attribute, and bound what its AI actually costs.
Related terms
- AI cost ledgerAn AI cost ledger is a transparent, per-workspace record of metered AI spend: every model call's cost is attributed and accumulated so a team can see exactly what its AI usage costs in real money. It is the alternative to opaque 'credits,' which abstract spend into a proprietary unit whose dollar value and burn rate are hard to reason about.
- Agent governanceAgent governance is the set of controls that make an AI agent's actions safe, attributable, and reviewable: human approval gates on consequential actions, an immutable audit trail of who approved what, role-based capability limits, and spend controls. It is the difference between an agent that suggests and one you can trust to act.
- Model GatewayA model gateway is a unified layer that sits between an application and one or more AI model providers, routing requests through a single interface. It centralizes provider abstraction, authentication, rate limiting, cost tracking, caching, and failover — so an app can swap models, enforce spending budgets, and observe usage without rewiring every call site.
- Large Language Model (LLM)A large language model is an AI system trained on vast amounts of text to predict and generate language, enabling it to answer questions, summarize, write, and reason over natural language. LLMs power modern AI assistants and agents. They are probabilistic next-token predictors, which makes them remarkably capable but also prone to confident errors.
- AI AgentAn AI agent is a software system that uses a large language model to pursue a goal across multiple steps — reading context, choosing tools, and taking actions — rather than answering a single prompt. In a work platform, agents triage issues, draft updates, and execute multi-step tasks as autonomous teammates, bounded by the permissions and approvals their operators set.