Opactiv brings the same cost attribution, budgeting, and governance discipline that organizations apply to cloud spend to the rapidly growing footprint of agentic AI workloads. As enterprises deploy autonomous agents that call large language models through a mix of gateways and proxies, Opactiv provides a single pane of glass for tracking, attributing, and controlling LLM consumption – independent of which gateway sits in front of the traffic.
The Multi-Gateway Reality
Modern agentic AI architectures do not always standardize on a single gateway. Different teams choose different tools for different reasons:
- Kong AI Gateway / Konnect: selected perhaps by platform teams already running Kong for API management
- Portkey: chosen potentially for its routing, fallback, and observability features
- Maxim AI: may be adopted by teams that want integrated experimentation, evaluation, and tracing
Opactiv treats each of these as a first-class data source. An organization can connect any combination of these gateway platforms and see consolidated cost, usage, and attribution across all of them.
How It Works
For each gateway provider, Opactiv connects to the gateway’s analytics/management plane – not the data path itself. The gateway continues to handle agent traffic without Opactiv inline. Opactiv’s billing scheduler periodically polls each provider’s analytics API on a configurable cadence (hourly or daily), ingesting per-request token usage records pre-aggregated by the gateway’s consumer/workspace model.
Each ingested record carries:
- Prompt tokens, completion tokens, and total cost
- Model name and provider (Anthropic, OpenAI, etc)
- Consumer/workspace identifier mapped to an Opactiv organization, pool, and agent
Timestamp and request identifier
Opactiv applies its existing tag-based attribution model to these records, so LLM spend lands in the same pool budgets, constraints, and reports used for cloud infrastructure costs.
Unified Reporting and Governance
Once ingested, agentic AI spend behaves like any other cost in Opactiv:
- Pool attribution: costs roll up to the team, project, or agent pool that owns the consumer
- Budgets and constraints: soft and hard limits on agent spend, with the same alerting model as cloud budgets
- Cross-provider analysis: compare cost per agent, per model, or per provider in a single report
- Forecasting: trend agentic AI spend alongside infrastructure spend
- Tagging policies: enforce that every agent’s traffic carries the org/pool/agent metadata required for accurate attribution
Result
Whether an organization runs five agents on one gateway or fifty agents across three gateways and a direct LLM API connection, Opactiv presents a single, consistent answer to the questions that matter: who is spending, on what, against which budget, and what should it have cost.
