Amnic: AI Token Management
Amnic · Finance & Accounting
Certification per AWS Marketplace.
Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
Amnic AI Token Management brings complete visibility and control to your AI spend. Supporting all major LLM providers (OpenAI, Anthropic, Google, and more), it functions as a purpose-built cost intelligence layer for teams scaling AI workloads. With Amnic AI Token Management, you can track token consumption in real time, set budget thresholds, detect anomalous spend spikes, attribute costs to teams and users, and compare model efficiency; all without manual log-digging,spreadsheet reconciliation, or end-of-month billing surprises.
Designed for Engineering, FinOps, and Finance teams, Amnic AI Token Management turns fragmented AI usage data into clear, actionable cost decisions. Whether you are right-sizing model selection, enforcing team-level budgets, identifying wasteful patterns, or producing accurate internal chargebacks, Amnic eliminates the blind spots that make AI spend unpredictable at scale.
Show the rest of the publisher’s description (3 more lines)
Amnic AI Token Management simplifies daily AI cost operations into a proactive system of accountability. Define spending limits at any level of granularity, get alerted before budgets are breached, and catch LLM-specific anomalies that generic cloud monitoring tools miss.
Built on the robust foundation of the Amnic platform, AI Token Management adds granular attribution and governance across every model, team, user, and cost center.
Four core capabilities: Budget Controls, Model Breakdown, Cost Attribution, and Anomaly Detection deliver the precision your teams need to scale AI confidently without the budget surprises.
Highlights
Highlighted by the publisher on AWS Marketplace.
E2E token tracking: Monitor LLM usage across OpenAI, Anthropic, Google, and more in a single dashboard, broken down by model, team, or service.
Team & model-level budgets: Set spend limits at any level of granularity and get proactive alerts before costs breach thresholds or hit your invoice.
Cross-model visibility: Compare token consumption and cost across GPT-4, Claude, Gemini, and more to guide smarter model selection and routing decisions.
Agent build and provenance
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The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Plans and pricing as listed
4 listed- Units
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Refund terms
As stated by the publisher on AWS Marketplace.
Thank you for choosing Amnic. We do not offer refunds for our products; all sales are final. However, exceptions may be made at our sole discretion for issues such as service defects or billing errors. To request an exception, contact us at hello@amnic.com within 30 days of the issue, providing your account details and a description of the problem. Our team will review and respond within 5 business days. Thank you for using Amnic.
Sources
Publisher resources
1 linkLinked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
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