Enterprise AI usage, governance & cost control
Control every request.
Govern every agent.
Account for every dollar.
TetherLLM gives enterprises one governed layer for AI usage, policy, cost attribution, and billing across every LLM and agent— enforcing control before spend while keeping provider choice open.
01
Built for organizations managing five- to seven-figure AI budgets. Start with a focused paid pilot sized to the control problem.
LIVE REQUEST PATHjob_7F3A91
LIVEsupport-agentprod / customer-ops
$0.01801
Identity + attributionTeam, project, agent, session
VERIFIED02
Policy + budgetReserved against monthly limit
PASSED03
Capability routingAlias: approved-reasoning
ROUTINGSelected providerVertex AI / Gemini 2.5 Pro
842ms latency97% policy confidence
One governed layer across
OpenAIAnthropicAzure OpenAIAmazon BedrockGoogle Vertex AIPrivate endpoints
01 / THE THESIS
AI adoption moved fast.
Control didn’t.
When every team buys models, builds agents, and retries work in a different tool, finance sees invoices, but nobody sees the system.
TetherLLM sits in the request path and owns the canonical record. It connects identity, policy, routing, attempts, outcomes, and cost without forcing your enterprise into a lowest-common-denominator API.
02 / THE PLATFORM
Govern the request.
Understand the outcome.
The gateway makes the real-time decision. The ledger and analytics explain what happened next. Both speak the same canonical language.
01Budget check: passed
Policy before spend
Stop runaway cost before the request leaves.
Enforce budgets, model access, provider policy, token limits, concurrency, and rate rules at the gateway, not in a report that arrives next week.
02Variance: −2.8%
Defensible accounting
Know what every AI dollar actually did.
Attribute requests and retries to the right tenant, department, project, application, agent, and user, with reserved, estimated, provider-reported, and reconciled cost kept distinct.
03Trace: 8 linked events
Agent governance
Treat agents like production software.
Give every agent an owner, version, permission scope, trace, evaluation history, and kill switch. Follow the whole job across tools, retries, fallbacks, and providers.
04Context health: 82
Context intelligence
Optimize for outcomes, not fewer tokens.
Explain when a conversation should continue, branch, compact, summarize, or start fresh based on observable context health, cache behavior, cost, and task success.
03 / OPERATIONAL TRUTH
A ledger, not a dashboard veneer
Estimated cost is not actual cost.
TetherLLM preserves provenance from the first reservation to the final provider reconciliation. Teams can forecast quickly without asking finance to trust an approximation forever.
- Every retry and fallback remains linked to the logical job
- Effective-dated provider pricing prevents historical drift
- Chargeback data stays attributable and auditable
AI SPEND / JULY$184,290.44
92% RECONCILEDSTATUSAMOUNTAS OF
Reconciled$169,547.2111:40 UTC
Provider reported$9,802.8811:37 UTC
Estimated$4,940.35Live
04 / YOUR BOUNDARY
Run it where trust requires.
One versioned deployment cell. Two operating models. Your prompts, provider credentials, detailed usage, and policy remain inside the customer boundary.
OIDC + SCIM◆Audit evidence◆Configurable retention◆Regional cells◆Customer-managed keys
A focused first step for material AI budgets.
Scope a paid
enterprise pilot.
Bring your provider mix, annual AI budget, agent portfolio, and hardest control gap. We’ll use a 30-minute working session to define a measurable paid pilot around usage, governance, or billing, with focused four-figure entry scopes available.
PILOT FIT
Book a pilot briefing
Best for teams with a five- to seven-figure annual AI budget and a named usage, governance, billing, or agent-control problem.
- One priority workflow or control boundary
- Defined operational and financial success criteria
- Four-figure entry scope for focused control problems
Prefer async?Qualify your pilot.