Anyray

Stop paying for
unused tokens.

Ensure your organization gets the most out of LLMs
with tokens optimization and attribution

Customer overview · 2026 · Confidential

The bill is compounding.

Every AI interaction sends wasteful tokens to the model.
Every token is billed.

You pay for them in spend and performance, on every call, compounding across sessions and teammates.

a typical LLM request context
signal noise
40–70%
of tokens are waste
100%
billed at full rate
every call
the same waste ships again

Just-in-time
optimization.

Anyray sits between users, agents, and models, optimizing every interaction in real time.

  • +320Btokens processed
  • 48%average tokens saving
  • +35%extra usage per seat
  • 02
    Self-hosted

    Runs inside your cloud. Nothing leaves your network.

  • 01
    One platform

    Any provider, any model.

    LangGraph
  • 03
    Seamless

    No change to workflow, no IT overhead.

Seven layers deep.

  1. 01Input
  2. 02Context
  3. 03Tools & MCPs
  4. 04Files
  5. 05Dev commands
  6. 06Codebase graph
  7. 07Caching

Anyray runs a
pipeline of optimization strategies
across all dimensions.

R&D

Agentic workflows

Analysts & BI

Back office

Knowledge workers

CI & scheduled jobs

Workload in, waste out.

Save up to 60% tokens with no loss in quality.

  • Savings

    Structural waste is removed automatically before it reaches the model. Every saved token is tracked in the admin console.

  • Productivity

    Engineers ship more with fewer cycles. Wasted interactions are identified and reduced automatically.

  • Performance

    Faster, more accurate responses as context stays clean, with fewer back-and-forth loops.

  • Visibility

    Monitor your organization's token usage and spend in one place, with an aggregated view across providers.

A single pane of glass.

The Anyray control plane gives leaders visibility and governance across AI tools and providers.

Overview

Export
Range: Last 30 days Org: All $48,210.94 total
Spend by Team
platform-eng$18,309.44
data-science$10,742.15
ai-gateway$6,301.42
ml-platform$5,905.20
Top users and agents
dana.reyes$6,740.15
code-review-agent$4,182.55
a.silva$3,301.42
nightly-etl-agent$1,905.20
Top Models
gpt-5$16,884.20
claude-opus-4-8$12,402.55
claude-sonnet-4-5$8,115.90
gemini-2.5-pro$5,340.18

Spend and usage

Token usage and cost, broken down by user, team, model, and provider.

The Anyray console overview: tokens saved, total saving, value created and extra capacity, above savings per day, tokens saved by model, and tokens broken down by team and by user.

Single view

One console for every AI your organization runs.

Budget & Fallback

Export
Budget
Max Budget $10,000.00 Reset Budget monthly

Resets on the 1st of each month at 00:00 UTC

Budget Window rolling 30d Soft Limit Alert 80% · #ai-platform-ops
Fallbacks & Enforcement
Budget Fallbacks gpt-5-mini claude-haiku-4-5 On Budget Exceeded Throttle Block Fallback
Advanced · per-model budgets, tag budgets, reset webhooks
Cancel Save Settings

Budget

Set a budget per user or agent across all tools.

Your data never leaves your network.

Anyray deploys inside your environment. Requests are optimized in-network, then forwarded to the model providers you already use.

  • Self-hosted

    Runs inside your cloud, nothing routed off the network.

  • Your endpoints

    Talks to the models, providers, and APIs you already trust.

  • Fallback

    If Anyray is ever unavailable, traffic routes straight through, untouched.

What teams are saying.

  • We turned on Anyray for our engineers expecting a billing note, and it became a metric we now track. Token spend dropped right around 60%, but the part the team actually noticed was performance. The agents stopped dragging half-irrelevant tool output into context, so responses came back tighter and we burned fewer round-trips per task.

    Rennen, CTOCallers AI

  • Before Anyray we knew our LLM bill was large and almost nothing about where it went. We run Claude, Codex and workloads on Bedrock, and Anyray handles all of them — spend broken down by user, team, model and provider, in one console. It took spend down substantially across our R&D teams and internal agents.

    Sagi, AI Platform LeadAccessFintech

Cumulative savings (tokens)
MarchAugust

Get a demo.

Go live in one day and watch token spend fall meaningfully.

  • Proven in production: +150B tokens saved across live deployments.

  • Every provider, every cloud: Anthropic, OpenAI or Copilot, self-hosted in AWS, Azure or GCP.

  • Control plane: full observability into token usage across your organization.