22B
Fewer wasteful tokens, stale tool results and round trips.
Stop paying for
unused tokens
Anyray helps organizations reduce LLM costs with inference compression. Better performance, same quality, lower costs.
Works with models and clouds
Research-backed.
Real-world production ready.
Anyray compression engine is grounded in breakthrough research that redefines token compression.
Deterministic
No model in the loop. Same input, same output.
Cache-safe
Cached bytes never change.
Fail open
Any error, the request goes through untouched.
Guardrails
Any step that hurts quality, latency or cache turns itself off.
Retrieval
Anything removed is one call away, byte for byte.
Trusted in production.
Stable under production load, no loss in quality, and lower spend from day one.
28%
Less LLM spend across ScyllaDB's R&D teams and agents.
Putting anything in front of every LLM call takes a leap of faith. Anyray earned that trust. It has cut our spend significantly across R&D and agents. The Anyray team feels like part of our platform team.
Yaniv
VP R&D, ScyllaDB
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
+40%
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.
-
01
One platform
Any provider, any model.
-
02
Self-hosted
Runs inside your cloud. Nothing leaves your network.
-
03
Seamless
No change to workflow, no IT overhead.
The numbers, per workload.
Anyray compresses and optimizes payloads without degrading the output — across every workload, from coding agents to research, analysis, and operations.
By function
-
Data
38%
Logs, metrics, and JSON dumps.
-
Coding
30%
Code search, file reads, and diffs.
-
Tools & MCPs
68%
Returns only the output the agent asked for.
-
Memory & recall
79%
Long sessions and cross-session recall.
View and reproduce Anyray benchmarks on GitHub
By workload
| workload | saved |
|---|
| Access logcompacting repetitive entries down to the relevant few | |
| Incident debuggingkeeping only the signals that point to the cause | |
| Synonym-gap logsfinding an OOM event the question never names | |
| GitHub issuesreading GitHub issues/comments to categorize/prioritize | |
| JSON arraykeeping only the fields and items the task uses | |
| Code searchgrep/semantic search returning many file+line hits | |
| Git diff outputreviewing what changed in a commit or PR | |
| Test-suite outputkeeping only the failed tests and the lines that explain them | |
| Flaky-suite re-runcollapsing a near-duplicate test re-run to what changed | |
| Codebase explorationreading source files to understand architecture | |
| Read a Python modulekeeping only the function bodies on the task path | |
| Read a large TS filekeeping only the code paths a TypeScript service task touches | |
| Multi-file tracefollowing one call path across several files | |
| Recurring reportremoving repeated instructions across a nightly batch | |
| RAG retrievalkeeping only the chunks that answer the question | |
| MCP tool bloatkeeping only the tool schemas the task calls |
See what every token buys.
You finally get the breakdown — spend tied to real work, so you can have an ROI conversation per project, not per invoice.
- By project
- By team
- By model
checkout-api$18.4k
agent-pipeline$12.1k
growth-web$9.7k
support-bot$5.3k
internal-tools$2.7k
Debugging 32%
Feature dev 42%
Code review 19%
Other 7%
Platform · 35%$16.9k
Coding assistants · 27%$13.2k
Data science · 19%$9.3k
Support · 12%$5.8k
Growth · 6%$3.0k
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 as a container in your own cloud account. Nothing routed off the network.
-
Your endpoints
Talks only to the model APIs you already trust. No new vendor in the data path.
-
No new dependency
If Anyray is ever unavailable, traffic routes straight through, untouched.
A single pane of glass.
The Anyray Console gives leaders visibility and governance across AI tools and providers.
Observability
Token usage and cost, broken down by user, team, model, and provider.
Single view
One console for every AI your organization runs.
Fallback & Budget
Export
Fallbacks & Enforcement
Budget Fallbacks gpt-5-mini claude-haiku-4-5 On Budget Exceeded Throttle Block FallbackBudget
Max Budget $10,000.00 Reset Budget monthlyResets on the 1st of each month at 00:00 UTC
Budget Window rolling 30d Soft Limit Alert 80% · #ai-platform-opsAdvanced · per-model budgets, tag budgets, reset webhooks
Cancel
Save Settings
Governance
Manage keys, budgets, and fallbacks per user or agent across all tools.
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
Token spend by user, team, model, and project, with intent breakdown across debugging, feature development, code review, and more.
Security
No new vendor in the data path. Traffic stays on infrastructure you already trust, with full audit visibility.