AI DevOps
Intelligent AI gateway for cost control, access governance, and data sovereignty across LLM and MCP interactions
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OptScale AI is a unified platform for FinOps and AI governance that routes, optimizes, and controls every AI interaction across local models, cloud LLMs, and
OptScale AI extends OptScale, an open-source FinOps platform trusted by enterprises since 2016, into AI governance. The platform includes an intelligent AI gateway that automatically routes queries to optimize for cost, latency, and compliance; real-time token compression through the Kompressor engine that cuts costs by up to 22.7% without breaking prompt caching; AI ROI & Impact analytics that connect spend to business outcomes; AI security guardrails including PII detection and DLP enforcement; and AI agent control with 20+ MCP connectors. It integrates with existing code via OpenAI-compatible API with sub-2ms overhead, works as SaaS or on-premises, and is trusted by enterprises including Airbus, Nokia, T-Systems, DHL, and PwC.
Pricing model: Freemium
No. Kompressor runs inline inside the OptScale AI gateway as a proxy. You point your agents at the gateway and compression applies to every request automatically, without touching the agent, its prompts, or its results. There is no SDK migration or per-agent instrumentation, and it adds about 0.2 ms per KB of tool output.
No. Kompressor is deterministic: the same tool output compresses to the same bytes every turn with no randomness or dependence on the user's question, so provider cache reads stay valid. It also fails open, so any result that is not smaller is discarded and any error passes the request through unchanged.
The largest gains are in ML training, builds from source, and data pipelines, where 55–81% of tool output is removed and roughly a quarter to a third comes off the bill. Short, reasoning-heavy tasks and plain prose have little to remove by design, so expect little saving there.
It de-duplicates rather than summarises, so the model keeps at least one real copy of every distinct line, and every elision is labelled and retrievable — the model can pull the original back for an hour through a retrieve call the gateway runs for it. It only touches machine-generated tool output over about 512 characters, never system, developer, or assistant messages. In the benchmark, solve rate was statistically unchanged (81.1% versus 82.2% with no compression).
Every benchmark figure is the difference between real provider bills with the cache discount already included, not tokens priced at the uncached list rate. On your own traffic you can hold out a slice as an unshaped control group and compare tokens billed side by side, labelled measured versus estimated. Where a provider or tier does not cache at all, the roughly 25% cut in prompt traffic goes straight to the bill.
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