ShellNewHandler VS OptScale AI

OptScale AI's answer:

Every AI gateway shows what you spend. OptScale AI shows what the spend produced. It joins per-actor model cost to merged PRs, features and tickets from your own systems of record — so you measure cost per merged PR, per feature, per team, not cost per token. Around that join sit four more pillars: an AI gateway routing across local models, public LLMs and MCP servers at 2 ms overhead; lossless cost optimization (token compression, cache alignment, prompt trimming, model selection); inline guardrails with content filtering, PII detection and DLP; and agent control with cost, time and recursion limits. Measurement leads, governance enforces — you can’t safely route to a cheaper model until you can prove the output still holds up.

OptScale AI's answer:

Three reasons. First, the impact join: a proxy sees tokens leaving, it can’t see whether the PR merged, got reverted, or took three review rounds — OptScale AI reads both sides and puts them on one row. Second, one control plane instead of four tools: routing, compression, guardrails and agent control ship together, so the savings are governed by default rather than bolted on later. Third, evidence and deployment posture: model and agent inventory, decision and redaction logs, audit-ready evidence export, GDPR Art. 27 and EU AI Act Art. 22 representation, and on-premises or air-gapped deployment where content never leaves your environment. Individual-level analytics are off by default — org-gated, small-group suppressed, audit-logged. Measurement, not surveillance.

OptScale AI's answer:

Enterprises whose AI usage has outgrown what anyone can account for. Four buyers sit around it: engineering leaders who need to show what AI-assisted development actually delivered; finance teams who need AI spend attributed per team, department and feature; security and compliance teams who need every prompt screened and an audit trail they didn’t have to assemble by hand; and platform teams who need agents bounded and unauthorized MCP servers and vector stores blocked. Strongest fit in regulated and data-sensitive sectors — banking and financial services, technology and software, media and communication, education, retail and e-commerce — and anywhere data sovereignty rules out sending everything to a public endpoint.

OptScale AI's answer:

OptScale AI was built around four exposures every organization running AI without a control plane carries today: prompt leakage, with code, contracts and PII pasted into consumer LLM accounts and no record it happened; shadow AI on personal provider keys, invisible to both invoice review and DLP; unbounded agents whose recursion loops surface on next month’s bill; and no audit trail for the question “who sent what, to which model?” — a question you eventually get in writing. Every tool on the market answered half of it by reporting spend. None could say what the spend produced. So the platform was built the other way round: measure the outcome first, then route it cheaper, screen every prompt, and keep every agent inside its limits.

OptScale AI's answer:

An LLM gateway sits on the request path, routing across local models, public LLM providers and MCP servers at 2 ms overhead, with compression, cache alignment, prompt trimming and model selection applied inline and reported as separate levers. Pre-built MCP servers connect the surrounding stack — GitHub, Jira, Slack, Confluence, Salesforce, Google Docs, Notion, HubSpot, Okta, AWS, QuickBooks — and auto-discover available tools and data sources. Guardrails run inline in both directions for content filtering, PII detection and DLP. Agents built with LangChain, CrewAI or custom code are registered and governed, not executed. The context-compression engine for coding agents is open source: github.com/hystax/optscale-kompressor. Deploys as SaaS, private or air-gapped, with SSO/SAML and local model hosting.

OptScale AI's answer:

SoftwareOne, NEKO Health, Luminance, Dexory, Taktile, osapiens, PhysicsX, Veesion, Viadex, SecureData, Nubephant, Bluebill