Actualyze AI
by Actualyze AI
Enterprise AI control plane that governs, secures and cost-optimises every model request
Actualyze AI is an enterprise AI gateway and control plane that sits between an organisation's people, applications and agents and every LLM they call. It ties each inference request to a named user, team and application, enforces access policy and budget, scans for PII, and routes to the cheapest or fastest provider with automatic failover.
Actualyze AI is an enterprise AI control plane that sits between an organisation's people, applications and agents and every model those workloads call, so that inference stops being an ungoverned direct line from a developer's API key to a provider. Every request is routed through a governed path, tied to a specific requester, team and application, and checked against access policy, budget and inline content scanning before it reaches OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Azure, Mistral, or a self-hosted vLLM or Ollama endpoint. The platform organises this around four pillars: Govern, covering per-team and per-model access controls, approvals, budgets and spend policy; Secure, covering inline scanning for PII and policy violations, guardrails and tamper-evident audit trails; Operate, a curated model catalogue with staging, deployment and latency and cost visibility; and Optimize, where Virtual Models route each request to the fastest, cheapest or highest-quality provider and fail over automatically when one degrades. Because the endpoint is OpenAI-compatible, adoption is a base-URL swap rather than a code change, and Actualyze abstracts the underlying provider credentials so model choice can change without touching applications. The company was founded in 2025 in Pasadena, California by Rafi Khardalian (CEO) and Sean Lynch (CTO), who previously built the managed private cloud company Metacloud and sold it to Cisco. It emerged from stealth on 3 August 2026 with a $7 million seed round led by Storm Ventures, with Canaan Partners, Morado Ventures and Jerry Yang's AME Cloud Ventures participating. The hosted platform is currently available only through an early-access design partner programme, with on-premises deployment slated for 2027.
The platform, security or FinOps leader at an enterprise where dozens of teams have quietly stood up their own OpenAI and Anthropic keys, and who now has to answer who is spending what, on which model, with which data.
Every AI request in the organisation flows through one governed, attributed and auditable path, without asking a single application team to rewrite code.
At a Glance
- Category
- Governance & Security
- Pricing
- Contact for pricing
- Target Market
- CIOs, CTOs, CISOs, Heads of AI, Platform Engineering Leaders, FinOps Teams
- Deployment
- Cloud-only, API-based
- Founded
- 2025
- Headquarters
- Pasadena, United States
- Customers
- Unnamed Fortune 500 design partners in retail, financial services and media; no public customer count
Key Features
- ✓Governed path for every inference call
Each request is tied to a user, team and application before it reaches a provider, replacing shared and untracked API keys.
- ✓Zero-code integration via base-URL swap
The endpoint is OpenAI-compatible, so existing applications point at Actualyze without any code change or SDK migration.
- ✓Virtual Models with intelligent routing
Routes each request by capability, cost or quality across providers and fails over automatically when one degrades or errors.
- ✓Token-level metering and budget enforcement
Meters spend per team and application and enforces hard caps and approval workflows before requests are allowed through.
- ✓Inline security scanning and guardrails
Inspects prompts and responses for PII and policy violations in real time rather than auditing after the data has left.
- ✓Tamper-evident audit trails
Records who called which model with what payload, giving compliance and legal teams a defensible record of AI usage.
- ✓Model catalogue with staging and deployment
Curates which models are approved, staged or retired across the organisation and tracks their latency and cost in production.
Capabilities
Use Cases
- •Ending shadow AI spend
Replace dozens of team-owned provider keys with one governed endpoint, so finance can attribute every token to a cost centre.
- •Putting a budget on agent workloads
Cap and monitor agents whose single user task fans out into dozens of autonomous model calls with unpredictable cost.
- •Blocking regulated data in prompts
Scan outbound prompts inline for PII and policy violations so sensitive records never reach a third-party model provider.
- •Switching model providers without a migration
Change the model behind a Virtual Model definition and every downstream application follows, since credentials and endpoints are abstracted.
- •Producing an AI usage audit trail
Give compliance and internal audit a tamper-evident record of which team called which model with which data, and when.
Ideal For
Best For
- ✓Attributing token spend to named teams, applications and cost centres when finance can no longer reconcile provider invoices
- ✓Enforcing which teams may call which models, with approval workflows and hard budget caps per team or application
- ✓Preventing PII and regulated data from leaving the organisation inside prompts, with inline scanning and audit trails
- ✓Keeping model choice portable across OpenAI, Anthropic, Bedrock, Vertex AI and self-hosted endpoints without application changes
- ✓Containing the cost blast radius of agents, where one user task fans out into dozens of autonomous model calls
Not Ideal For
- ✗Regulated, air-gapped or data-residency-constrained buyers today: only a hosted SaaS exists, and on-premises deployment is not promised until 2027
- ✗Teams that need a production-ready, generally available product with published SLAs. Access is currently limited to an early-access design partner programme
- ✗Engineering-led organisations comfortable running open-source gateways such as LiteLLM, where the governance and attribution layer can be assembled for the cost of maintaining it
- ✗Latency-critical inference paths where adding a proxy hop between application and model provider is an unacceptable trade for governance
Integrations
Deployment
Market & Ratings
Unnamed Fortune 500 design partners in retail, financial services and media; no public customer count
Market Analysis
Pros
- ✓Zero-code adoption via an OpenAI-compatible base-URL swap removes the usual blocker for a governance layer, which is getting every application team to change code
- ✓Ties spend and policy to identity rather than to an API key, which is what actually enables chargeback, approvals and least-privilege model access
- ✓Genuinely provider-neutral across OpenAI, Anthropic, Bedrock, Vertex AI, Azure, Mistral and self-hosted vLLM or Ollama, with automatic failover
- ✓Founders have prior enterprise infrastructure exit experience with Metacloud, acquired by Cisco, and spent a year with platform, security and finance leaders before building
Cons
- ✗Early access only. There is no general availability, no self-serve signup and no published SLA, so this is a design-partner bet rather than a purchase
- ✗Hosted SaaS is the only deployment option and on-premises is not expected until 2027, which rules it out for air-gapped, sovereign or strict data-residency environments
- ✗Zero independent buyer evidence exists: no G2, Capterra, TrustRadius or PeerSpot listing, no Product Hunt launch, and no Hacker News or Reddit discussion of the product could be found
- ✗The AI gateway category is crowded and partly commoditised. LiteLLM is free and open source, and Cloudflare, Kong and Databricks bundle comparable gateways with infrastructure customers already buy
- ✗The headline efficiency numbers, 20 to 25 percent lower token cost and 22 percent less untracked usage, are vendor-reported from a small design-partner cohort with no published methodology
- ✗Inserting a proxy in front of every inference call adds a hop and a new single point of failure in the critical path, which the failover feature mitigates but does not remove
Pricing
Early Access Design Partner Program
Contact for pricing
- ✓Hosted SaaS control plane
- ✓Govern, Secure, Operate and Optimize pillars
- ✓OpenAI-compatible endpoint
- ✓SSO via SAML/OIDC, Okta and Entra ID
- ✓Virtual Models with routing and failover
- ✓Token-level metering and audit logging
No list pricing is published anywhere. The platform is sold only through an early-access design partner programme, so commercial terms are negotiated case by case and there is no self-serve tier, no free tier and no published trial. Expect the total cost of ownership to be the Actualyze contract on top of the underlying provider tokens, which you continue to buy directly; the vendor's counter-argument is design-partner data showing 20 to 25 percent lower token cost and a 22 percent drop in untracked usage, both unaudited.
Security & Compliance
Sources
This page was written from 6 sources, 5 on domains other than actualyze.ai.
- 1.prnewswire.com — actualyze ai emerges from stealth with 7m seed round to deli
- 2.pymnts.com — actualyze ai raises 7 million for platform for governing ai
- 3.techedgeai.com — actualyze ai unveils enterprise ai platform to govern secure
- 4.finsmes.com — actualyze ai raises 7m in seed funding
- 5.citybiz.co — actualyze ai launches from stealth with 7 million seed round
- 6.actualyze.ai — actualyze.aivendor
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