Framework alignment & audit status — not a certification claim. SOC 2 Type II and ISO/IEC 27001 audits in progress.
Route. Observe. Tackle. Audit.One AI gateway for every LLM call.
Self-hosted AI gateway between your stack and every LLM provider — governance, security and cost control in one endpoint, with sub-5ms in-path overhead.
Deploys on
Why teams chooseRota Engine.
Near-zero overhead on every request, at any scale.
Point your existing SDK at the gateway URL. Done.
Every item on the OWASP LLM Top 10 — on by default.
In-stream provider failover keeps your AI running.
Swap any provider by changing the model string.
Every call logged with a cryptographic chain. SIEM-ready.
Concurrency scales with load — no capacity planning.
Failed calls retry on a healthy model, transparently.
A whole provider degrades? Route to a standby group.
P50/P95/P99 latency, tokens and errors — per route.
Injection, PII and policy checks before the model call.
Grounding, leakage and toxicity checks on responses.
Works for you, works with you.
Ecosystem compatibility — an OpenAI-compatible client connects in one URL change, zero code migration. Logos denote interoperability, not endorsement.
ONE AI GATEWAY.
Every model. Every tool. Fully governed.
One integration. Complete control.
Change one line.Govern every call.
One URL. Your SDK, unchanged. Every model, every endpoint — governed.
Self-hosted by design.Runs in your region.
Rota Engine deploys entirely inside your own cloud or datacenter — any provider, any region. Requests and keys stay on infrastructure you control; nothing routes through us.
- Runs on
- AWS · GCP · Azure · on-prem
- Data residency
- Stays in your chosen region
- Keys & traffic
- Never leave your VPC
- Ships as
- One container · Helm chart
Automatic LLM guardrails.Every control your auditors ask for.
Normalization, injection detection, PII redaction, budgets and audit — every request runs the full stack, zero config on day one.
Input normalization
Unicode, homoglyph, zero-width, bidi and base64 decoded into one canonical form — so guardrails see the real prompt.
PII detection & redaction
Regex and ML detectors strip names, emails, SSNs and tokens before a request ever leaves your VPC.
Content policy engine
YAML allow/deny rules per model, per team. Test policies on real traffic before you roll them out.
Cost attribution & budgets
Per-team, per-app and per-key quotas. Hard caps, soft alerts and a single spend timeline across four levels.
Rate limiting & throttling
Sliding-window limits per key, per model and per tenant — deterministic, not best-effort.
Unified provider routing
Load-balance and fail over across every major LLM provider with one OpenAI-compatible endpoint.
Immutable audit logging
Every request, redaction and routing decision logged with a cryptographic chain. SIEM-exportable.
Real-time observability
P50/P95/P99 latency, token throughput, error rates and per-route breakdowns — out of the box.
Proof in numbers,not slideware.
Rota sits in-path with 50 ms p99 added latency, scales linearly per node, and catches threats other stacks miss entirely. Below: our own load-test numbers against a reference DIY gateway and generic proxy, plus a capability matrix checked against the public docs of the AI gateways teams actually evaluate — LiteLLM Proxy, Portkey, and Cloudflare AI Gateway.
In-path, near-zero overhead
p99 added latency versus 180 ms for a DIY gateway and 420 ms for a generic proxy — fast enough to be invisible in chat and voice UX.
Scales linearly
Throughput per node versus 1,200 req/s DIY and 600 req/s generic — one node covers a mid-size app's peak traffic without re-architecting.
Catches what others miss
Threat catch rate versus 61% for DIY and 12% for a generic proxy — the gap between blocking a prompt-injection attempt and shipping it straight to your model.
How Rota compares
Our own load test vs. a reference DIY gateway and generic proxy — methodology below.
- Rota Engine
- DIY gateway
- Generic proxy
Delay Rota adds to every request, end to end.
Requests one node sustains before you need another.
Repeat prompts served from cache instead of re-billing the LLM.
Attacks correctly blocked from a 10k-prompt OWASP LLM Top 10 corpus.
Figures from Rota's internal load tests on a single 8-vCPU node and a 10k-prompt OWASP LLM Top 10 corpus, run against a reference DIY gateway and generic reverse proxy Rota built for comparison — not a claim about any named competitor. Your results depend on traffic mix.
Capability matrix
Checked against LiteLLM Proxy, Portkey, and Cloudflare AI Gateway — the AI gateways teams actually put next to Rota.
| Capability | Rota Engine | LiteLLM Proxy | Portkey | Cloudflare AI Gateway |
|---|---|---|---|---|
| Prompt-injection / adversarial-input scanning | ||||
| Multi-provider automatic failover | ||||
| Semantic response caching | ||||
| PII / secret redaction | ||||
| Self-hosted, zero data egress |
Checked against each vendor's own public documentation in July 2026 — LiteLLM Proxy's official docs, Portkey's public GitHub README, and Cloudflare AI Gateway's developer docs. Dashed dash = not published there, not a claim the capability is absent. Partial = present, gated to a paid or enterprise tier.
This isn't a demo.This is production.
One gateway. Every LLM vendor, governed.
Every model, one endpoint. Real-time token tracking, rate limits and content safety on every call — not bolted on after.
HIPAA compliance, by default.
Zero-trust routing with automatic PHI detection and immutable audit trails — governing budgets across 12 clinical apps.
One brain for 8 million requests a day.
Multi-model orchestration across GPT-4, Claude and fine-tuned models — smart cost-routing and automatic fallback, built in.
Replacing four boxes withone path.
Rota turned our AI rollout from a CISO escalation into a checkbox. The audit trail alone paid for the year.
We swapped four sidecars and a homemade proxy for one path. P50 went down, not up.
First gateway we tried that didn't make us choose between speed and governance.
Quotes from Rota Engine customers, shared with permission. Some names and companies are anonymized at the customer’s request.
What is an
AI gateway?
Also called an LLM gateway, LLM proxy or AI control plane.
An AI gateway is a single, governed endpoint that all LLM and agent traffic flows through. Instead of every application integrating each model provider directly — and re-implementing security, spend limits and logging in every codebase — the gateway enforces them once, in one place, for every request.
In practice that means multi-provider LLM routing (OpenAI, Anthropic, Gemini, Bedrock, Vertex, Groq, xAI and more behind one API), health-aware fallback when a provider degrades, hierarchical cost budgets per team and tenant, and in-path LLM guardrails: prompt-injection defense, PII and secret redaction, and insecure-output handling — aligned with the OWASP LLM Top 10 and NIST AI RMF. Every decision lands in an immutable, hash-linked audit trail.
Rota Engine is the self-hosted AI gateway: it runs inside your own VPC via Docker, Kubernetes or Helm, so prompts and data never leave your boundary — zero retention by default, data residency (GDPR/KVKK) met by architecture rather than by contract. Integration is typically one base-URL change, and median in-path overhead stays under 5ms, so governance never becomes the bottleneck.
Questions,
answered plainly.
What an AI gateway is, how Rota Engine deploys self-hosted in your VPC, and where it sits in your AI stack.
01What is Rota Engine?
Rota Engine is an enterprise AI control plane: a self-hosted gateway that sits between your stack and every LLM and agent provider, adding routing, security, cost control and audit on a single endpoint.
02Is Rota Engine self-hosted?
Yes. It runs inside your own VPC and deploys via Docker, Kubernetes or Helm. Your prompts and data stay within your boundary, with zero retention by default — Rota Engine is software you run, not a destination for your traffic.
03What is an AI gateway?
An AI gateway is a single endpoint that all LLM and agent traffic flows through, so routing, security policy, spend limits and logging are enforced in one place instead of being re-implemented in every application.
04What is the difference between an AI gateway and an API gateway?
An API gateway routes opaque REST/gRPC traffic; an AI gateway understands LLM traffic — tokens, prompts, streaming responses and model semantics. That awareness enables controls an API gateway can't provide: token-level budgets, model fallback chains, prompt-injection defense and PII redaction in the request path.
05Is an AI gateway the same as an LLM gateway?
The terms are used interchangeably. Both describe a middleware layer between your applications and model providers that adds routing, security, cost control and observability. Rota Engine covers both: LLM traffic and agent/tool traffic through one governed endpoint.
06Why choose a self-hosted AI gateway over a SaaS one?
With a SaaS gateway, every prompt — including PII and secrets — transits a third party's infrastructure. A self-hosted AI gateway like Rota Engine runs inside your own VPC, so data never leaves your boundary, residency requirements (GDPR/KVKK) are met by architecture, and there is no per-token markup on your traffic.
07What is AI governance?
AI governance is the set of policies and controls that decide which team can call which model, under what data class and budget, with every decision logged and auditable. Rota Engine implements it as config, not code review: policy routing by data class, cost class and latency target, hierarchical spend budgets per team/tenant/app/key, and an immutable, hash-linked audit trail.
08How much latency does Rota Engine add?
Median in-path overhead is under 5ms, so Rota Engine can govern every request without becoming a bottleneck in production traffic.
09Which model providers does it support?
Every major LLM and inference provider behind one endpoint — including OpenAI, Anthropic, Gemini, Bedrock, Vertex, Groq and xAI — with new models routable the day the provider ships them.
10How does Rota Engine provide AI security?
It provides prompt-injection defense, PII and secret redaction, insecure-output handling and an immutable, hash-linked audit trail. Controls are aligned with the OWASP LLM Top 10, NIST AI RMF and KVKK/GDPR.
11How does it control AI spend?
Hierarchical budgets let you cap and attribute spend per team, tenant or environment, so every model call is measurable and cost is governed rather than discovered on the invoice.
12How hard is it to integrate?
Rota Engine is a drop-in: point your existing SDK or base URL at the gateway — typically a single URL change — and existing calls flow through the control plane unchanged.
Ship AI features fast.Govern them securely.
Bring one month of real traffic. We configure your first policy, route a slice through Rota and report latency, savings and risk reduction in numbers — not slides.