Multi-LLM routing
Route prompts by capability, cost, latency, and sovereignty — frontier when needed, regional when sufficient, open-weight when sovereign.
AI teams · Platform
Every AI call lives on the gateway.
Apinizer's AI Gateway routes prompts, governs MCP servers, registers agent identities, filters bad input, and posts cost to the same audit trail you already use for APIs.
The problem
AI traffic without a gateway is a second platform you didn't budget for.
Teams that skip the gateway get a parallel universe — model keys in env vars, MCP tools wired straight into apps, agents talking without identity, costs invisible until the invoice. Apinizer treats AI as just another lane: same Manager, same Workers, same audit, same identity. The AI plane is a feature, not a side project.
- 1
AI plane
- 5+
AI protocols
LLM · MCP · A2A · embeddings · vision
- 0
audit gaps
Capabilities
What Apinizer does here
MCP server governance
Every MCP tool registered, scoped, audited. Agents only see the tools you allow; every call lands in the audit ledger.
Agent-to-Agent gateway
A2A identity, scope, and trust live on the gateway. Agents authenticate like people; the gateway keeps the trust boundary.
Prompt firewalls
Injection, jailbreak, content safety, DLP, and checksum-validated PII masking at the edge — with encoded payloads decoded before any guard reads them, and an external guardrail engine behind them when a pattern is not enough. Bad prompts blocked; clean prompts logged; the compliance answer is one query.
Cache + cost control
Semantic cache short-circuits paid calls; per-consumer token attribution turns every prompt into a line item.
Inventory and retention
One screen lists every AI asset in the installation — proxies, providers, models, knowledge bases, vector databases — and a shadow-AI report names the models being called that nobody put in the catalog. Retention profiles decide how much of an exchange is ever written down, from the full record to nothing at all.
AI observability
Latency, errors, tokens, anomalies — same Elasticsearch surface as your APIs. Trace any AI call back to a consumer, project, and model.
Use cases
In production, this looks like…
- Banking
Istanbul bank puts customer-service LLM behind the same gateway as core APIs
AI assistant inherits the API plane's auth, audit, and rate-limit policy. Regulator stops asking 'where does the model live'.
1 control plane
- Automotive
Munich OEM unifies vehicle telemetry APIs and in-vehicle assistant LLM traffic
Connected-car APIs and the assistant share one gateway, one identity, one trace. AI rollout doesn't reopen the platform RFP.
- Government
Riyadh ministry ships a citizen chatbot under existing controls
Chatbot is just another endpoint. Authentication, audit, and rate-limit inherited from the citizen API surface — no new compliance memo.
- Insurance
Paris insurer routes claims AI through the same gateway as claims APIs
Underwriting model calls go through the same access control as claims data. One dashboard for both; one auditor answer for both.
- Telecom
Milan carrier retires a standalone AI router POC after 5 months
Multi-LLM routing, semantic caching, MCP governance, observability all arrive in Apinizer. The standalone stack is decommissioned.
1 vendor retired
- Retail
Amsterdam retailer governs supplier API traffic and supplier-agent A2A traffic together
B2B partners hit the same gateway; their integration agents are published as A2A agents on the gateway. Same SLA, same audit, same identity.
- Public sector
Central-European ministry routes local-language prompts to a regional model first
90% of summarization traffic served by an in-region resident model; frontier providers used only for adversarial cases.
90% regional
- Energy
Baku utility runs operations agents on open-weight models inside the operator network
Local 70B model serves SCADA-adjacent agents. Hosted providers reserved for non-operational use. The agent never crosses the network boundary.
How a call moves
Route, enforce, cache, audit — in that order.
- Step 01
Route
Capability, cost, latency, and policy decide the model — frontier, regional, open-weight, or cache.
- Step 02
Enforce
Prompt firewall, PII redaction, scope check, and consumer rate limit run before the upstream call.
- Step 03
Cache
Semantic match in the vector index short-circuits paid calls when the meaning has been answered before.
- Step 04
Audit
Prompt, response, tokens, cost, consumer, project — all indexed in Elasticsearch with the rest of your traffic.
Recommended products
What we'd reach for first
AI Gateway
The AI lane — routing, MCP governance, A2A gateway, firewalls, semantic cache.
Open the AI Gateway pageIdentity Manager
One identity surface for API consumers, AI consumers, and agent identities.
Open the Identity pageAnalytics Engine
Per-model, per-project, per-consumer telemetry on the same dashboards as your APIs.
Open the Analytics pageMonitoring
Provider health, latency probes, anomaly detection across every AI lane.
Open the Monitoring pageResources
Keep going
AI Gateway overview
The AI lane on the same Manager, Workers, audit, and identity surface as your APIs.
AI Gateway product
Multi-LLM routing, MCP governance, A2A gateway, firewalls, cache.
Architecture overview
Where the AI lane sits in the topology — control plane, data planes, providers.
AI observability
Trace any AI call back to a consumer, project, model, and cost.
Token economics
How per-consumer cost reports turn every prompt into a line item.
Unified API & AI platform
The strategy: one platform, every protocol, every audit — AI included.
One AI plane
Put every AI call on the gateway you already trust.
A 30-minute walkthrough — routing, MCP, A2A, firewalls, cache, observability — on a Kubernetes of your choice.