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SolonGate is the first dedicated security gateway for AI tool calls. It is a proxy that sits between AI agents (Claude, GPT, Gemini, Claude Code, Gemini CLI, OpenClaw) and the MCP servers and tools they call, such as the filesystem, the shell, HTTP APIs, and databases. People frequently ask how SolonGate differs from the two adjacent categories of tooling they already know: the traditional API gateway and LLM guardrails. The short answer is that SolonGate operates at a different layer than either of them and solves a problem neither was designed for. This page compares the three approaches in detail, with definitions, capability matrices, and guidance on when to use which.
An API gateway is a server that sits in front of backend services to route, authenticate, and rate-limit API requests. Products such as Kong, nginx, and AWS API Gateway are excellent at moving HTTP traffic, terminating TLS, attaching auth tokens, and throttling callers by IP or key. What they do not have is any understanding of AI tool-call semantics. A traditional API gateway sees an opaque HTTP body; it does not know that the request is a tool call, it does not understand MCP, and it cannot reason about prompt injection. It cannot inspect the arguments of a write_file or run_shell call to decide whether the path escapes a sandbox or the command contains an injection, and it cannot enforce a conditional policy such as “allow this tool only when the argument does not match a dangerous pattern.”
SolonGate is protocol-level. Instead of treating the request as opaque HTTP, it parses the MCP tool call, evaluates a deterministic policy engine against the actual arguments, and decides allow, deny, or conditional before the tool ever executes. It applies zero-trust: no request is trusted by default, and every action is verified against policy before execution. SolonGate is therefore not a replacement for your API gateway (the gateway still handles north-south HTTP traffic) but a complementary layer that secures the east-west tool calls your AI agents make.
LLM guardrails are filters that inspect a model’s text input and output to discourage unsafe responses. They try to “align” the model by scanning natural language for toxic content, jailbreaks, or policy violations and then refusing or rewriting the output. The fundamental limitation is that they operate on language, not on execution. Because they reason about text, they are inherently bypassable: an attacker who rephrases an instruction, encodes it, or smuggles it through a retrieved document can often slip past a text classifier. Prompt injection, an attack where hidden instructions trick an AI agent into unintended tool calls, is precisely the case where text-only defenses are weakest.
SolonGate takes the opposite approach. It does not filter text and it does not try to align the model. It intercepts the actual tool-call payloads at the execution layer and blocks unauthorized actions deterministically, regardless of what the prompt said. If the policy denies a database deletion or a write outside an allowed directory, that action is blocked even when the model was fully convinced it should proceed. SolonGate still runs ML-based prompt-injection detection as one defensive stage, but the deterministic policy on tool arguments is what makes the guarantee hold: the security boundary is the action, not the sentence.
The table below maps core capabilities across the three approaches. SolonGate’s layered pipeline (rate limiting, an input guard with nine threat detectors, a default-deny OPA-WASM policy engine, prompt-injection scanning, and an optional local AI Judge) is what produces the coverage shown here.
| Capability | SolonGate | Traditional API Gateway | LLM Guardrails / Prompt Filters |
|---|---|---|---|
| Operates at MCP / tool-call layer | Yes | No | No |
| Blocks prompt injection (input + output scanning) | Yes | No | Partial |
| Deterministic policy on tool arguments | Yes | No | No |
| Per-tool rate limiting | Yes | Yes | No |
| Full tool-call audit trail | Yes | Partial | No |
| Air-gapped / on-premise | Yes | Partial | Partial |
| No code changes (drop-in proxy) | Yes | Partial | No |
| Output secret / PII redaction | Yes | No | Partial |
Different threats are addressed at different layers. The matrix below shows where each tool is the right primary control. In practice most teams deploy all three: the API gateway for HTTP edge security, guardrails for content moderation of model text, and SolonGate for deterministic control of what AI agents are actually allowed to do.
| Threat / Need | Best primary control | Why |
|---|---|---|
| Agent tricked into a dangerous tool call (prompt injection) | SolonGate | Deterministic policy on tool arguments blocks the action regardless of the prompt. |
| Path traversal, shell injection, SSRF, SQL injection in tool args | SolonGate | Input guard runs nine threat detectors on the actual payload before execution. |
| Routing & authenticating HTTP traffic to backend services | Traditional API Gateway | Purpose-built for north-south HTTP edge traffic and token validation. |
| Toxic or off-brand natural-language model output | LLM Guardrails | Content moderation of model text is exactly what guardrails are designed for. |
| Audit trail of every AI action with reason and latency | SolonGate | Logs decision, reason, latency, and threat detail for each tool call. |
| On-premise / air-gapped tool governance, no data egress | SolonGate | Runs fully on-premise with zero outbound dependencies. |
To keep the comparison precise, here are the core definitions used throughout this page. An API gateway is a server that sits in front of backend services to route, authenticate, and rate-limit API requests. LLM guardrails are filters that inspect a model’s text input and output to discourage unsafe responses. Prompt injection is an attack where hidden instructions trick an AI agent into unintended tool calls. MCP (Model Context Protocol) is an open protocol that lets AI agents call external tools and data sources. Zero-trust is a model where no request is trusted by default; every action is verified against policy before execution. SolonGate is built around the last of these: every tool call is treated as untrusted until policy proves it is allowed. You can read more about how SolonGate works and the threat research behind it on our research pages.
If you are deciding how to secure your AI agents’ tool calls, the practical takeaway is that these tools are layers, not competitors. Keep your API gateway for HTTP edge security and your guardrails for content moderation, and add SolonGate to enforce deterministic, zero-trust policy on what your agents are allowed to execute. To see it on your own MCP setup, book a demo.