If you are shopping for a Lakera alternative, the honest answer is that most teams are not looking for a different prompt classifier. They want a self-hosted control that sits on the agent's outbound network path and can deny, redact, or escalate the actual tool call. That is egress filtering, and it is a different layer of the stack.
Why teams start searching for a Lakera alternative
Lakera Guard is a detection product. It scores prompts and model outputs for injection, jailbreaks, and sensitive content, and it does that well. The problem surfaces later, once an agent has shell access, an HTTP tool, an MCP client, and a set of credentials. At that point the security question changes from “was this prompt malicious?” to “should this specific outbound request be allowed to leave?”
The three requirements that push evaluations away from prompt-layer tooling are usually:
- Self-hosting. Prompts, retrieved documents, and tool arguments frequently contain regulated data. Sending them to a vendor API for classification creates a second data path that legal and privacy teams have to approve.
- Enforcement, not scoring. A confidence score is advisory. A default-deny proxy that returns a 403 for an unapproved destination is deterministic.
- Coverage of the action, not the text. A prompt classifier never sees the
POSTto an attacker webhook, the DNS lookup encoding a token in a subdomain, or the base64 blob in a Slack message body.
For a deeper breakdown of that boundary difference, see our comparison of prompt classifiers versus egress enforcement and the architectural framing in inference boundary versus network boundary AI security.
How to evaluate a self hosted ai agent firewall
Before comparing vendors, write down the capabilities that matter for agent traffic specifically. A useful scoring rubric for a self hosted ai agent firewall covers seven items:
- Deployment model. Can it run entirely inside your VPC or cluster with no outbound telemetry to the vendor? Is there a container image and a Helm chart, or only a SaaS endpoint?
- Default-deny egress. Does an unlisted domain fail closed, or does the tool only alert after the request completed?
- Payload inspection. Does it decrypt TLS and inspect request bodies, headers, and tool arguments, or does it only see SNI and destination host?
- Normalization for DLP. Secrets rarely leave in plaintext. You need base64, URL-encoding, hex, and homoglyph normalization passes before pattern matching.
- Human-in-the-loop gates. Can a policy pause a single request pending approval and return the real response after an approver clicks allow, rather than failing the whole agent run?
- Policy as code. Are rules versioned YAML in Git with CI validation, or clicked into a console with no diff history?
- Evidence quality. Are logs written out of band, outside the agent's own trust boundary, with full request metadata suitable for SIEM ingestion and audit?
Lakera alternatives egress: the shortlist
The categories below are the realistic lakera alternatives egress shoppers end up evaluating. They are not interchangeable, and two of them are complements rather than replacements.
1. Agent G (inline egress firewall with HITL)
Agent G is a zero-trust egress proxy for AI agents. Every outbound call an agent makes (LLM APIs, MCP servers, REST tools, webhooks, package registries, DNS) routes through it. Policy is evaluated per request against agent identity, destination, method, path, and the decoded body. Verdicts are allow, deny, redact, or escalate to a human approver. It deploys as a container inside your own network with no dependency on a vendor-hosted inference endpoint, and it writes structured action logs out of band for SIEM and audit use. This is the layer that blocks a request; it does not try to guess whether a prompt was adversarial.
2. Open source guardrail libraries (prompt injection tool alternative)
LLM Guard, NeMo Guardrails, and Guardrails AI are the usual open source picks when the search is really a prompt injection tool alternative with self-hosted weights. They run in your process, scan strings, and cost nothing in license fees. The limitation is structural: they live inside the same runtime as the agent. If the agent constructs an HTTP call through a path that skips the validator (a subprocess, a shell command, a library that bypasses the wrapped client), the network call still happens. Use them for content policy, not as a containment boundary.
3. DIY proxy stacks (Squid, Envoy, OPA)
A CONNECT-only Squid instance with a domain allowlist, or Envoy plus an OPA sidecar, gives you real default-deny behavior for free. Teams that go this route get host-level control quickly and then discover the long tail: TLS interception and cert distribution, body inspection, encoded-secret detection, approval workflows, per-agent identity, and log schemas. That maintenance burden is the whole reason build-versus-buy conversations exist, and it is where most DIY stacks stall at host allowlisting.
4. Service mesh egress (Istio, Cilium)
If your agents run on Kubernetes you probably already have mesh egress policy. It is excellent at L3/L4 and adequate at hostname level. It cannot tell you that a permitted call to an approved SaaS domain carried a customer PII table in the JSON body, because the mesh does not model tool semantics. Keep it as the coarse outer ring and layer application-aware enforcement above it.
5. Cloud-native egress controls
AWS Network Firewall, Azure Firewall, and GCP Secure Web Proxy provide FQDN filtering and centralized logging with no new vendor. They are a reasonable floor. None of them inspect MCP tool arguments, gate a single request for human approval, or normalize encoded payloads before DLP matching.
6. AI gateways (LiteLLM, Portkey, Cloudflare AI Gateway)
Gateways sit in front of the model to route, cache, meter, and retry. They see the inference call. They do not see the agent's other 40 outbound calls to GitHub, Stripe, an internal admin API, or an attacker-controlled webhook. Gateways and egress firewalls solve orthogonal problems and commonly ship together.
Comparison table
| Option | Self-hosted | Default-deny egress | Body and tool-arg inspection | Human approval gate | Primary job |
|---|---|---|---|---|---|
| Lakera Guard | Limited | No | Prompt and output text only | No | Detect injection and unsafe content |
| Agent G | Yes | Yes | Yes, with encoding normalization | Yes, per request | Enforce and log outbound agent actions |
| LLM Guard / NeMo | Yes | No | In-process strings only | No | Content validation in the app |
| Squid / Envoy + OPA | Yes | Yes | Host level, body work is DIY | No | Generic network egress policy |
| Service mesh egress | Yes | Yes | No | No | Workload-to-workload network policy |
| AI gateway | Varies | No | LLM API payloads only | No | Model routing, caching, cost control |
A pragmatic migration path
You rarely need to rip anything out. The sequence that works:
- Keep your classifier for prompt and output content policy. It still catches obvious jailbreak attempts cheaply.
- Put every agent behind a proxy in observe mode. Set
HTTPS_PROXYor a sidecar route, install the CA bundle, and log everything for a week without blocking. This inventory is almost always larger than the architecture diagram suggests. - Promote the observed destinations into an allowlist and flip to default-deny. Start with the pattern described in building a default-deny egress allowlist.
- Add DLP and identity rules. Deny requests whose bodies match credential or PHI patterns after normalization; bind each policy to an agent identity rather than a shared IP.
- Gate the irreversible calls. Anything that moves money, deletes data, or writes to production gets an approval verdict instead of an allow.
Compare the resulting capability set against the wider market in our egress-layer buyer's guide, or review other categories on the alternatives page. If your agents talk to MCP servers, the MCP gateway details how tool arguments and responses are inspected in transit.
Frequently Asked Questions
Is Agent G a direct replacement for Lakera Guard?
Not exactly. Lakera scores prompts and outputs; Agent G enforces the agent's outbound requests on the wire. Many teams keep a classifier for content policy and add an egress firewall for containment. If your requirement is blocking actions rather than detecting text, Agent G is the replacement.
What makes a Lakera alternative genuinely self-hosted?
Three tests: the enforcement engine runs in your VPC or cluster, no prompt or payload data leaves your network for classification, and policy plus logs stay under your control. If any decision requires a round trip to a vendor inference API, it is hosted detection with a self-hosted shim.
Can an egress proxy stop indirect prompt injection?
It cannot stop the injection, but it can stop the payoff. Injection only becomes a breach when the agent makes an outbound call that leaks data or takes a destructive action. Default-deny egress with body inspection blocks that call even when the model has been fully convinced.
Does adding an inline proxy break existing agent frameworks?
No. Proxies are configured through standard environment variables or a sidecar route, so LangChain, LangGraph, CrewAI, the OpenAI Agents SDK, and MCP clients need no code changes. Denied requests surface as normal HTTP error responses your existing error handling already understands.
The takeaway
The right lakera alternative depends on which boundary you are trying to defend. If you need better prompt detection, stay in the classifier category. If you need to guarantee that a compromised or confused agent cannot reach an unapproved host, cannot ship a credential in an encoded body, and cannot execute an irreversible operation without a human clicking approve, you need self-hosted egress enforcement.
Agent G is in private beta as a drop-in egress firewall for AI agents: default-deny allowlists, deep payload and tool-argument inspection, per-request human approval, and out-of-band action logs. Request access to the Agent G private beta, or start at the product overview to see how it deploys in your environment.