Netra Reviews & Product Details

Netra is an end-to-end AI observability, evaluation, simulation and security testing platform that gives engineering teams complete visibility into every decision their AI agents make, from development through production. It is purpose-built for multi-step, multi-agent, and multi-tool workflows where traditional APM and LLM monitoring tools fall short. The platform is organized around four core capabilities. Observability delivers full-fidelity tracing across every LLM call, tool invocation, reasoning step, and retrieval, with real-time cost, latency, and error tracking. Evaluation enables teams to score agent quality automatically on every decision using built-in rubrics, custom LLM-as-judge evaluators, and code evaluators, with online evals running continuously on live traffic. Simulation lets teams stress-test agents against thousands of real and synthetic scenarios before production, using diverse personas and A/B comparisons against a baseline. Prompt Management provides a centralized workspace where every prompt is versioned, lineage-tracked, and rollback-safe, with every production response traceable back to its exact prompt version. Netra is built on OpenTelemetry, making it compatible with any OTLP-compliant backend and ensuring teams can get started with just 2 to 3 lines of code. It integrates with 14+ LLM providers including OpenAI, Anthropic, Google Gemini, and AWS Bedrock, and 12+ AI frameworks including LangChain, LangGraph, CrewAI, and LlamaIndex. The platform is SOC2 Type II certified and compliant with GDPR and HIPAA, with strict US and EU data residency and zero cross-region data sharing. Enterprise teams get on-premise deployment, isolated databases, and SSO. Available on a Free plan with no credit card required, a Pro plan at $39 per month, and custom Enterprise pricing.

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KeyValue Software Systems

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Netra AI Systems Inc.

Pricing

Pricing provided by Netra.

Free

Free Trial

Pro

$39.00

Per Month

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Netra Integrations

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Integration information sourced from real user reviews.

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GU

George U.

Co-Founder

Small-Business (50 or fewer emp.)

Updated 9/23/2026

Originally submitted 9/22/2026

"Our Experience Testing AI Agents with Netra"

5/5

What do you like best about Netra?

What I like most about Netra is that it helps me understand why an AI agent failed. I can follow the full trace to see the model calls and tool decisions behind an answer, instead of guessing from the final response. The interface makes those results easy to follow, and having text, voice, and image evaluations in one place is useful when working across different AI applications.

The integrations are another strength. The SDKs make it possible to bring evaluations into a development workflow, run tests after changes, and go straight from a failed test to its trace. I also like seeing pass rates alongside latency, duration, and cost. An agent may give the right answer, but response time matters just as much in a voice interaction.

Onboarding was straightforward, and the support was helpful when working out how to set up meaningful evaluations. The free plan gives teams room to try the platform, while the paid plans scale with usage. I would still keep an eye on span volume as testing grows. For me, the value is having the evidence to investigate failures and track performance in one place, with less time spent pulling results together from separate tools. Review collected by and hosted on G2.com.

What do you dislike about Netra?

You really need a vendor demo to understand the product’s full capabilities. There are not many public forums where you can find discussions around the platform. Review collected by and hosted on G2.com.

What problems is Netra solving and how is that benefiting you?

Netra solves a problem we were running into with AI agent testing. You often end up using different tools for functional testing, security, and testing voice, text, or image interactions. Netra brings all of this into one place.

What I find useful is that it goes beyond checking whether an agent gave the right answer. It helps uncover reasoning failures, hallucinations, prompt injection risks, unsafe responses, and issues with multimodal interactions. When something fails, we also get better visibility into where things went wrong.

That saves time during testing and makes debugging easier. More importantly, it helps uncover problems that could easily be missed when different teams are testing different parts of the agent separately. It gives us much more confidence before moving an AI agent into production. Review collected by and hosted on G2.com.

Current UserValidated ReviewerSource: Organic

PS

Prasen S.

Co-Founder & CEO

Small-Business (50 or fewer emp.)

10/1/2026

"Clear Prompt & Tool Visibility with Powerful Agent Benchmarking—and Great Support"

4.5/5

What do you like best about Netra?

Netra brings user-level prompts, agent-level prompts, and tool calls into a single, clear view. Previously, we had to piece all of this together from logs spread across AWS, our other infrastructure, and OpenRouter. What stood out most to the team is the evaluation Netra runs on top of those logs. We can now benchmark our agents properly, which would have been very difficult to build ourselves.

The team has also been a huge support throughout. They’re always approachable, and they act on feedback quickly. We needed an on-prem deployment because our security data is so sensitive, and Netra was able to support that. Review collected by and hosted on G2.com.

What do you dislike about Netra?

At first, the traces were a bit difficult to follow. Netra also didn’t support multi-agent setups at the time, which was important for our use case. We brought both issues to the team, and within about a week they reworked the trace structure to better reflect agent-level changes and tool calls, and they also added multi-agent support. Review collected by and hosted on G2.com.

What problems is Netra solving and how is that benefiting you?

At Axari, we build digital twins that help CISOs manage their organization’s security. As our agents became more complex, observability and benchmarking turned into must-haves, and trying to do it ourselves was painful. Engineers had to dig through logs spread across AWS and OpenRouter, manually match model calls, and roll their own logging just to understand what happened in a run.

Netra gives us a single place to trace and evaluate every agent run, which makes it much easier to benchmark, iterate, and improve our agents quickly.

Our data is highly sensitive. It can include employee data, payment data, and an organization’s attack data and trace maps. Running Netra on-prem lets us analyze all of our agent logs without exposing any of that. Anyone building agents needs a tool like this, whether it’s a chatbot or a full multi-agent system, because no one fully knows what an agent can do, or how attackers might exploit it. Review collected by and hosted on G2.com.

Validated ReviewerSource: Organic