Cerver vs Open-Kritt

Cerver and Open-Kritt are both agent frameworks tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Cerver

Cerver is session infrastructure for AI agent fleets: each session carries its full transcript, cost record, model choice, and compute target as a single object you control. You write routing policies — or let auto-routing handle it — so routine tasks go to cheaper models and complex work earns the frontier. Mid-session you can swap the underlying model or compute without losing the transcript. The local relay option means sessions that need your repo or CLI attach to your machine and run on Claude Max or ChatGPT subscriptions you already pay for, which drops marginal token cost close to zero. Spending caps ship on by default, so a runaway parallel agent fleet stops at your number.

Open-Kritt

The tool runs parallel AI agents across a codebase, so vulnerability discovery that would serialize into hours on a single-context scan distributes across concurrent analysis threads. It targets security researchers and bug bounty teams who need to sweep repositories at scale, not review a function at a time. Self-hosting is supported under AGPL-3.0, which means your code and findings never leave your infrastructure — a requirement for any org with compliance constraints. The open-source core is inspectable and forkable, but managed scans are a paid-only feature, so teams that want the hosted workflow face a significant spend threshold. The page describes GitHub integration as a first-class path, making it a practical fit for teams already running security workflows inside existing CI infrastructure.

Attribute	Cerver	Open-Kritt
Pricing	Paid	Paid
Price	$89/mo + $10/dev, max $300/mo	—
Free trial	No	No
Open source	No	Yes
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	—	Local, GitHub, self-hosted
Released	—	2026-07
Pros	Routing policies direct routine tasks to cheaper models automatically, so teams that previously ran every session on a frontier model by default can cut token spend without manually triaging each request. Spending caps are on by default for every account, which means a parallelized agent fleet that goes wrong stops at a number you set — not at an invoice that arrives later. Transcript persistence across model and compute swaps means switching from a hosted model to a local machine mid-session does not restart context, so experiments and recovery from compute failures do not lose work. Local relay sessions run on Claude Max or ChatGPT subscriptions already in place, so teams with existing paid subscriptions offload token costs entirely for local compute workloads. The side-by-side agent comparison runs inside one session and surfaces a real output diff, so choosing between two models or runtimes is based on actual task results rather than benchmark averages.	Parallel agent analysis across large codebases, so security researchers are not bottlenecked by single-context limits that cause coverage gaps on repositories too large for one model pass. AGPL-3.0 open-source license with self-hosting support, which means organizations with compliance requirements can audit the tool's behavior and keep all code and findings on their own infrastructure rather than routing through a third-party service. Direct GitHub repository integration, so teams can point the tool at existing repos without building a separate code ingestion or preprocessing step. Support for Codex and Claude Code model backends, so teams can align the analysis engine with the model their organization already has access to or trusts for security-sensitive tasks. Inspectable agent orchestration code under an open license, which means a security team can verify exactly what the agents are executing — a requirement that opaque SaaS tools cannot satisfy.
Cons	Cerver does not provide a workflow builder or pipeline canvas — teams that need to define multi-step agent logic with branching based on prior step output have no native way to express that inside the platform. They build the branching logic externally and use Cerver only for session management, which means maintaining two systems from the start. The supported harness list — Claude Code, Codex CLI, OpenAI SDK, xAI — is fixed by the vendor. Teams running agents on frameworks outside that set, such as LangChain or custom tool chains, will find no documented integration path. At that point the platform's session tracking provides no value, and those teams move to infrastructure that supports their stack. The session-focused model means observability is scoped to what happens inside a Cerver-managed session. Teams that need tracing, evals, or logging that spans systems outside those sessions — for example, database calls, external APIs, or queue workers — get no visibility from Cerver and must instrument those layers separately.	Managed scans are a paid-only feature with a spend threshold the validator context confirms is substantial; independent researchers and small bug bounty teams operating on limited budgets hit this wall immediately and are forced to self-host, which shifts the burden of infrastructure provisioning, scaling, and maintenance entirely onto the team. Self-hosting the agent infrastructure requires operational capacity that security research teams — typically focused on findings, not DevOps — often lack; teams without a dedicated infrastructure engineer end up spending sprint time on setup and uptime instead of auditing, and those teams frequently abandon self-hosted options for managed security tooling that absorbs that operational cost. No API is available per the tool's current documentation, which means teams that want to embed Kritt.ai's analysis into an existing CI/CD pipeline or trigger scans programmatically from another system face a hard integration ceiling; teams requiring API-driven automation switch to tools with exposed endpoints.

Bottom line

Open-Kritt is open source; only Cerver exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Cerver and Open-Kritt?

Cerver is Paid, while Open-Kritt is Paid and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Cerver better than Open-Kritt?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.