Velociraptor vs GRR: which fits an incident response team setting up endpoint forensics today
For an incident response team that must collect and hunt on endpoints now, Velociraptor is usually the faster path to a working deployment, because its server is a single Go executable that stores data in files and needs no separate database. GRR is the better fit when your team already runs Python infrastructure, wants a Google-built framework under Apache-2.0, or plans to adopt its Rust agent. Either way, the decision should be made against your own constraints, not a generic ranking.
Choose Velociraptor when you want one binary to run the server, a web GUI and a local triage mode with velociraptor gui, when analysts will write or modify VQL artifacts in the GUI without rebuilding clients, and when you need offline collectors for hosts where a client cannot be installed. Its license is AGPL-3.0, so confirm with counsel what that means if you modify the server and make it reachable over a network. Velociraptor 0.77.3 was released on October 5, 2026 with a request to update for multiple CVEs, so confirm your deployment is on that release or later before go-live.
Choose GRR when your team is comfortable with Python, when Apache-2.0 terms fit your legal review more easily, or when you want to test the Rust agent (RRG) alongside the Python agent. Its recommended install is Docker Compose, which makes a lab build quick to stand up. Before you commit, decide three things: your license obligations under AGPL-3.0 versus Apache-2.0, whether your team's skills lean toward Python or Go, and whether you have custom integrations. GRR 4.0.0.0 removed the legacy UI and the v1 HTTP API, so any existing integration must move to the v2 API and its new JSON format. If you need offline collection, check which GRR capabilities cover that case with the GRR documentation before you assume parity.
Which platform fits each incident response scenario
This table maps common incident response scenarios to the platform that fits each one better, with the condition that would reverse the choice. Read each row against your own team's skills, hosts and licensing constraints before shortlisting.
| Scenario | Better fit | Reason and condition that favors the other platform |
|---|---|---|
| Small team standing up forensics quickly with little build time | Velociraptor | Velociraptor ships as a single statically compiled Go executable with no separate database, so the server is mostly files on disk. GRR is better if your team already runs its Docker Compose deployment and prefers Google's recommended install path. |
| Hosts where a client cannot be installed | Velociraptor | Offline collectors built from the GUI have all the capabilities of a normal client and can run any artifact, writing results to JSONL or CSV. Choose GRR only if you can install its agent on every target, since the sources supplied do not describe an equivalent offline mode for GRR. |
| Existing Python tooling and custom API integrations | GRR Rapid Response | GRR is a Python server and client, and its v2 API is protocol buffer based with a different JSON format. Choose GRR if your team can absorb the 4.0.0.0 migration, because the legacy v1 API and UI were removed. Velociraptor is the better choice if you would rather write VQL artifacts than maintain API routers. |
| Team that must avoid AGPL-3.0 obligations | GRR Rapid Response | GRR is Apache License 2.0, while Velociraptor is AGPL-3.0. Confirm with counsel whether your use of a modified Velociraptor server triggers source disclosure duties before deciding. If AGPL is acceptable, Velociraptor remains a strong option. |
| Team already running osquery and wanting live forensics on top | Velociraptor | Velociraptor's documentation credits osquery as one of its inspirations, and its osquery artifact lets you run osquery alongside VQL collection. GRR can also run osquery, but the osquery binary must already be on each client and its path set in Osquery.path, with clients repacked and redeployed after the change. |
| Team that wants Google-maintained code | GRR Rapid Response | GRR is developed by Google in the google/grr repository under Apache 2.0. Velociraptor was acquired by Rapid7 in April 2021, which said it would keep building the open-source community. Choose Velociraptor if you value its single-binary model and are comfortable with a vendor-backed open-source project. |
How each platform is built and what that means for operations
Velociraptor and GRR take different routes to the same goal: remote live forensics across endpoints. The differences in server packaging, storage, agents and API stability decide how much work your team carries after the first deployment. Read each card as a question to put to both projects before you commit.
Single binary with file-based storage
Velociraptor ships as one statically compiled Go executable, and its server keeps data in files on the local filesystem rather than in a separate database. For an incident response team, this means fewer moving parts to back up, patch and restore. The trade-off is that you own file layout, disk capacity and backup schedules directly, so confirm how your team will snapshot and recover the data directory and whether a distributed file system such as Amazon EFS fits your recovery design.
Client, frontend, GUI and gRPC API
A Velociraptor deployment has a client on each endpoint, a frontend that talks to those clients, a web GUI for administrators and a gRPC API. The server is typically deployed on a cloud VM and is only fully supported on Linux. If your analysts work from Windows or macOS workstations, the GUI is still reachable through the browser, but the server host itself must be Linux. Confirm the server OS, the frontend exposure model and how your automation will call the gRPC API before you design network paths.
GRR Python server and client
GRR pairs a Python client installed on target systems with Python server infrastructure that manages and talks to those clients. Operationally, your team needs Python server skills, a supported Python deployment path and familiarity with GRR's own release cadence. The project recommends Docker Compose for installation, which reduces setup variance. Check that your platform team can maintain the Python stack, including dependency updates, and that the client templates you repack match the operating systems in your fleet.
RRG Rust agent alongside Python
GRR 4.0.0.0 added server-side support for RRG, a new GRR agent written in Rust. Both the Python and Rust agents are supported, and actions are scheduled on either depending on availability and supported features. This means a mixed fleet is a working state, not only a migration step, but it also means some actions may run on one agent and not the other. Before rollout, ask GRR which actions each agent supports on your operating systems and test your key collection flows on both.
v2 API and removed legacy UI
GRR 4.0.0.0 completely removed the legacy UI and the legacy v1 HTTP API in favor of the v2 API, which is built on protocol buffers and uses a different JSON format. Any custom integration, router or script written against v1 must be updated before upgrading. Plan this as engineering work with its own test cycle, and check whether your SOAR or case management tooling depends on the old endpoints. Budget time for regression testing on the new API before you move production traffic.
Parsing pushed to the endpoint
Velociraptor's own history page says it is easier to deploy than GRR because it pushes more parsing and analysis to the endpoint. In practice, the server handles less processing per result, which can reduce load on a single server as your fleet grows. This is the vendor's design claim, so test it in your environment with your fleet size, collection types and expected result volumes before you rely on it for capacity planning.
Feature by feature: what each platform supports today
This table compares Velociraptor and GRR Rapid Response on license, language, deployment, storage, collection, hunting, osquery, API style, releases, community channels and installation methods. Read each row as a fact about the current release, and treat any cell that says to confirm as an open question for your own evaluation.
| Capability | Velociraptor | GRR Rapid Response |
|---|---|---|
| License | AGPL-3.0 | Apache License 2.0 |
| Primary implementation language | Go, shipped as a single statically compiled executable | Python server with Python agent |
| Agent language | Velociraptor client built from the same Go binary | Python agent plus new Rust agent (RRG), both supported |
| Server deployment model | Typically a cloud VM, fully supported on Linux only | Recommended via Docker Compose, with image, pip and source options |
| Data storage | Files on the server filesystem, no separate database | Server-side storage per the GRR documentation; confirm backend details |
| Offline collection | Offline collectors built from the GUI, can run any artifact, output JSONL or CSV | Not confirmed in the supplied facts; confirm with the GRR documentation |
| Hunting across an environment | Supported, described as hunting across an environment in the documentation | Remote live forensics across hosts; confirm hunt workflow in current docs |
| osquery integration | Velociraptor documents an osquery artifact approach and migration guidance | Osquery collector flow; osquery binary must be on the client and set in Osquery.path |
| API style | gRPC API | v2 API based on protocol buffers with JSON; legacy v1 HTTP API removed in 4.0.0.0 |
| Latest release cited | 0.77.3, released October 5, 2026 | 4.0.0.0, released December 15, 2025 |
| Community channels | GitHub repository and project documentation | GitHub issues and the grr-users mailing list |
| Documented installation methods | Single binary, server on cloud VM, GUI local triage mode via velociraptor gui | Docker Compose (recommended), GRR Docker image, pip packages, source |
| Identity integration (SSO, SAML, OIDC) | Not confirmed in supplied facts; confirm in the Velociraptor documentation and your proxy setup | Not confirmed in supplied facts; confirm in the GRR documentation and your proxy setup |
Licensing and editions: what AGPL-3.0 and Apache-2.0 each ask of your team
Velociraptor is licensed under the GNU Affero General Public License version 3 (AGPL-3.0), according to the LICENSE file in the Velocidex/velociraptor GitHub repository. Rapid7 announced on April 21, 2021 that it had acquired the project, and said it would continue to build the Velociraptor community and remained committed to supporting its open-source community. The supplied facts do not name a paid Velociraptor edition, so do not assume a commercial tier exists. Ask Rapid7 directly whether a commercial license, support contract or enterprise build is offered, and get the answer in writing before you plan a budget around it.
- License terms: AGPL-3.0 is a strong copyleft license. Changes you distribute must be released under the same terms.
- Network use clause: The AGPL adds obligations when you modify the software and let users interact with it over a network, which is the main difference from GPL-only licenses.
- Vendor ownership: Rapid7 owns the project following the 2021 acquisition, so confirm how its roadmap and community governance are handled.
- Paid options: No paid edition is named in the supplied facts. Confirm with Rapid7 whether one exists and what it adds.
- Internal use: Running Velociraptor inside your own organization is the common pattern, but whether your use triggers any distribution or network duty depends on what you change and how you expose it.
Before you modify Velociraptor artifacts or server code, have your counsel confirm how AGPL-3.0 applies to your planned use.
GRR Rapid Response is open source under the Apache License 2.0 and is developed on GitHub in the google/grr repository. Apache-2.0 is a permissive license. It allows you to use, modify and redistribute the software, including in proprietary products, provided you keep the license and notice requirements. Because the code is developed in a public repository, you can read the source, track changes and pin a version that your team has reviewed. The supplied facts do not describe a paid GRR edition, so confirm with the project maintainers or your own sources whether any commercial support or add-on exists.
- License terms: Apache-2.0 is permissive, with no network-use clause comparable to the AGPL.
- Notice requirements: Keep the license and notice files when you redistribute GRR or derived builds.
- Source location: The project lives in the google/grr repository on GitHub, which is where you should track releases and changes.
- Patent terms: Apache-2.0 includes an express patent grant. Have counsel review it if your organization has specific patent policies.
- Community support: Community channels are GitHub issues and the grr-users mailing list, so plan internal ownership for fixes that you cannot get from those channels.
Review the license text and notice obligations with your counsel before you repackage GRR clients for your fleet.
The license choice affects how your team modifies, distributes and hosts the tool, more than how you run it day to day. AGPL-3.0 obligations matter most when you change Velociraptor and then offer the modified version to users over a network, such as a hosted forensics service you run for other companies or business units. Internal use of unmodified software generally carries fewer obligations, but the boundaries are a legal question. Apache-2.0 obligations are lighter, centered on attribution and notices. Neither license covers the cost of running the software, which is your team's own work.
- Modifications: AGPL-3.0 applies more conditions when you change the code and make it available over a network.
- Distribution: Repackaging clients or server builds for third parties triggers notice and source obligations under both licenses.
- Service delivery: If a managed provider runs either tool for clients, review each license's terms on hosting for others.
- Internal artifacts: Custom artifacts written in VQL are configuration you author, but confirm with counsel how they are treated when shared outside your organization.
- Counsel review: This is general information, not legal advice. Have your counsel review the specific license text against your planned use.
Document your intended modifications and distribution model before deployment so counsel can review them against the exact license text.
What drives the running cost of Velociraptor and GRR
The running cost of either platform is mostly people and hosting, not licenses, because both are free to use under open-source terms: Velociraptor under AGPL-3.0 and GRR under Apache License 2.0. The one-time costs are the initial build: standing up the server, packaging and rolling out clients, and writing or adapting the artifacts or flows your incident response team needs. The recurring costs are the server infrastructure, patching, client repacking and redeployment, and the time an analyst or engineer spends keeping collection working. For Velociraptor, the server is typically a cloud VM that stores data in files on its filesystem, so hosting spend scales with endpoint count, retention and collection volume. For GRR, the Python server infrastructure is the larger footprint to plan for, and the Rust agent rollout adds a second agent path to test.
Skills drive effort on both sides. Velociraptor work centers on VQL and artifact authoring in YAML, which a Go developer does not need to write but an analyst does need to read and modify. GRR work centers on Python and the GRR API. The bigger one-time cost for existing GRR shops is the move from the legacy v1 HTTP API to the v2 API, which the GRR 4.0.0.0 release notes describe as protocol buffer based with a different JSON format. Any custom API integrations, routers and scripts must be inventoried and rewritten before the upgrade, and that rewrite is usually the most expensive single item. Ask who will own that work and whether it is in scope. For Velociraptor, the recurring cost of patch discipline matters: the project asked users to update to 0.77.3 to address multiple CVEs, so budget for a standing process to track releases, test them and roll them out to the server and clients.
DeployOpen does not publish prices. A quote follows from a written scope, and most engagements are fixed-scope, with ongoing support, upgrades and continuous testing available as retainers. When you request a quote, ask for the agent scope (which operating systems, whether offline collectors are included, and whether GRR runs both agents), the number of endpoints and the expected growth, the hosting model and who operates the infrastructure, the upgrade and testing responsibilities for each release, the handling of custom GRR API migration or artifact authoring, and whether a retainer covers CVE response windows. Confirm each of these with the vendor or your own environment before comparing totals, since the answers change the one-time and recurring split more than the license terms do.
Moving between Velociraptor and GRR: what transfers and what gets rebuilt
This sequence shows a team which parts of its endpoint forensics setup move across, which parts must be rebuilt, and how to confirm each stage before the old server is retired.
Inventory existing collection workflows and integrations
The IR lead and the platform owner list every collection workflow, scheduled flow, hunt, report and custom API integration on the current platform, noting which ones call the GRR v1 HTTP API or depend on the legacy UI. Done means each item is tagged as transfer, rebuild or retire, and the list is signed off by the people who run the integrations. Items with no owner are flagged before any work starts.
Confirm which integrations need GRR 4.0 changes
If the team is moving to or running GRR, the integration owner checks each custom client against the GRR 4.0.0.0 release notes, which removed the legacy UI and the v1 HTTP API in favor of the v2 API with a different JSON format. Done means every v1 integration has a named owner and a rewrite ticket, and the team has confirmed the v2 mapping in a test environment.
Map artifacts and collection content across platforms
The IR engineers compare each existing artifact or collection flow with the target platform's equivalent, since VQL artifacts and GRR flows are not interchangeable in general. Treat every mapping as a hypothesis to verify against the target documentation, and record for each one whether it is ported, rewritten or dropped. Done means a mapping sheet exists and each rewritten item has been run once on a test host.
Run both platforms in parallel on a pilot group
The platform owner deploys the new server alongside the existing one and enrolls a small, defined group of endpoints in both, so that the same investigative questions can be answered on each. Results from the two platforms are compared for completeness and timing. Done means the pilot group has produced matching findings for the agreed test cases and any gaps are logged with owners.
Repack and redeploy clients to the pilot group
The endpoint team builds new client packages for the target platform and deploys them to the pilot endpoints, using the vendor's client deployment guides for Windows, macOS or Linux. Clients are confirmed as checking in to the new server and running the ported collections. Done means each pilot endpoint shows a healthy check-in, and the rollback path to the old client is tested on one host.
Expand clients and retire the old server
After the pilot results are accepted, the team moves the remaining endpoints in waves, then stops the old server only after evidence retention is confirmed. The evidence custodian confirms that previously collected data is exported or stored where policy requires, and that legal and audit holds are honored. Done means the retention check is signed off in writing and the old server is decommissioned with its data location recorded.
Implementing Velociraptor or GRR: where incident response projects run long
Velociraptor's build is lighter on infrastructure, but the rollout and evidence work still takes the time. On the Velociraptor side, the server is typically deployed on a cloud VM and is only fully supported on Linux, and it keeps its data in files on the local filesystem rather than in a separate database, so the sizing question is disk growth from collected files and hunt results, plus whether the path is a distributed file system such as Amazon EFS. Confirm the storage volume and retention you need for collected artifacts before you pick an instance size. Offline collectors built from the GUI can run any artifact and write JSONL or CSV, which helps where a client cannot be installed, but each build still has to be packaged, approved and tracked. On the GRR side, the recommended path is Docker Compose, with the Docker image, released pip packages and a source install as alternatives, and client guides exist for Windows, macOS and Linux. Each option needs a decision about who owns the server runtime and its upgrades.
The delays that appear in both projects are mostly outside the software. Agent packaging and endpoint rollout approvals often wait on change control, and a staging group of endpoints that mirrors your real OS mix is the only reliable way to test a client build before a wide push. Custom artifacts in Velociraptor or custom flows in GRR need someone who can write and test them, and that skill is not always on the incident response team. If you plan to run GRR 4.0 or later, the v1 HTTP API and legacy UI are gone, so any custom API integration or router must move to the v2 API, which uses a different JSON format. Evidence handling and chain-of-custody procedures also need to be written down before the first collection, covering who can start a collection, where results are stored and how exports are logged. Confirm with your own team which endpoint operating systems you must cover, whether the network paths from those endpoints reach the server, and whether your approval process allows agents to be deployed through your existing tooling.
Questions buyers ask before choosing or hiring help for endpoint forensics
What does endpoint forensics deployment cost and what drives the price?
Cost depends on the written scope, not on a list price, because DeployOpen does not publish one. The main drivers are the number of endpoints and operating systems to cover, whether you need offline collectors for hosts that cannot take a client, how much server hosting and storage you run yourself, how many custom artifacts or integrations are in scope, and how much staff time your team commits for approvals and evidence handling. Some costs are one-time, such as design, initial deployment and testing. Others recur, such as hosting, upgrades, retained support and periodic testing. Ask any provider for a quote that separates these two groups and lists the assumptions behind each line.
How long does a first Velociraptor or GRR deployment take?
No duration can be promised before scope is known, and DeployOpen agrees scope, deliverables and timelines in writing before work starts. The timeline is set mainly by the number and variety of endpoints, the server hosting model, identity and network approvals, whether custom VQL artifacts or GRR flows must be written, and how quickly your change and evidence-handling processes can sign off. A small pilot on a limited host group usually moves faster than a fleet-wide rollout. Ask for a written plan with milestones and the dependencies your team must clear.
What must our team supply before an engagement starts?
Expect to supply an endpoint inventory with operating systems, locations and business owners, network access paths between endpoints and the server, and a named contact for each approval. Your staff also need to approve which artifacts or collection flows may run, who may start collections, and how collected evidence is stored, hashed, transferred and retained. Without these decisions, an incident response team can deploy the tool but cannot prove chain of custody for what it collected. Confirm these responsibilities in the scope document so they are not assumed.
Can our team deploy Velociraptor or GRR alone?
A small team can usually run a limited pilot alone, especially with Velociraptor, which ships as a single statically compiled Go executable, can run as a local triage tool through the velociraptor gui command, and needs no separate database. The trade-offs appear at scale and under pressure: you own TLS and certificate handling, server patching, upgrades and backup, and you must write and test artifacts yourself. GRR depends on a Python server with its own deployment choices, so the effort is higher. Decide in advance who will answer a production incident at 2 a.m. before choosing a self-run model.
Do the GRR 4.0 API and UI changes affect our existing setup?
Yes, if you have built on GRR before version 4.0.0.0. That release, published December 15, 2025, completely removed the legacy UI and the legacy v1 HTTP API in favor of the v2 API, which is based on protocol buffers and uses a different JSON format. Custom API integrations and routers must be updated, and any scripts, dashboards or runbooks that call the old endpoints will fail. Also note that GRR 4.0.0.0 added server-side support for the Rust RRG agent alongside the Python agent. Inventory your integrations before upgrading and test them on a non-production server first.
Does the AGPL-3.0 license of Velociraptor affect how we use it?
Velociraptor is licensed under AGPL-3.0, while GRR is Apache License 2.0, and the difference matters most if you modify Velociraptor and make it available to others over a network, or if you bundle it into a product you distribute. Internal use for incident response by your own staff is a different situation from offering a modified service to outside users. Whether these conditions apply to your organization, and how they interact with your contracts, is a question for your legal counsel. DeployOpen does not give legal advice on licensing.
Can DeployOpen run either platform for our incident response team?
DeployOpen plans, implements, customizes, integrates, hardens, backs up, upgrades, documents and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks, so both Velociraptor and GRR fall within that kind of work. Most engagements are fixed-scope with agreed deliverables, and ongoing support or upgrades can be arranged as a retainer. DeployOpen does not hold any vendor partner status for these projects unless stated otherwise, and it does not issue audit reports or certifications. Share your environment, forensic evidence requirements and team size to receive a scoped quote.
How DeployOpen helps choose, implement or migrate endpoint forensics
DeployOpen plans, implements, customizes, integrates, hardens, documents and supports self-hosted open-source platforms inside your own infrastructure, so the Velociraptor or GRR decision is tested against your network, identity setup and response runbooks. Engagements are fixed-scope, with the scope, deliverables and timeline agreed in writing before work starts.
How we scope
- Map which endpoints, operating systems and network zones need remote live forensics and offline collection.
- Confirm whether your team needs VQL artifacts and hunts or GRR's Python and Rust agent model.
- Agree in writing which licensing, hosting and server-side components the deployment covers before work starts.
How we build
- Deploy the chosen platform in your cloud accounts, data center or air-gapped network, with the server hosting plan documented.
- Configure authentication, client packaging and offline collector templates against your incident response procedures.
- Test collection and hunt workflows on representative endpoints, recording gaps before the platform is used in a live case.
How we hand over
- Deliver written runbooks for deploying clients, running hunts, collecting evidence and updating the server.
- Document the upgrade path, including any API changes such as GRR 4.0 moving to the v2 API, so your integrations are updated.
- Walk your responders and platform staff through the handover and record what was tested and what remains open.
How we support
- Offer ongoing support, upgrades and continuous testing as a retainer after the initial handover.
- Review new platform releases, such as security updates, against your deployment before applying them.
- Ask for a quote built from your written scope, since DeployOpen does not publish a price list.
