Meta announced Rebalancer as open-source software on September 21, 2026. It is a library for modeling and solving constrained assignment problems—such as placing services on servers or hardware in racks. Meta says it uses Rebalancer for roughly 40 million assignment problems per day; that is a company-reported production figure, not an independently audited benchmark.
What Rebalancer does
Rebalancer helps developers express assignment problems in which objects must be placed into bins or containers while satisfying rules and optimizing goals. An object might be a workload, server shard, or piece of hardware; a bin might be a server, datacenter, rack, or meeting room. The model describes the objects and bins, their dimensions and relationships, and the constraints and objectives that should govern the assignment.
Meta describes Rebalancer as a C++ library with a Python interface. The project separates the problem specification from the solving strategy: it converts the model into an expression graph, which can then be searched directly or translated into a mixed-integer program for an external solver. See the official introduction for the project’s modeling concepts.
This is a general-purpose optimization library, not an AI assistant or a turnkey placement service. Developers still need to define what counts as a valid assignment and what the system should optimize.
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How its two solving approaches differ
Rebalancer’s model can be paired with different solving approaches. The practical choice depends on the required guarantee, problem size, model size, available time and memory, and whether an external solver is acceptable.
| Approach | What it does | Trade-off and likely fit |
|---|---|---|
| Local search | Starts with an assignment and explores changes, such as moving objects between bins. | Can scale to very large problems, but it is heuristic and does not guarantee a global optimum. Meta says nearly all of its large-scale problems use this approach. Meta’s announcement |
| Mixed-integer programming (MIP) | Rebalancer translates the model into a formulation for an external solver, including open-source HiGHS or commercial Gurobi and FICO Xpress. | A MIP solver can establish an optimum if it solves the formulation to completion, but larger models can demand too much time or memory. Meta describes using MIP for smaller or moderate problems, prototyping, and offline tuning. Solver overview |
“Optimal” applies to the MIP approach when a solver completes the optimization; it does not mean every Rebalancer run finds a global optimum. Local search gives up that guarantee in exchange for scalability. Nor does the MIP option imply that the largest workloads Meta describes can be handled economically by MIP. External solver availability, terms, and requirements differ, so check the relevant solver’s own documentation before choosing one.
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What Meta reports about production scale
Meta says it had used Rebalancer internally for more than nine years before open-sourcing it. In its September 21, 2026 announcement, the company reported these operational figures:
| Meta-reported measure | Workload or qualification |
|---|---|
| Roughly 40 million assignment problems per day | Meta’s reported usage at the time of its 2026 announcement; not an independent benchmark. |
| More than 30 unique problem formulations | Reported in the same announcement. |
| P99 solve time of 12 seconds | For a problem with 265,000 objects and 3,200 bins, as reported by Meta in 2026. |
| Average solve time of 171 seconds | For runs with more than 1 million objects and 5,000 bins; Meta says there were more than 3,400 such runs. |
These figures describe Meta’s workloads and operational experience. They are not a controlled comparison with another solver, and they do not establish the time or quality a different organization should expect from its own model. Constraints, objectives, hardware, solver choice, and workload shape can all affect results. Meta’s announcement provides the source context for the reported numbers: Rebalancer: a generic, high-performance library for assignment problems.
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Problems Meta says it has modeled
Meta’s examples span infrastructure placement and routing as well as non-infrastructure assignments. The company cites:
- Placing hardware across racks and fault domains, and placing services or tasks on servers.
- Routing traffic among datacenters, allocating shards and servers, and balancing machine-learning workloads.
- Grouping serverless functions and planning load-balancing migrations.
- Assigning meeting rooms or support tickets.
These are examples Meta says it has modeled, not a guarantee that every use case is equally suitable or independently validated. A problem must be expressible in terms of objects, bins, constraints, and objectives, and the chosen approach must fit its scale and operational needs.
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Inspecting runs and adopting the project
Meta also released Rebalancer Explorer, a Dockerized web UI for examining runs. The announcement describes using it to investigate binding constraints, explore the effects of relaxing constraints, and understand why an object received a particular bin. This can help developers diagnose a model’s behavior rather than treating an assignment as a black box. Details and current setup instructions are in the official repository.
The repository describes the project as Apache 2.0 licensed and includes build and package-install options. Before adopting it, check the repository for current installation instructions and requirements, and check the terms and setup needs of any external solver you plan to use. Rebalancer’s source license does not itself determine the license or availability of those separate solver products.
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What to evaluate before choosing a solving mode
- Need for a proven optimum: If a provable optimum matters, assess whether a MIP formulation and solver can finish within your time and resource limits. If a high-quality feasible assignment at large scale is the priority, local search may be the more practical direction.
- Model and workload size: Estimate not just the number of objects and bins, but also the constraints and relationships in the model. Meta’s published workloads are not a performance forecast for a different formulation.
- Operational budget: Consider solve-time deadlines, available memory and compute, and whether repeated runs are part of the intended workflow.
- Dependencies and licensing: Confirm whether your chosen MIP solver is available under terms that fit your project. Rebalancer’s documentation lists HiGHS, Gurobi, and FICO Xpress integrations; solver-specific conditions remain separate.
- Debuggability: Decide whether you need an interpretable baseline, the ability to inspect binding constraints, or tools for examining why a particular assignment was made.
Meta’s official materials do not provide a controlled apples-to-apples benchmark comparing local search with MIP across the same models. Treat the two methods as alternatives with different guarantees and resource profiles, not as a universal speed ranking.
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