Ceris is serverless optimization infrastructure. It lets you solve scheduling, portfolio, and resource allocation problems via a single API call — no solver license, no infrastructure to manage, and no OR team required. You get production-grade results in seconds and pay only for compute used.

Do I need a commercial solver license?

No. Ceris runs HiGHS and OR-Tools, which are free, open-source, production-grade solvers. They handle the vast majority of optimization problems — portfolio allocation, scheduling, supply chain, resource planning — without a commercial license. If you already have a Gurobi license, you can bring it for additional solver options.

How does serverless optimization work?

Submit your optimization problem via REST API. Ceris routes it to the best available solver, handles burst compute and parallelization, and returns results with a full audit trail. No servers to provision, no cold starts to tune, no infrastructure to maintain.

What problems can Ceris solve?

Ceris handles linear programming (LP) and mixed-integer programming (MIP) problems including portfolio optimization, workforce scheduling, resource allocation, supply chain planning, energy dispatch, and production scheduling. If your problem can be expressed as an optimization model, Ceris can solve it.

How is Ceris different from Nextmv?

Nextmv provides DevOps tooling for teams that already have solver access and OR expertise — model hosting, testing, versioning. Ceris solves the access problem: it gives you production-grade optimization without needing a solver license, infrastructure, or dedicated OR team. Ceris also focuses on general optimization (scheduling, portfolio, allocation) rather than routing specifically.

Build vs. Buy for Optimization Infrastructure: A Real Cost Analysis

You have a routing problem that's costing you $50,000 per month in inefficient deliveries. Your team debates optimization infrastructure build vs buy for three weeks while the bleeding continues. Sound familiar? Every growth-stage company hits this inflection point: when spreadsheets break and you need real optimization, but you're not sure whether to build it yourself or buy a solution.

The answer isn't obvious. I've watched teams spend six months building what they could have bought for $2,000/month, and I've seen others pay $100,000/year for platforms they barely use. The right choice depends on factors most teams don't consider: the hidden cost of engineering time, the spectrum of options beyond "build vs. buy," and the crossover points where economics flip.

Here's what it actually costs to get optimization running in production—and how to think about the decision systematically.

The Optimization Infrastructure Spectrum

Most "build vs. buy" discussions assume two options. Reality offers five distinct approaches, each with different cost structures and capabilities:

1. Spreadsheet + Manual Process

Cost: $0 software, high labor cost
Works for: <10 weekly optimization runs
Breaks when: Data complexity exceeds Excel's limits

2. Custom Heuristics

Cost: 2-4 engineer-months initial, ongoing maintenance
Works for: Simple problems with predictable patterns
Breaks when: You need optimal solutions, not "good enough"

3. Open-Source Solver Integration

Cost: OR-Tools (free) + engineering time
Works for: Teams with OR expertise and time
Breaks when: You need commercial solver performance or support

4. Commercial Solver Licensing

Cost: Gurobi ($15,000-$150,000/year) + infrastructure + engineering
Works for: Complex problems requiring optimal solutions
Breaks when: Infrastructure overhead exceeds optimization value

5. Dedicated Optimization Platform

Cost: API pricing or platform fees
Works for: Teams wanting optimization without infrastructure
Breaks when: Volume exceeds cost-effective pricing tiers

The key insight: most teams skip straight from option 1 to option 4, ignoring the middle ground where they might find better economics.

What It Actually Costs to Run Solvers Yourself

Let's get specific about the real costs of self-hosting optimization infrastructure. I'll use concrete numbers from teams I've worked with.

Commercial Solver Licensing

Gurobi pricing reality:

Named-User license: $15,000/year (1 concurrent solve)
Token Server: $50,000/year (shared pool, typical deployment)
Cloud instances: $75,000/year (fully managed, but vendor lock-in)

CPLEX comparable:

Full license: $120,000/year (enterprise deployment)
Runtime license: $40,000/year (distribute with your application)

That's just the solver. Now add everything else.

Infrastructure and Engineering Costs

Platform engineering reality check: An optimization infrastructure setup requires:

DevOps engineering: 2-3 months initial setup (containerization, auto-scaling, monitoring)
Ongoing maintenance: 20% of one engineer's time (solver updates, infrastructure patches, monitoring)
Compute costs: $2,000-$8,000/month (depending on solve frequency and problem size)

Real example from a 60-person logistics company:

Gurobi Token Server: $50,000/year
AWS infrastructure: $48,000/year (mix of on-demand and reserved instances)
Platform engineer time: $45,000/year (20% of $225k fully-loaded senior engineer)
Total first-year cost: $143,000

For comparison, their previous manual routing process was costing them $200,000/year in inefficiency. The optimization pays for itself, but they spent four months building infrastructure instead of improving models.

The Hidden Cost: Opportunity Cost

Here's what most cost analyses miss: engineering time has two costs. The direct cost (salary) and the opportunity cost (what else that engineer could have built).

Case study numbers:

Senior OR engineer: $200,000 fully-loaded salary
Time spent on infrastructure: 30% (common for self-hosted teams)
Opportunity cost: $60,000/year of optimization improvements not built

That $60k represents features like dynamic re-optimization, constraint relaxation analysis, or multi-objective optimization. Features that directly impact business value.

The 30% rule: In my experience, OR teams self-hosting solvers spend 30% of their time on infrastructure. That percentage drops to 5% with managed platforms.

Where the Economics Cross Over

The build vs. buy decision has clear inflection points based on team size, solve volume, and latency requirements.

Team Size Breakpoints

1-2 optimization engineers: Buy. You don't have the bandwidth to maintain infrastructure and improve models.

3-5 optimization engineers: Gray zone. Build if you have specific latency/customization needs that APIs can't meet.

6+ optimization engineers: Build starts making sense, assuming you have platform engineering support.

Volume and Cost Analysis

Let's model the crossover points with real numbers:

Low volume (< 1,000 solves/month):

Managed platform: $500-2,000/month
Self-hosted: $12,000+/month (minimum viable infrastructure)
Winner: Managed platform by 6-10x

Medium volume (10,000 solves/month):

Managed platform: $5,000-15,000/month
Self-hosted: $15,000/month
Winner: Depends on solve complexity and latency needs

High volume (100,000+ solves/month):

Managed platform: $50,000+/month
Self-hosted: $20,000-30,000/month
Winner: Self-hosted if you have the engineering capacity

Latency Requirements Reality Check

If you need sub-100ms optimization responses, you're probably building. Most APIs add 50-200ms network latency that some applications can't tolerate.

But question the requirement. I've seen teams spend months optimizing for latency that didn't matter to users. Real-time route optimization for delivery drivers? Probably needs <1s response. Weekly supply chain planning? 30 seconds is fine.

The "Start with API, Graduate to Self-Hosted" Path

The smartest teams don't choose build or buy—they choose both, sequentially.

Phase 1: Prove value with APIs (months 1-6)

# Quick validation with a managed optimization API
import requests

def optimize_routes(orders, vehicles):
    response = requests.post(
        'https://api.optimization-platform.com/vrp',
        json={
            'orders': orders,
            'vehicles': vehicles,
            'options': {'max_solve_time': 30}
        },
        timeout=30,
    )
    response.raise_for_status()
    body = response.json()
    return body.get('routes', [])

# Costs: $2,000/month, 0 infrastructure overhead

Phase 2: Scale with hybrid approach (months 6-18)

Keep API for quick experiments and edge cases
Build critical paths with self-hosted solvers
Costs: $8,000/month, some infrastructure overhead

Phase 3: Full self-hosted (18+ months)

Custom solver integration with your exact requirements
Full control over performance and data
Costs: $15,000+/month, significant engineering overhead

Real example: A food delivery company started with Mapbox Optimization API ($1,500/month), graduated to self-hosted OR-Tools for core routing ($8,000/month total), and eventually built custom constraint propagation for sub-second re-optimization ($25,000/month total). Each phase was right for their needs at the time.

Decision Framework: Where Do You Fit?

Stop debating abstractions. Score your situation:

Technical Requirements (1-5 scale)

Solve frequency: 1 = weekly, 5 = real-time
Problem complexity: 1 = simple routing, 5 = multi-objective with complex constraints
Latency sensitivity: 1 = batch overnight, 5 = sub-second response required
Customization needs: 1 = standard algorithms work, 5 = novel constraint types

Score < 10: Start with managed platform Score 10-15: Hybrid approach or careful build evaluation Score > 15: You probably need to build

Team Capability (1-5 scale)

OR expertise: 1 = none, 5 = multiple PhD-level practitioners
Infrastructure capacity: 1 = no platform team, 5 = dedicated MLOps/platform engineers
Timeline pressure: 1 = need results next month, 5 = can invest 18+ months

Score < 8: Buy, no question Score 8-12: Buy first, plan to build later Score > 12: Build is viable if requirements justify it

Making the Decision Stick

Whatever you choose, plan for change. Requirements evolve, teams grow, and economics shift.

If you're buying: Negotiate contract terms that don't trap you. Ensure data portability. Understand the pricing model at 10x your current scale.

If you're building: Start with the minimum viable infrastructure. Don't build auto-scaling until you need it. Don't optimize for problems you don't have yet.

In both cases: Instrument everything. Track solve times, success rates, and business impact. Data beats opinions when it's time to reevaluate.

The best decision is the reversible decision. The worst decision is no decision while your inefficient processes bleed money.

FAQ

Q: Can open-source solvers like OR-Tools compete with commercial solvers like Gurobi?

A: For many problems, yes. OR-Tools handles routing, scheduling, and assignment problems well. But Gurobi typically solves complex mixed-integer problems 2-5x faster, which matters for large-scale operations. The performance gap varies by problem type—test with your actual data, not benchmarks.

Q: How long does it take to build optimization infrastructure from scratch?

A: Plan 3-6 months for a basic production setup with a commercial solver, assuming you have platform engineering support. Add 2-3 months if you need custom auto-scaling or multi-tenancy. If you're starting with open-source solvers and no platform team, budget 6-12 months to get it right.

Q: What's the minimum team size that should consider building?

A: You need at least one full-time OR engineer and access to platform/DevOps engineering (doesn't have to be dedicated). Most successful build decisions happen with 3+ optimization engineers. Smaller teams should buy unless they have very specific requirements that managed platforms can't meet.

Q: How do I evaluate managed optimization platforms?

A: Test with your real data, not toy problems. Measure end-to-end latency including network overhead. Understand the pricing model—does it scale linearly with your growth? Check data residency and security compliance. Ask about solver versions and update policies. Most importantly, ensure you can export your models if you need to switch later.

Q: Should startups ever build optimization infrastructure?

A: Rarely. Startups should focus on proving product-market fit, not building infrastructure. The exception: if optimization performance is your core differentiator (like autonomous vehicle routing), you might need to build eventually. But start with managed platforms to validate demand first. I've seen too many startups burn runway building infrastructure for optimization problems that didn't matter to customers.

This analysis reflects real deployment patterns, but your situation is unique. At Ceris, we're building serverless optimization infrastructure that gives teams the performance of self-hosted solvers without the infrastructure overhead—basically trying to push the build vs. buy crossover point higher so more teams can focus on optimization instead of DevOps.