Every data lead who scopes a multi-agent research runner reaches the same fork. Ship it on LangGraph or CrewAI, wire in proxies and a vector store, hire two engineers, and own the stack. Or buy a runner, keep the model keys, and spend the FTE months on the retrieval logic that actually differentiates the work. The choice usually gets deferred because the numbers look fuzzy.
They are not fuzzy. Frontier vendors have published enough on token economics, orchestration overhead, and success-rate variance that a break-even volume is calculable in an afternoon. What follows is that calculation, with the line items priced against public 2026 rates.
What the Runner Actually Has to Do
A production research runner is not one LLM call. It is an orchestrator that plans a query, spawns three to five subagents in parallel, gives each its own context window and tool set, and reconciles their output with a citation pass. Anthropic's public writeup describes exactly that pattern, and reports that on their internal research eval a Claude Opus 4 lead with Sonnet 4 subagents outperformed single-agent Opus 4 by 90.2%. Token usage, in the same evaluation, explained 80% of the performance variance.
The architecture assumes you have solved four unglamorous problems underneath it: web extraction that survives Cloudflare and DataDome, a vector store with usable recall on your corpus, a routing layer that picks a cheaper model when the task allows, and durable artifacts so subagents can hand off references instead of re-passing full context. Skip any of them and the token bill explodes without a matching quality gain. Search.co covers this ground in why manual research breaks and in the how it works overview.
The Real Cost of Building In-House
Three cost centers matter: people, model tokens, and extraction infrastructure. The rest is rounding.
People. A credible build needs two senior engineers for roughly six months to a first production release, then one engineer indefinitely for on-call and drift. The 2026 Robert Half guide puts senior AI/ML base pay at a $170,750 midpoint, with total comp reaching $242,507 on Levels.fyi once equity and bonus are added. Loaded monthly cost per engineer lands between $22K and $38K. Twelve FTE-months for the initial build, at the midpoint, is roughly $360K before a single token is spent.
Model tokens. This is the line item that surprises finance. Agents consume about 4x the tokens of chat calls, and multi-agent research runners about 15x the tokens of chat. The multiplier compounds because context gets re-passed between agents, retries burn full traces, and orchestrator calls sit on top of the workers. Prompt caching helps: cross-vendor benchmarks show 41-80% API cost reductions with the right caching strategy, but the engineering to get there is not free.
Extraction. Residential proxy pricing in 2026 ranges from about $1/GB at the low end to $5-8/GB at the enterprise tier. Bright Data lists near $8.40/GB at committed volume; Oxylabs sits around $8/GB with a published 99.95% success rate. What matters is cost per successful request, not sticker price, because a cheap pool that fails on your targets forces retries that show up as extra tokens.
The Buy-Side Line Items
A managed runner replaces the engineer-months and the orchestration bill with a platform fee, then lets you keep your own model provider keys. That last piece matters more than it sounds. A BYOK arrangement means the 15x token multiplier hits your Anthropic or OpenAI invoice at negotiated enterprise rates, not a marked-up passthrough. The runner charges for what it does add: agent orchestration, proxy rotation, vector storage, citation checking, and the audit trail that GitHub-committed artifacts give you.
Extraction and proxy management are typically bundled. That collapses the residential-proxy line item and the anti-bot engineering headcount into a single subscription. The comparison stops being "build vs buy the whole stack" and starts being "which parts do we want to own." Most teams end up owning the retrieval prompts, the evaluation harness, and the schema of the output; everything else is commodity infrastructure they would rather rent.
Where Break-Even Sits
Take a mid-market research team running 2,000 deep-research queries a month, each averaging 400K tokens across the agent graph and 30MB of scraped payload.
Build side: 12 FTE-months at $30K loaded = $360K one-time, then $30K/month steady state. Tokens at Sonnet-class rates work out to roughly $8K-$12K per month with caching. Proxies at $4/GB blended, roughly 60GB/month, add $240. Vector DB and observability round to $1.5K. Twelve-month total, all in, sits between $520K and $560K.
Buy side: engineering time collapses to one integration engineer for the first quarter, roughly $75K loaded. Platform fee for a runner in this volume band is typically $8K-$15K/month. Tokens on the same query mix, billed through BYOK at the same enterprise rate, run the same $8K-$12K. Twelve-month total lands between $265K and $360K.
Break-even flips the other way somewhere north of 15,000 queries a month, where the platform fee scales past the marginal cost of the second in-house engineer and the proxy bundle stops being a discount. Below that, the buy path is cheaper by a factor of roughly two, and it ships in weeks rather than months. Search.co has walked through the retrieval side of the same math in the infrastructure-as-moat piece.
The Trade-offs That Do Not Show Up in the Spreadsheet
Two operational realities decide most build-or-buy calls after the numbers stop mattering.
The first is control over the orchestration surface. Teams building on LangGraph get a graph runtime they can shape node by node, which matters when the task has custom validation or a domain-specific tool. Teams buying a runner get a narrower interface, but the runner's authors have already fought the failure modes that LangChain flags as recurring: context engineering, state pollution, and infinite loops. If your query mix is genuinely idiosyncratic, build. If it looks like competitor tracking, SEC extraction, or market monitoring, buy.
The second is the reliability floor. Anthropic's own guidance is that multi-agent architectures fit breadth-first, high-value tasks that exceed a single context window; they are not the right choice for tasks with heavy inter-agent dependencies. Buying a runner does not change that, but it does mean the vendor has already solved the retry logic, the cost caps, and the observability that let a team sleep through a Friday deploy. A build team spends the first three months rediscovering those problems.
How to Decide This Week
Run three checks against the query workload the runner will actually serve, not the demo one.
- Estimate monthly query volume and average tokens per query. If the annualized token spend is under $150K, the FTE math almost never justifies build.
- Score the query mix on how idiosyncratic it is. If more than half maps to standard agent types (competitor, filings, citation, entity resolution), a runner already ships those.
- Price the extraction load in GB per month against the success rate you need on your five hardest targets. If you cannot commit to $2K+/month in proxy spend to hit 95% success, buying the bundled version is cheaper than staffing it.
The verdict most mid-market teams reach in 2026 is a hybrid: buy the runner, keep model keys, own the evaluation harness and the retrieval prompts. That is the shape of the platform we built at Search.co, and the shape most of the comparable stacks converged on. The build case is real, but it holds mainly for teams whose query volume, or whose orchestration requirements, sit outside what a managed runner is priced to serve. Everyone else spends the FTE months on the parts of the pipeline that no vendor can ship for them.
Written by
Timothy CarterTimothy Carter is the Chief Revenue Officer at SEARCH.co , where he leads global sales, client strategy, and revenue growth initiatives across a portfolio of digital marketing and software development companies. With over 20 years of experience in enterprise SEO, content marketing, and demand generation, Timothy helps clients—from startups to Fortune 1000 brands—scale their digital presence and revenue. Prior to his current role, Timothy led strategic growth and partnerships at several high-growth agencies and tech firms. Tim resides with his family in Orlando, Florida.