OpenAI Agent Builder vs Claude Agent SDK: a studio's decision framework

Pick wrong here and you ship the same agent twice — once in a visual canvas, once in code — before anyone notices the workflow never belonged on the platform you started with. We've done that. Twice. The decision between OpenAI Agent Builder and the Claude Agent SDK isn't really a model comparison; it's a decision about who owns the agent six months from now and what kind of iteration your team can actually sustain.

We're a senior engineering studio and a member of the Anthropic Claude Partner Network. Across 40 production agents shipped in 2025 and 2026, we run both platforms — OpenAI Agent Builder for workflows where ops co-owns the design, Claude Agent SDK for workflows where engineering carries the SLA. This is the decision framework we wish we'd had before the first side-by-side deployment.

Two things changed in April 2026 that make this comparison worth re-examining right now. On April 15, OpenAI shipped a major Agents SDK update — native sandbox execution (bring-your-own or built-in support for E2B, Modal, Cloudflare, Daytona, Runloop, Vercel, Blaxel), a new model-native harness with configurable memory, and Codex-style filesystem tools³. One day later, Anthropic shipped Claude Opus 4.7, which kept Opus 4.6's headline pricing ($5/$25 per M tokens) but ships a new tokenizer that can produce up to 35% more tokens for the same input⁴. Translation: Builder got materially more capable on long-running agentic tasks, and Opus got quietly more expensive on token-heavy workloads. Both shift the decision boundary.

If you've already decided on a platform and want the implementation playbook, skim the ergonomics section (§4) and the cost benchmarks (§5), then go read our full Claude Agent SDK production playbook. If you're still picking, read §1 and §2 — that's where 80% of the decision gets made.

Quick verdict — the 60-second answer

Pick OpenAI Agent Builder when a non-engineer co-owns the workflow, you need OpenAI's built-in tools (code interpreter, file search, web), and quick visual iteration beats code ownership as a priority.

Pick Claude Agent SDK when engineering owns the agent end-to-end, production SLAs matter, and you want Anthropic's managed tool loop (retries, streaming, token accounting) without re-implementing it.

Run both when you have mixed workflow shapes — ops-driven automations belong in Builder, engineering-driven agents in the SDK. We've never regretted splitting; we've regretted forcing a single platform on a portfolio that didn't want it.

Dimension	OpenAI Agent Builder	Claude Agent SDK
Category	Visual canvas + Agents SDK runtime	Code-first SDK only
Team shape	Ops + eng co-authorship	Eng-led
Default specialist model	GPT-5.4 ($2.50 / $15 per M)	Claude Sonnet 4.6 ($3 / $15 per M)
Default router model	GPT-5-mini ($0.25 / M input)	Claude Haiku 4.5 ($1 / $5 per M)
First-call tool success (our fleet)	94.3% on GPT-5.4	96.1% on Sonnet 4.6
Median $/request (ops agents)	$0.008–$0.041	$0.011–$0.032
Built-in tools	Code interp ($0.03/session), file search ($0.10/GB/day), web, hosted vector	None (you bring your own)
Visual editor	Yes, first-party canvas1	No first-party builder
Subagents as primitive	Via multi-agent graph	Native /agents primitive2
Native sandbox execution	Yes (April 2026 update)3	No (roll your own)
Computer use	Native (GPT-5.4)	Not first-party as of April 2026
Eval harness UI	First-party (AgentKit Evals)	Roll your own
Lock-in risk	High (canvas logic + OpenAI tools)	Medium (plain API, easy to wrap)
Time-to-first-demo	Hours	Day
Time-to-production (our average)	3–5 weeks	2–4 weeks

If you only read one row, read Team shape. Every other trade-off downstream of that one gets easier once you've matched platform to ownership.

Two categories, not two brands

The framing most comparison posts get wrong is treating "OpenAI Agent Builder" and "Claude Agent SDK" as peers. They're not. OpenAI shipped AgentKit at DevDay 2025 as a platform that bundles three things: the visual Agent Builder canvas, the code-first Agents SDK, and built-in runtime services (ChatKit, Connector Registry, Evals)¹. The April 15, 2026 Agents SDK update added native sandbox execution, a model-native harness, and Codex-style filesystem tools — which strengthens the whole AgentKit stack because Agent Builder workflows run through the same runtime³. Anthropic shipped the Claude Agent SDK as a code-first runtime with Subagents and Skills as the two packaging primitives — no visual canvas, no hosted evals UI, no first-party connector registry, no first-party sandbox².

So the honest comparison lives one level up. It's visual + SDK + hosted services + native sandbox (OpenAI) versus SDK-only with code-first packaging (Anthropic). Both are valid production choices; they optimize for different team shapes.

The implication is specific. When a buyer asks us "should we pick OpenAI or Anthropic for this project," the answer almost always routes through three questions:

1. Who edits the agent after launch? If the answer includes a non-engineer, Agent Builder's canvas is not a nice-to-have — it's the whole reason to pick the platform. If engineering owns edits, the canvas is friction.
2. Do you need OpenAI's built-in tools? Code interpreter, file search, and hosted web search are genuinely differentiated. Nothing in the Claude ecosystem is as convenient for data-analysis agents or document-Q&A agents. If your workflow leans on those, start there.
3. What's your exit cost tolerance? Agent Builder workflows encode decisions in the graph that don't export cleanly. The SDK-only path has lower exit cost — you own the code, the API surface is plain JSON, and a 4-day port is usually the worst case.

Anything else — model quality, token price, tool-use reliability — moves at most 10–15% of the decision. Team shape and tool-mix are the other 85%.

What we built on each: 5 real workflows

The cleanest way to make this comparison concrete is to walk through five workflows we actually shipped, what platform won, and why. Every dollar figure below is median per-request on Claude Sonnet 4.6 or GPT-5.4 (with a small-model router handling classification) unless otherwise noted. Traffic figures are steady-state after the first 30 days in production.

Workflow 1 — Invoice OCR → ERP writeback (Claude Agent SDK)

A 120-person B2B services company ingests ~650 supplier invoices/day across three formats (PDF, image, EDI). The agent extracts line items, matches POs, and writes approved invoices back to NetSuite. We built it on Claude Agent SDK with a Haiku 4.5 router classifying the document type and a Sonnet 4.6 specialist doing extraction. Median cost per invoice: $0.024. P95 latency: 11s. First-pass accuracy on 12k production invoices: 96.3%. This is a code-ownership workflow — the client's eng team inherited it after our 30-day hypercare and has run it for nine months with one prompt tweak.

Why Claude SDK won it. The accuracy ceiling mattered more than iteration speed. Sonnet 4.6's nested-JSON tool-call reliability on multi-page documents is genuinely better than GPT-5.4 in our measurements, and the 30-case eval harness we wired in (see Pattern 5 in the production playbook) caught a regression in week two that would have cost them roughly $9k in rework.

Workflow 2 — Inbound support triage → Zendesk routing (both — A/B'd)

A 40-person SaaS with ~1,200 inbound tickets/week. Agent classifies intent, detects urgency, and routes to one of seven queues. We shipped identical logic on both platforms for 8 weeks as an A/B, routing 50% of traffic each way.

Platform	Median $/ticket	P95 latency	Routing accuracy	Engineer hours to change routing rules
OpenAI Agent Builder (GPT-5-mini)	$0.008	7s	91.4%	2.5
Claude Agent SDK (Haiku 4.5 + Sonnet 4.6)	$0.011	6s	92.1%	4.0

What won it. We kept the Builder version because the support lead — an ops person, not an engineer — edits the routing rules monthly. The 0.7-point accuracy gap didn't justify a workflow where she couldn't touch the graph herself. The SDK version was technically better-engineered and slightly more accurate; it lost on ownership fit.

Workflow 3 — Sales lead enrichment → CRM routing (OpenAI Agent Builder)

RevOps workflow at a 60-person B2B: ~200 inbound leads/day from form fills, enriched via hosted web search + Clearbit + a hosted file-search index of the company's own sales collateral, then routed to the right AE. Built in Agent Builder on GPT-5.4 because the entire workflow leans on OpenAI's built-in web search and file search. Median cost: $0.041/lead. P95 latency: 14s. Lead-to-AE time dropped from 14 minutes to 22 seconds.

Why Agent Builder won it. Rebuilding OpenAI's hosted web search + file search on the Claude side would have meant wiring our own search proxy, our own vector store, and our own content ingestion pipeline. Three months of work to save 15–20% on the token bill. The math didn't work.

Workflow 4 — Internal research agent (Claude Agent SDK)

Legal-adjacent research agent for a 25-person consultancy, querying across Notion + Google Drive + a private case library. Handles ~40 queries/day from the partner team. Uses two subagents (retrieval specialist + synthesis specialist) coordinated by a router. Median cost: $0.18/query (heavier on tokens because synthesis runs long). P95 latency: 22s. Citation accuracy on a 50-case eval: 94%.

Why Claude SDK won it. Two reasons. First, subagent decomposition is a first-class primitive — the router-plus-specialists shape is genuinely cleaner in the SDK than it is as a multi-agent graph in Builder. Second, the partner team cares about auditability: every citation in the output links back to the source document with a confidence score, and wiring that logging in code was faster than wiring it inside a visual canvas we'd then need to export.

Workflow 5 — Vendor reconciliation for monthly close (OpenAI Agent Builder)

Finance close automation at a 90-person company: agent reconciles 8 vendor ledgers against the GL, flags mismatches, and generates a reviewer queue. Runs once a month, processes 3,200 transactions. Built in Agent Builder on GPT-5.4 because Code Interpreter does the reconciliation math inside a managed sandbox. Total cost per close: **$19/run** (model tokens + Code Interpreter sessions at $0.03 each). End-to-end runtime: 38 minutes.

Why Agent Builder won it. Code Interpreter inside a hosted run is a genuinely differentiated capability, and the April 2026 Agents SDK update made the surrounding sandbox story even more competitive — native support for E2B, Modal, Daytona, Cloudflare, Vercel, Runloop, and Blaxel means we can pair Code Interpreter with custom sandboxed tools on the same runtime³. The Claude equivalent — spinning up our own Python runner with the right numpy + pandas environment, wiring it as a tool, and handling the failure modes — was a 3-week build estimate. Agent Builder gave us the same thing in an afternoon. Lock-in risk is real (the reconciliation logic lives partly in the canvas graph) but the workflow runs monthly and the client accepts the tradeoff.

Developer ergonomics compared

Ergonomics is where the platforms feel most different day-to-day. The visual-versus-code axis dominates, but the subtler differences around packaging, iteration, and debugging matter more after week three.

Iteration speed. Agent Builder is faster to the first demo by roughly a factor of two — you can wire a working agent in an afternoon without writing code. The SDK catches up by week two because code-level edits (tweaking a prompt, adding a retry, extracting a skill) are faster than clicking through nodes in a canvas. By the time an agent is 6 weeks into production, a code-based iteration cycle tends to be 30–50% faster than a canvas-based one, simply because git diff is faster than visual diff.

Packaging and reuse. Claude's Skills mechanism is a genuinely useful packaging primitive — a skill is a bundle of prompts, tools, and examples the agent loads on demand, versioned alongside the rest of the code². It keeps system prompts small (ours average ~800 tokens instead of the 2,000–4,000 we used to ship) and makes domain logic reviewable. OpenAI's equivalent is currently sub-workflows inside Agent Builder plus the Connector Registry for external services. Both work; the SDK's approach is closer to how engineering teams already think about shared code.

Subagents. This is where the SDK pulls clearly ahead for engineering teams. The /agents primitive is native — a subagent is a full agent with its own system prompt, tool surface, and context window, invoked as a tool from the main agent. In Agent Builder, multi-agent graphs are modelled as connected nodes; it works, but the canvas gets busy fast and the mental model diverges from how your ops team thinks about delegation. Our rule of thumb: if a workflow has 2+ distinct phases that want isolated contexts, the SDK's subagent model will save you 15–30% on tokens and the canvas will cost you an extra day of debugging per phase.

Debugging. Agent Builder's first-party trace UI is excellent — every step of every run is inspectable inside OpenAI's platform, including tool inputs, outputs, and model reasoning. The SDK ships no UI; you wire your own observability (we default to OpenTelemetry + a boring dashboard). For the first 2 weeks of a project, the Builder UI is a meaningful productivity gain. After that, a custom dashboard tuned to your specific SLAs tends to be more useful — generic traces are noise once you know what to watch.

Version control. SDK agents live in git. You get PR review, blame, branching, rollback. Builder workflows live in OpenAI's platform with versioning that's closer to Google Docs than to git — it works, but the review story is weaker, and there's no native way to branch an agent and merge changes back. For regulated environments this matters; for an ops team editing routing rules, the Builder model is actually better than forcing them into git.

Local dev. SDK wins cleanly here — npm run dev or the Python equivalent gets you a running agent against a staging key in 30 seconds. Builder requires the OpenAI platform for authoring; you can test exported workflows locally but the round-trip adds friction.

Cost + latency benchmarks from 40 agents

Numbers below are medians across 40 production agents running between December 2025 and April 2026. Mix: 23 on Claude Agent SDK, 12 on OpenAI Agent Builder / Agents SDK, 5 on a hybrid router that falls between the two. Traffic: ~380k requests/day aggregate.

Workload class	Platform	Model mix	Median $/request	P95 latency	Requests/day (median)
Triage / classification	Claude SDK	Haiku 4.5 router + Sonnet 4.6	$0.011	6s	1,200
Triage / classification	Agent Builder	GPT-5-mini	$0.008	7s	1,200
Document extraction	Claude SDK	Sonnet 4.6	$0.024	11s	650
Document extraction	Agents SDK	GPT-5.4	$0.029	12s	500
Lead enrichment (tool-heavy)	Agent Builder	GPT-5.4 + web + file search	$0.041	14s	200
Lead enrichment (tool-heavy)	Claude SDK	Sonnet 4.6 + custom tools	$0.034	16s	180
Research / synthesis	Claude SDK	Sonnet 4.6 + subagents	$0.18	22s	40
Reconciliation (Code Interpreter)	Agent Builder	GPT-5.4 + Code Interpreter	$0.62/run	38min	30/mo

Three patterns show up repeatedly:

Pattern 1: Small-model routers kill the bill. On every workflow where we introduced a Haiku 4.5 or GPT-5-mini router, total cost dropped 30–45% versus a single-model-does-everything baseline. The router classifies; the specialist does the expensive work. This is platform-agnostic — it worked on both stacks.

Pattern 2: Claude's tool-use premium pays for itself on complex chains. On workflows with ≤3 tool calls per request, GPT-5.4 is 15–20% cheaper on input tokens and no different in reliability. On workflows with 6+ tool calls per request, Claude Sonnet 4.6's first-call success rate translates to 8–14% fewer retries, which closes the per-token price gap. For long tool chains we default to Claude; for short chains it's a toss-up.

Pattern 3: Built-in tools are the cost story nobody plots. OpenAI's hosted web search bills per query, Code Interpreter runs at $0.03 per session, and File Search charges $0.10 per GB of storage per day¹. The Claude SDK has no hosted tools, so you pay only model tokens — but you also pay your infra bill for whatever you wired up. On workflows where built-in tools are in the hot path, Agent Builder's total-cost-of-ownership can run 20–30% higher than naive comparisons suggest. On workflows where they aren't, the two platforms are within 10%. Prompt caching (up to 90% off cached input tokens on both sides) is the single biggest lever we pull on expensive workflows — worth wiring before worrying about platform choice.

A note on latency. P95 numbers above include network + tool calls, not just model inference. On pure-inference workloads we don't see a meaningful latency difference between the two platforms — both settle around 4–7s p95 for single-call classification agents. The gaps widen only on tool-heavy workloads, and there the gap is driven by which tools you're calling, not which platform hosts them.

Tool-use + handoff reliability

This is the section where we spend the most time in technical diligence calls. The numbers below are from a March 2026 internal benchmark across all 40 production agents, measured on our golden-trace test sets (30–50 reference cases per agent). Directionally, our results track the public comparison on Artificial Analysis, where Sonnet 4.6 and GPT-5.4 trade the top spot across reasoning, knowledge, math, and coding subtests depending on effort setting⁵ — our fleet numbers below are the production-tool-use slice of that picture, not a generalist benchmark.

Methodology note. Each agent has a fixed golden-trace set scored hourly in production. Shallow schemas are 1–3 top-level JSON fields; nested schemas are 4+ fields with at least one nested object or array; long chains are requests that trigger 6+ distinct tool calls before terminating. "First-call success" means the tool call produced a valid structured response with no retry. Numbers are medians across agents within each category — individual agents vary ±1.5 points.

First-call tool success rate (percentage of tool calls that produce a valid structured response on the first attempt):

Model	Shallow schemas (1-3 fields)	Nested schemas (4+ fields)	Long chains (6+ calls)
Claude Sonnet 4.6	98.7%	96.1%	94.4%
GPT-5.4	98.1%	94.3%	89.2%
Claude Haiku 4.5	97.2%	92.0%	87.1%
GPT-5-mini	96.8%	91.7%	85.3%

Claude Sonnet 4.6 edges ahead on every axis, and the gap widens as schemas get nested or chains get longer. This is the single concrete reason we default to Claude for extraction workflows. On triage / classification — where a single tool call handles the whole decision — the gap is small enough that other factors (cost, team shape) dominate. Two caveats: GPT-5.4's March 2026 release materially closed the gap on shallow schemas (prior GPT-5 generations trailed by 2–3 points) and added native computer-use control that isn't matched first-party on the Claude side yet — so for agents that drive a browser or operate a desktop, GPT-5.4 becomes the default regardless of tool-chain length.

Handoff reliability between subagents / nodes. On Claude SDK's native /agents primitive, context compression during handoffs is managed by the runtime — we see handoff failures (context loss, token overflow) in ~0.4% of production requests across 40 agents. In Agent Builder multi-agent graphs, handoff failures run 1.2–1.8% depending on graph topology. Both are low enough not to matter on most workflows; both become binding constraints on research-style agents where handoffs happen 4+ times per request.

Built-in tool reliability. OpenAI's hosted tools are generally reliable but fail differently than custom tools. Web search occasionally returns stale results (cache invalidation lags); code interpreter fails silently on memory-constrained runs and the error surfaces as an empty tool response rather than an exception. Both are recoverable with defensive prompting — but you need to know they happen. Claude's custom-tool path surfaces errors as exceptions you can catch and retry explicitly; the discipline is higher but the failure modes are more visible.

The guardrails we wire either way. Regardless of platform, every production agent gets three guardrails: a max_tool_calls budget (default 12, tuned per agent), a token-cost circuit breaker that kills the request at 2× budgeted cost, and same-args detection that aborts if the agent calls the same tool with the same arguments twice consecutively. We borrowed this pattern from the Claude Agent SDK playbook and now apply it to Agent Builder workflows through custom nodes. It's the single cheapest defense against the two most expensive failure modes (silent cost explosions, infinite loops).

Eval + observability story

Evals are the deployment gate. An agent that can't prove it handles its reference cases doesn't go to production traffic — same rule, different tooling on each platform.

OpenAI AgentKit Evals. First-party, included in the platform, graph-level evals attached directly to Agent Builder workflows¹. You can score traces, define pass/fail criteria, and run regression suites against a fixed input set. The April 2026 Agents SDK update strengthened the observability story further — the new model-native harness standardizes how traces, memory state, and sandbox execution get logged, so evals can now score full sandboxed runs instead of just model calls³. The UX is genuinely good for ops-aware teams — a non-engineer can author evals alongside the workflow. The limitation is format: you're inside OpenAI's platform, your evals live in their data plane, and exporting eval results for a BI tool or an internal dashboard requires their API.

Claude Agent SDK evals. No first-party UI. You build your own harness (the three-layer harness from the production playbook: prompt unit tests, output property tests, golden-trace drift detection) and run it in CI. Data lives wherever you put it. This is more work upfront and more flexibility downstream — we've wired Claude evals into GitHub Actions, into custom Grafana dashboards, and into per-project review workflows. None of that is possible at the same depth inside Agent Builder.

Observability. Agent Builder ships a trace UI that covers 80% of what you need in the first 6 weeks. The SDK ships nothing; we default to the three-layer harness feeding OpenTelemetry into whatever observability stack the client already uses (Datadog, Grafana, Honeycomb). The long-run pattern: Agent Builder is faster to "good enough" observability; the SDK is better for "exactly what we need" observability.

What we actually watch per agent — four signals across both platforms: end-to-end latency p50/p95/p99, token cost per request with a weekly trend, tool-call success rate per tool, and eval score against the golden trace set run hourly. One on-call page rule: if any one goes >2σ off its 7-day baseline, someone gets paged. The boring observability stack beats fancy tracing every time, regardless of platform.

When OpenAI Agent Builder wins

Score: 4/5 for non-engineering-led teams. The visual canvas is genuinely differentiated; the built-in tools are genuinely good; the lock-in risk is real but manageable for the right workflows.

Standout strengths. First, the canvas makes the workflow editable by non-engineers — for ops-driven automations, this is the whole ballgame. Second, Code Interpreter, File Search, and hosted web search are real capabilities that are annoying to replicate on other stacks. Third, the first-party eval + trace UI gets you to good observability in an afternoon instead of a week. Fourth, AgentKit's Connector Registry gives you vetted integrations to common SaaS tools without wiring OAuth flows yourself. Fifth — and this is the big April 2026 shift — native sandbox execution across E2B, Modal, Daytona, Cloudflare, Vercel, Runloop, and Blaxel means long-horizon agents that inspect files, run commands, and edit code now have a first-class runtime³.

Trade-offs. Platform lock-in is not theoretical — canvas graphs encode decisions that don't export cleanly. Runtime customization is bounded by what the platform exposes; if you need a weird retry policy or a custom tool-call preprocessor, you may be stuck. Visual-to-engineering drift is a real long-term risk: ops edits the canvas, eng doesn't see the diff in review, and a month later the workflow does something neither team expected. The April 2026 harness is Python-first — TypeScript support is planned but not shipped as of this writing³, so TS-only shops should check the current state before committing.

Pricing snapshot (April 2026). Model API cost (GPT-5.4 at $2.50/$15 per M tokens; GPT-5-mini at $0.25/M input) + built-in-tool costs (Code Interpreter $0.03/session, File Search $0.10/GB/day, hosted web search per query)¹. Batch API and prompt caching drop costs further on eligible workloads. For a typical ops agent running ~1k requests/day, expect $200–$600/month in total OpenAI costs before volume pricing.

When to pick it.

1. A non-engineer co-owns the workflow — not as a reviewer, as an editor who needs to change rules monthly.
2. The workflow leans on code interpreter, file search, or hosted web search — rebuilding these on another stack would take weeks.
3. You're committed to OpenAI models for this workflow and don't foresee needing cross-provider fallback in the next 12 months.
4. Time-to-first-demo matters more than runtime customization — the canvas gets you working in hours.

When Claude Agent SDK wins

Score: 5/5 for engineering-led teams shipping production ops agents. The SDK is the most production-grade code-first agent runtime we've used; the tool-use reliability numbers back that up across 40 deployments.

Standout strengths. First, code-first from day one — the agent lives in git, PR review catches regressions, and the eval harness is standard CI infra. Second, Subagents and Skills are real packaging primitives that keep domain logic reviewable and system prompts small — Subagents run in their own fresh conversations with intermediate tool calls staying inside the subagent and only the final message returning to the parent². Third, first-call tool success rate on nested schemas and long chains is the best we've measured. Fourth, Claude Partner Network access unblocks production issues in hours instead of days. Fifth, the protocol is plain JSON — wrapping it in a thin router for cross-provider fallback is straightforward if you ever need it.

Trade-offs. No first-party visual canvas, which rules it out for workflows a non-engineer needs to edit. No first-party observability UI — you wire your own, which is a feature for mature teams and a tax on early-stage ones. No built-in hosted tools for web search, file search, or Code Interpreter — you bring your own, or rebuild the capability on your infra. No first-party sandbox execution runtime equivalent to OpenAI's April 2026 update — computer-use and long-horizon filesystem agents are a weaker fit on the Claude side as of this writing. The first 2 weeks of a Claude SDK project are slower than the first 2 weeks of a Builder project; the rest of the project is faster.

Pricing snapshot (April 2026). Model API cost — Claude Sonnet 4.6 at $3/$15 per M tokens, Haiku 4.5 at $1/$5, Opus 4.7 at $5/$25 (released April 16, 2026, with a new tokenizer that can produce up to 35% more tokens for the same input — plan accordingly)⁴ — plus whatever infra you host. Prompt caching gets you up to 90% off cached input; batch processing up to 50% off. Sonnet 4.6 runs ~15–25% more on input than GPT-5.4, offset by higher tool-use success and fewer retries. For a typical ops agent running ~1k requests/day, expect $300–$800/month in total Anthropic costs plus your own hosting.

When to pick it.

1. Engineering owns the agent end-to-end, and the team is comfortable with code-first tooling.
2. Production SLAs — latency budgets, cost budgets, accuracy floors — are binding constraints that will need tuning.
3. The workflow involves nested extraction, multi-step reasoning, or subagent delegation where tool-use reliability is a binding constraint.
4. You want the lowest exit cost — the agent lives in your code, behind an API you own, portable if you ever need to switch.
5. Claude is already your model commitment for the rest of the stack, and platform uniformity is a tiebreaker.

FAQ

The questions in the frontmatter cover what buyers ask us on diligence calls — pricing, lock-in, cross-provider fallback, handoffs, and how the two platforms compare on specific failure modes. If your question isn't there and you'd like an answer grounded in the 40-agent fleet, book a strategy call — we'll either give you a concrete answer or tell you we don't have enough data yet.

The short version of every FAQ: platform choice is downstream of team shape and tool-mix. Model quality, token price, and tool-use reliability are real inputs, but they move maybe 10–15% of the decision. If you optimize them before you've answered the ownership question, you ship the same agent twice.

If you want the Claude-side implementation playbook in depth, go read Claude Agent SDK in production: a studio's playbook. If you want us to build or audit one of these with you, book a strategy call — our one-week audit is $1,500 and refundable against a build if you move forward.