AI Agents Are the API Economy's Biggest Customers: What Agencies Should Charge

On August 24, 2026, PYMNTS reported a shift that every AI agency needs to price into its next proposal: AI agents have become "the fastest-growing class of API consumers," and the API economy's core assumption — that every API was built for a human developer, analyst, or customer — no longer holds. Agents now query endpoints, process responses, and act on them in long sequences with no human in the loop, and the pricing, identity, and trust infrastructure underneath the API economy "were not designed for this" and "are being rebuilt now."

For AI agencies, this isn't a macro story. You build agent workflows that consume APIs on the client's behalf — every token, every call, every resolution is a cost line you either absorb, pass through, or price around. Here's what the data shows and what it means for how you bill.

Every API Was Built for a Human (Until Now)

The old API economy was simple: a human wrote code, read the response, and decided what to do next. Pricing followed seats, developers followed documentation, and API keys were tied to people.

That assumption is what's breaking. An AI agent doing a shopping task queries roughly 5,000 websites — versus about 5 for a human doing the same job, according to Cloudflare's Matthew Prince (SXSW 2026, via Cloudflare Radar). One agent task can touch more endpoints than a whole department used to in a month. When the consumer of an API is software, the credential is no longer tied to one human, and per-seat economics stop making sense.

The Numbers Behind the Agent Shift

The growth data is consistent across every independent source that measures it:

• OpenRouter processed more than 1 trillion tokens per day in late 2025, in a study covering 100T+ tokens and 5M+ developers. "Agentic inference" — models planning, retrieving from APIs, revising, and iterating — was the fastest-growing behavior on the platform (a16z / OpenRouter State of AI, Dec 4, 2025).
• AI-related API traffic on Postman grew 73% year over year (Postman 2024 State of the API Report, cited July 10, 2025).
• Bots crossed 50% of all global HTML requests on April 27, 2026, and by early June 2026 stood at 57.5% bots vs. 42.5% humans (Cloudflare Radar, June 6, 2026).
• Gartner predicts 60% of brands will use agentic AI for one-to-one interactions by 2028, and that AI agents will outnumber sellers 10 to 1 by 2028 (both forecasts, Gartner, Jan 15 and Jul 28, 2026).

One framing caveat matters: no public dataset measures agents' absolute share of API traffic or billings. The defensible claim is growth — agents are the fastest-growing class of API consumers — not that they're already the largest by volume.

How Agent Workflows Actually Consume APIs

Understanding where the cost lands matters more than the headline numbers. A typical client agent doesn't make one API call; it makes a fan-out:

• An agent plans a task, calls a search API, reads results, calls content APIs, drafts output, calls a generation model, revises, and calls back.
• Each step can retry, reload context, or spawn subagents — and every one of those intermediate calls bills.
• A single support ticket can fan into multiple actions; loops still bill; and metered credits often don't roll over.

The result is that API consumption has shifted from "a few predictable calls per user" to "machine-scale, bursty, and hard to forecast." That's why the enterprise deployments are landing in the highest-volume workflows: financial institutions are already using agents for loan origination, claims processing, transaction reconciliation, and client onboarding, per the WEF/Accenture AI Playbook for Financial Services (June 2026). Goldman Sachs co-developed autonomous agents with embedded Anthropic engineers for trade accounting and client vetting (CNBC, Feb 6, 2026). Allianz Partners cut its claims cycle from 19 days to 4, with 71% of claims closing in 12 hours or less (Travel Weekly, Sep 24, 2025). Lloyds reported generative AI delivering £50M+ in annual value, expected to double to £100M in 2026 — an expectation, not yet realized (Lloyds, Feb 26, 2026).

What "Agent-First Pricing" Means — and Why Per-Seat SaaS Is Breaking

"Agent-first pricing" is billing based on agent activities, completed tasks, outcomes, or resources used — not human seats. The logic is simple: per-seat pricing assumed the user was a person who logged in, and one agent can now do the work of a department. There are three dominant models:

This isn't theoretical. The vendors agencies build on have already repriced themselves for agents.

How the Market Prices Agent Usage Today

• Salesforce Agentforce lists $2 per conversation and $0.10 per action (20 Flex Credits per action), plus a $125/user/month flat add-on (list prices as of mid-2026 — real bills stack platform seats, Einstein requests, and Data Cloud credits on top).
• Intercom Fin charges $0.99 per resolution, has resolved 40M+ conversations, and hit a 67% resolution rate in December 2025 — with public complaints that bills get expensive fast.
• The underlying token cost collapsed: GPT-4 cost $30 per 1M input tokens in March 2023; GPT-4o mini cost $0.15 per 1M by July 2024 — a 200x drop in 16 months (TokenCost AI Price Index, Mar 20, 2026). Frontier output pricing fell from $60/1M (GPT-4, 2023) to $15/1M (GPT-5.4, 2026), and the "good enough" tier runs roughly 50x cheaper per unit of quality (TokenMix, Apr 29, 2026).

Put those together and the direction is unambiguous: machine-scale consumption is now economically viable, and per-seat math is absurd — an agent has no seat. The catch is forecast complexity: credits don't roll over, one ticket fans into multiple actions, and loops still bill. Agencies that don't model that variance eat the cost on fixed bids.

What This Means for Agency Delivery Economics

Three shifts change your cost basis and your negotiation position:

1. Your cost basis just collapsed. The same client deliverable costs a fraction of what it did 18 months ago. If you bill hourly or pass through API costs as a line item, clients with a calculator will ask why their bill didn't fall. If you bill for outcomes, the collapse is margin expansion.
2. Forecasting is the new skill. The agencies that win this cycle will quote "what will this agent actually consume" credibly — including retries, loops, and fan-out — not just list token prices.
3. Trust is a service line. Accenture's payments survey found 78% of payments leaders expect fraud to increase significantly with agentic payments and 87% say trust is the key barrier (Accenture, May 27, 2026). Clients will pay for oversight, guardrails, and identity work around agents — that's billable scope, not overhead.

The Runtime Line: When OpenAI Hosts the Harness

Which Harness Changes the Engagement

Managed harness. OpenAI manages sessions, orchestration, context compaction and recovery while your application "provides tools and chooses its execution environment" — the runtime becomes a vendor you integrate and price.

Self-hosted. You run codex exec-server in your own or the client's environment, registering the executor with an environment ID and a restricted API key over WebSocket — real scope and a different operator.

Partner sandbox. OpenAI's self-hosted sandbox guide lists nine providers — Blaxel, Cloudflare, Daytona, DigitalOcean, E2B, Modal, Oracle Cloud Infrastructure (OCI), Runloop, Vercel — and its provider setup pages put the compute hand-off procedurally, not in costs: "delete the session and stop the provider sandbox separately."

The gate above all three: the Agents API "currently supports data residency only in the United States and does not support Zero Data Retention (ZDR)", and "Choosing a self-hosted sandbox does not make the Agents API ZDR-eligible."

What Stays Agency Work

• Tool and MCP inventory, per session — the agent object is "the model, instructions, tools, and MCP servers available to the agent"; each tool you add is yours to re-test.
• Evals and acceptance testing — no product feature substitutes for your own test set.
• Data boundaries — residency, retention and the ZDR gate are yours to document; sessions and published artifacts are deleted afterwards, by the builder.
• Cost control — the spend-limits doc is generic: caps are org and project hard limits returning a 429 with organization_spend_limit_exceeded or project_spend_limit_exceeded, and "Enforcement is not instantaneous, so recorded spend can slightly exceed the configured amount." Nothing is Agents-API-specific, which is why it is yours to instrument a hard stop around.
• Client-side obligations — the OpenAI-Beta: agents=v1 header, session deletion, stopping partner sandboxes separately, and a container expiry that "isn't configurable". Fan-out: the multi-agent guide defaults max_concurrent_subagents to 6, excluding the coordinator — the 6-concurrent-subagent cap.

Pricing the Container Meter

Quote it in OpenAI's units first: 1 GB $0.03 · 4 GB $0.12 · 16 GB $0.48 · 64 GB $1.92 per 20-minute session per container. Name the SKU honestly: the row is labelled "Hosted Shell and Code Interpreter", Agents API occurs 0 times in the pricing page's text, and the path from a sandbox to that row is a docs cross-reference, not a line item.

Reconcile the unit, because the page does not. The changelog entry dated Jun 2, 2026 supersedes the session rate for eligible sessions: "billed per minute with a 5-minute minimum, instead of being billed at the full 20-minute session rate," and "The underlying per-minute rate will remain the same" — a change OpenAI says "will lower effective cost for customers." The footnote carries the per-minute rule; the supersession clause and the rate sentence are the changelog's. Eligible is defined nowhere we can read: quote it and stop.

Then do the arithmetic, all of it DERIVED — this page's math on those statements, because OpenAI publishes no per-minute dollar figure for this row (its per-minute dollars are audio rates). Per-minute = tier ÷ 20: $0.0015 · $0.006 · $0.024 · $0.096. Floor = per-minute × 5: $0.0075 · $0.03 · $0.12 · $0.48 — 25% of the printed session price at every tier, so the session price is 4.0× the floor and the units cross at exactly 20 billed minutes. Stated assumptions: one 4 GB container, 3 sessions a day, 22 working days = 66 sessions → $7.92 at the session unit, or $4.752 at 12 derived billed minutes.

Decide who owns the gap. Usage is best-effort: it "can be null when unknown, and recorded counts may change as accounting arrives," "Missing usage does not mean zero usage," "These counts are not a final bill," and the fields "do not expose a separate cache-write count" where cache-write pricing applies. You cannot reconcile an invoice from OpenAI's numbers alone: your own session-and-retry instrumentation is the control, and the quote line is re-baselined, not fixed while the beta iterates. You can run the runtime line on your own numbers, and the token half is priced, not estimated. The container line is never the total; for the product side, OpenAI agents for everyone.

Managed Agents, September 2026: What Moved to Someone Else's Meter

Added September 18, 2026 · covers the September 10, 2026 launch. Every figure in this section was re-read from OpenAI's own pages and documentation on 2026-09-18. The two openai.com announcement pages were read from Wayback snapshots (captured 2026-09-15 and 2026-09-16) because the live pages return 403 to this host; the developers.openai.com documents are live reads. Nothing here replaces the sections above — it is the cost-and-scope delta they were written before.

On September 10, 2026, OpenAI put two products into its API. The Agents API is a managed cloud-agent service in public beta that "is powered by the open-source Codex harness" — OpenAI hosts and maintains that harness, and you choose whether the agent runs in an OpenAI-hosted environment, in your own, or in one of nine partner sandboxes (including Blaxel and Vercel) (OpenAI, Introducing the Agents API). GPT-Live-1, the full-duplex voice model behind ChatGPT's Voice mode, went to developers in the same release (OpenAI, GPT-Live-1 in the API). Neither release changes what an agency is hired to do; both change whose meter you are standing on. The Agents API carries no separate platform price — "There are no additional fees for using the Agents API" — you pay tokens, tools and containers, while GPT-Live-1 is a hard clock at "$0.05 per minute for the front-end voice layer," billed per second with no rounding up to a whole minute (OpenAI API pricing, gpt-live-1 model page).

What moved out of your application code

• Session orchestration and recovery. "OpenAI manages sessions, orchestration, context compaction, and recovery," and a session is "A durable instance of an agent that works on tasks and responds to input" (Agents API overview). That loop used to be your checkpointing code. The published long-session claim is agents built to run "reliably for days," and the only clock OpenAI actually publishes on that path is a one-hour sandbox idle timeout — "This timeout isn't configurable" (OpenAI-hosted environments).
• Context compaction. Compaction is automatic on the managed harness, which is what lets a session run past one context window without the developer writing summarisation logic (Agents API overview).
• Subagent fan-out. Multi-agent delegation is managed: the default is "6", excluding the coordinator (multi-agent guide) — the same cap this page already prices as a fan-out cost guardrail.
• Tool plumbing. Tool search loads tool definitions on demand to cut tokens, and programmatic tool calling runs calls in parallel and filters results in code before they re-enter context (Agents API overview). The tools themselves still bill: web search is "$10.00 / 1k calls" plus search-content tokens at model rates, and file search is "$2.50 / 1k calls" plus storage at "$0.10 / GB per day (1 GB free)" (OpenAI API pricing).
• Sandbox provisioning — and the container meter. Where the agent needs a shell, the container bills per 20-minute session per container: "1 GB $0.03, 4 GB $0.12, 16 GB $0.48, 64 GB $1.92", with a "5-minute minimum per session," and eligible sessions bill by the minute instead of at the full session rate (OpenAI API pricing). The runtime-meter section above is where this page reconciles that unit, including its derived per-minute and floor figures.
• Retries are the honest exception. No public document promises a managed retry count or policy. The guidance is developer-side: on a 409 while a session is still being set up, "wait and retry with a limit on the number of attempts"; on a 5xx from a status or file-list request, "Retry with increasing delays and a deadline." What the vendor does say is that a lost stream is not a lost job — "Closing an event stream does not cancel the task" (OpenAI-hosted environments).

Two absences decide how credibly you can quote the beta. The Agents API publishes no session duration cap, no session concurrency cap and no free tier — the only stated clock is that one-hour sandbox idle timeout. The only published caps anywhere in this story are GPT-Live-1's concurrent sessions (T1 25, T2 50, T3 200, T4 300, T5 500, free tier unsupported) (gpt-live-1 model page). A proposal that assumes a session ceiling is currently assuming.

What an agency still owns — and still bills for

The managed harness takes the plumbing. It does not take the accountability, and that is the whole pricing argument:

• Agent and outcome design. What the agent is for, what it may do unsupervised, and what result the client is buying. That is the billable unit once the runtime is somebody else's line item — and it is the same argument as the orchestration-tuning retainer, priced per outcome instead of per hour.
• Evals and acceptance thresholds. No vendor ships your pass/fail bar. OpenAI's launch page leads with a customer's eval moving from 0.71 to 0.85 — an improving number on the customer's own test set (Introducing the Agents API).
• Data and integration plumbing. MCP servers, function tools, retrieval and permission design stay yours, and the built-in shell and patch tools are unavailable in a no-environment session, so a team that leaned on them has to build the equivalent (Agents API overview). CRM, telephony, billing, ERP, identity and secrets, or a VPC appear on no OpenAI price list because they are not OpenAI's to deliver (Introducing the Agents API).
• Compliance, SOW and accountability. Who answers for a failed call, what the agent may say, what gets logged and where data may be processed all stay with the agency. The Agents API "currently supports data residency only in the United States and does not support Zero Data Retention (ZDR)," and "Choosing a self-hosted sandbox does not make the Agents API ZDR-eligible" (Agents API overview); regional endpoints carry a 10% uplift (OpenAI API pricing).
• Cost governance. Caching, compaction tuning, closing idle sessions, and the metric that matters — cost per successful task rather than per call. The vendor hands you the argument: "Closing saves $0.05 per minute of idle voice time;" (voice latency and cost guide).

How the billable line shifts

When infrastructure becomes a meter, two things change at once. Your build scope shrinks — there is less orchestration code to write and maintain — and your cost basis becomes a vendor's price list that you do not control, cannot freeze and must re-baseline. Both push the same way: the defensible invoice stops being "we built the agent loop," because that loop is now a feature of somebody's product, and becomes "we own the outcome, the eval bar and the integration surface."

The voice layer shows how sharp the shift is. Take a realistic production case: a six-minute voice support session on a GPT-Live-1 front end with a gpt-6-astra backend, a 4 GB hosted sandbox and two web searches. The voice minutes are $0.3000, the backend is $0.6100, the sandbox is $0.0360 and the searches are $0.0200 — $0.9660 per session (derived on this page from OpenAI's published rates). Put the same voice layer on a cheap backend and the session is $0.3692. Voice is 31% of the first bill and 81% of the second: swap the model and the model stops being the lever, because the minutes are. At 1,000 sessions a day that is $966.00 versus $369.20 a day, or $28,980.00 versus $11,076.00 over 30 days. Once the front end is $0.05 a minute, selling model selection is selling the small line.

Voice pricing also became a cross-vendor fight, which is another reason to quote minutes rather than brands: GPT-Live-1 is $0.05 a minute, or $3.00 per open hour, against Gemini 3.8 Live's audio rates at $0.005 a minute in and $0.018 a minute out — about $0.023 a minute, or roughly $1.38 per hour (derived from Google's published rates; a 2.17x gap that is not like-for-like, because Google meters audio tokens while OpenAI meters wall-clock session time, and Google's live model has a free tier while GPT-Live-1's does not).

The crossover table: self-hosted vs managed agent

Rates on this table are OpenAI's, re-read 2026-09-18; the percentages and per-minute equivalents are this page's arithmetic, not published figures. Model the managed lines on your own volumes with the managed-session rows on the AI agent API cost calculator, price the voice half with the GPT-Live-1 cost calculator and the GPT-Live-1 voice agent cost breakdown, and put a hard stop around the beta's best-effort usage accounting with the agent cost controls checklist.

Three answers this section exists to give

How AI Agencies Should Bill Agent Usage

Here are five pricing moves you can implement this week:

1. Move to outcome- or value-based pricing for agent work. If an agent resolves a ticket, closes a lead, or completes a workflow, bill per outcome — that's how the platform vendors price it, and it lets you keep the margin when token costs fall. Intercom's $0.99/resolution shows clients will accept per-outcome math.
2. Pass through API costs with a transparent margin. Put token/model costs in the contract as a pass-through line at a documented markup (e.g., 10–20%), and re-baseline it quarterly. Transparency is your defense when prices move — and prices keep moving.
3. Keep a retainer as the floor, with usage-based overage. Flat-fee retainers still work for governance, oversight, and maintenance — the human-in-the-loop work that doesn't scale with tokens. Add a metered bucket for agent consumption on top. Hybrid is the safest structure in 2026.
4. Model the loops, not just the tokens. When you quote, ask what share of the estimate is raw model usage versus human review, and price retries, subagent fan-out, and context reloads explicitly. Budget rails — a hard cap and a kill switch — are a sellable feature, not just protection for you.
5. Re-baseline your own margin quarterly. Token prices fell ~200x in 16 months and keep falling 30–50% per year. If your pricing is anchored to last year's model costs, you're leaving margin on the table — or about to get a painful renegotiation. Run your numbers through the AI agency pricing calculator and the agency profit margin benchmarks before every quarterly review.

For a deeper look at the billing-model options and cost variables, our AI coding agent pricing guide walks through usage-based vs. outcome-based vs. hybrid structures, and how much an AI agency costs in 2026 covers the retainer side. If you're worried about blowup scenarios, AI agent cost blowups is the cautionary read.

Will AI Agents Replace API Keys and Humans?

No — AI agents don't replace API keys; they consume APIs through them, at machine scale. A single agent task can query thousands of endpoints, so the credential is no longer tied to one human. What agents are replacing is human-driven consumption: they're now the fastest-growing class of API consumers, and identity and trust infrastructure is being rebuilt around machine identities (PYMNTS, Aug 24, 2026; a16z/OpenRouter, Dec 2025; Cloudflare Radar, Jun 2026).

The nuance matters for how you pitch clients. Agents still authenticate with API keys — the key survives; what changes is volume, patterns, and ownership. Machine identity, discovery, and reputation become first-class concerns, and MIT's Ramesh Raskar frames the build-out as identity/discovery, trust/reputation, insurance/repair/legal, and stablecoin micropayments — "the PC era of AI" (MIT Sloan, Jul 13, 2026). Humans don't disappear; they shift to oversight. The WEF playbook describes semi-autonomous agents that escalate to humans, and both Lloyds and Allianz keep explicit human oversight. What's genuinely being replaced is the assumption that a human reads every API response — and per-seat pricing built for human users. Agencies that sell the oversight layer win; agencies that fight it don't.

The Bottom Line for Agencies

AI agents are the API economy's fastest-growing customers, and the pricing model underneath the whole stack is being rebuilt around them — per-outcome, per-action, per-usage, with no human seat in sight. That's a threat to agencies still billing like 2024, and an opportunity for agencies that reprice for the agent era: outcome-based billing, transparent API pass-through with margin, hybrid retainers, honest forecasting of loops and fan-out, and a quarterly re-baseline habit. The data, the vendors, and the enterprises have already moved — clients will expect their agency to have moved too. For the orchestration layer of that hybrid: The retainer variant: orchestration tuning, scoped and measured.

Frequently asked questions

Sources

• PYMNTS, "AI Agents Become the API Economy's Biggest New Customers," Aug 24, 2026: pymnts.com/news/artificial-intelligence/2026/ai-agents-become-the-api-economys-biggest-new-customers
• a16z / OpenRouter, State of AI — 100T-token study, Dec 4, 2025: a16z.com/state-of-ai
• Postman 2024 State of the API Report, AI-related API traffic +73% (cited Jul 10, 2025): blog.dreamfactory.com/beyond-the-buzz-predicting-the-next-five-years-of-data-ai-gateways
• Cloudflare Radar — bots cross 50% of HTML requests, 57.5% vs 42.5% humans; Prince 5,000-site agent queries (Jun 6, 2026): stackfutures.com/blog/bots-overtake-human-web-traffic-cloudflare-2026
• WEF / Accenture, The AI Playbook for Financial Services (Jun 2026): weforum.org/publications/the-ai-playbook-for-financial-services
• CNBC — Goldman Sachs + Anthropic agents (Feb 6, 2026): cnbc.com/2026/02/06/anthropic-goldman-sachs-ai-model-accounting.html
• Lloyds — 2026, the year of agentic AI (Feb 26, 2026): lloydsbankinggroup.com/insights/2026-the-year-of-agentic-ai-and-a-new-era-for-finance.html
• Travel Weekly — Allianz Partners claims 19 days to 4 (Sep 24, 2025): travelweekly.com/Travel-News/Travel-Agent-Issues/Allianz-Partners-leverages-AI-for-faster-payouts
• Salesforce Agentforce pricing (list prices, mid-2026): eesel.ai/blog/agentforce-pricing
• Intercom Fin AI pricing guide (Mar 3, 2026): myaskai.com/blog/intercom-fin-ai-agent-complete-guide-2026
• TokenCost AI Price Index — 200x token price collapse (Mar 20, 2026): tokencost.app/blog/ai-price-index
• TokenMix AI pricing history (Apr 29, 2026): tokenmix.ai/blog/ai-pricing-trends-history
• Accenture — Agentic Commerce and the Future of Payments (May 27, 2026): accenture.com/us-en/blogs/banking/agentic-commerce-payments
• Gartner — 60% of brands use agentic AI by 2028 (Jan 15, 2026): gartner.com/en/newsroom/press-releases/2026-01-15-gartner-predicts-60-percent-of-brands-will-use-agentic-ai-to-deliver-streamlined-one-to-one-interactions-by-2028
• Gartner — AI agents outnumber sellers 10:1 by 2028 (Jul 28, 2026): gartner.com/en/newsroom/press-releases/2026-07-28-gartner-predicts-ai-agents-will-outnumber-sellers-10-to-1-by-2028-yet-fewer-than-40-percent-of-sellers-will-say-agents-improved-productivity
• MIT Sloan — Raskar, "Who will own the AI agent economy?" (Jul 13, 2026): mitsloan.mit.edu/ideas-made-to-matter/who-will-own-ai-agent-economy
• OpenAI, API pricing — built-in tools, container rates (read 2026-09-12): developers.openai.com/api/docs/pricing#built-in-tools
• OpenAI, API changelog — container session billing change, Jun 2, 2026 (read 2026-09-12): developers.openai.com/api/docs/changelog
• OpenAI, Agents API observability guide — usage fields, cache-write gap, "not a final bill" (read 2026-09-12): developers.openai.com/api/docs/guides/agents-api/observability
• OpenAI, Agents API overview — managed harness, US-only residency, no ZDR (read 2026-09-12): developers.openai.com/api/docs/guides/agents-api/overview
• OpenAI, OpenAI-hosted environments — sandbox expiry and container rates (read 2026-09-12): developers.openai.com/api/docs/guides/agents-api/environments/openai-hosted
• OpenAI, Introducing the Agents API (September 10, 2026) — managed Codex harness, "There are no additional fees for using the Agents API," US-only residency: openai.com/index/introducing-the-agents-api (Wayback snapshot 20260916214436; the live page returns 403 to this host)
• OpenAI, Introducing GPT-Live-1 in the API (September 10, 2026) — "$0.05 per minute for the front-end voice layer": openai.com/index/introducing-gpt-live-1-in-the-api (Wayback snapshot 20260915202858)
• OpenAI, Agents API multi-agent guide — default 6 concurrent subagents, excluding the coordinator (read 2026-09-18): developers.openai.com/api/docs/guides/agents-api/multi-agent
• OpenAI, gpt-live-1 model page — concurrent-session limits by tier, no free tier (read 2026-09-18): developers.openai.com/api/docs/models/gpt-live-1
• OpenAI, voice latency and cost guide — wall-clock billing, silence counts, "Closing saves $0.05 per minute of idle voice time;" (read 2026-09-18): developers.openai.com/api/docs/guides/voice-latency-cost
• Google, Gemini API pricing — Gemini 3.8 Live audio $0.005/min in and $0.018/min out, free tier (read 2026-09-18): ai.google.dev/gemini-api/docs/pricing
• OpenAI API pricing, Agents API overview and OpenAI-hosted environments — re-read 2026-09-18 for the managed-agents section; the same pages are the 2026-09-12 entries above.

Accuracy note: All facts, dates, and figures verified against the sources above 2026-08-24 (draft t_095bafd3; evidence gate PASS). The runtime-meter section was added 2026-09-12 and its OpenAI sources were re-read that day; the per-minute and floor figures there are this page's own derivation (tier ÷ 20, × 5), not OpenAI-published rates. Salesforce figures are list prices as of mid-2026; real bills stack platform seats, Einstein requests, and Data Cloud credits on top. Lloyds £100M is a 2026 expectation, not yet realized. Gartner figures are 2028 forecasts. No public dataset measures agents' absolute share of API traffic — growth framing only. The “Managed Agents, September 2026” section was added 2026-09-18 and its OpenAI prices, quotas and quotes were re-read that day (the two openai.com announcement pages from Wayback snapshots dated 2026-09-15 and 2026-09-16); its voice-session totals, voice shares and per-hour equivalents are this page’s own derivation from those published rates.