BUILD INCLAUDECODEXOPENCODERUN IT ON OYA.
One platform under the agents your customers touch: runtime, memory, gateway connections, a skill library, secrets, evals and a single invoice. Bring the agent you already built, keep your tools, change what runs it.
- 5× fewer tokens per task
- 100% state preserved byte for byte
- 1 invoice, not 14
- 41× cheaper in year one
- 6.4× on recurring tooling alone
https://getoya.ai/api/mcpThe most efficient place to run an agent.
A token loop re-sends every tool result back through the model on the following step, spending tokens, latency and determinism to move state the model never needed to read. Oya compiles the plan once and wires each value by reference.
| Measure | Vercel AI SDK | Mastra | oya |
|---|---|---|---|
| total tokens | 5,754 | 24,387 | 2,536 |
| model round-trips | 6 | 6 | 2 |
| latency | 17.6s | 15.3s | 4.7s |
| execution order | model-chosen | model-chosen | one fixed DAG |
| state fidelity | unguaranteed | unguaranteed | guaranteed |
Same task, same tools, same model: reconcile on claude-haiku-4-5, measured over 8 trials on the real API. The difference is who moves the data. Run the benchmark yourself
What you need to build production agents.
Eight pieces of plumbing sit between a demo that works and an agent a customer pays for. None of them are your product. All of them are already here.
Memory store
Persistent state that lives outside the model context, so the goal doesn't drift on run two.
Gateways and MCP
One connection layer to the systems that matter: 32 gateways today, plus your own MCP servers.
Versioned skills
Typed, sandboxed functions with credential isolation. 96 in the catalog, versioned like code rather than pasted into a prompt.
Guardrail harness
Hard budget, step and scope limits. A runaway loop stops instead of quietly billing you.
Structured planning
One plan up front, then deterministic execution. The model decides intent, not order.
Evals
Replayable traces and scored cases, so "it seems better" becomes a number before you ship.
State preservation
The model works with a handle, not the contents. It cannot read the value, so it cannot corrupt it.
Audit trail
Every run recorded with inputs, tools, output and cost. Hand it to a customer, or an examiner.
What one agent costs to build yourself.
An AI employee is not one purchase. It is a stack: a model to reason, a harness to run the loop, tracing to see why a run failed, search and a browser to research a prospect, a sandbox to run tool code, a messaging API, an OAuth layer, durable scheduling, memory, version control, a CI pipeline, and cloud infra under all of it. Then someone has to build the integrations, and keep building them. Here is the bill.
The agent being priced: Sends 20 personalized LinkedIn invites a day, researches every prospect before it writes, comments on peer posts, and routes inbound DMs to Slack.
Oya ships this one as a template. The teardown prices building the same thing from parts.
or $2,500 per agent, per year
- Search, browser and sandbox built in
- Run traces and cost accounting included
- 32 gateways, OAuth handled
- Routines, triggers and memory native
- Specs versioned to your own GitHub repo
- Live the same day
A senior engineer, or Oya Engineer.
Tooling is the cheap half. A Nango subscription gives you OAuth, not connectors: every integration is still code somebody writes, tests, versions and repairs when the API moves. This is the line most build-versus-buy models leave out, so here it is priced on both sides, along with the two figures that outlast the build: how long until the agent is doing the job, and what it costs to keep it doing the job.
$102,080 against $2,500.
What covers what.
Each line above, and the shipped Oya capability that stands in for it. Every entry maps to something already in the product, not to a roadmap.
Method and sources
- Vendor prices. Published list rates as of August 2026, taken at the lowest tier that supports a production workload: Langfuse Pro $199, Nango paid entry $250, E2B Pro $150, Browserbase Startup $99, Inngest Pro $75, Pinecone Standard $50, LangSmith Plus $39 per seat, LangGraph Platform $35, Unipile from 49 euro per month, Claude Code Max 20x $200. Where a vendor publishes a range, the low end is used.
- The CI/CD line. GitHub Team at $4 per user per month for 3 seats, plus about 5,000 Actions minutes beyond the 3,000 included at the $0.006 per minute Linux rate, plus container storage above the 2 GB allowance. Docker builds and a test suite on every push reach that volume quickly.
- Usage-based lines. LLM inference, web search and cloud infra are estimates for one agent at the volume described, not list prices. Your numbers will move with volume.
- Engineering cost. One senior engineer at a fully loaded US rate of about $95 per hour: 6 to 8 weeks to first production run, 8 integrations at roughly 3 days each, 2 weeks for spec versioning and a deploy pipeline, and maintenance at 15% of an FTE thereafter. Adjust the rate for your market and both totals move together.
- Time to first production run. The three build lines run in sequence at one engineer, which is 3 to 4 months of calendar time and assumes nothing goes wrong. Same day on Oya is the deploy path the walkthroughs show end to end, not a best case: describe the agent, review the spec it writes, deploy. Neither figure includes the time it takes you to decide what the agent should do.
- The maintenance line. It is called out on both sides because it is the largest single figure in the teardown and the only one that repeats every year: an integration is not finished when it ships, it is finished when the vendor stops changing the API. On Oya that work is ours, which is what the $0 means; it does not mean nothing breaks.
- Oya Engineer. The platform’s own build agent. Every built-in tool is disabled, so it can only act through Oya’s own toolset: it writes the agent from a spec, edits skills, wires gateways, and runs evals against saved cases. It is not a replacement for engineering judgment, and someone still writes the spec and reviews what ships. The $0 is the absence of a build project, not the absence of a human.
- Oya pricing. The per agent licence: $250 per live agent per month, or $2,500 per year. Drafts and offline agents are free. Pay as you go is also available, metered per run, which lands well below the licence for low volume agents.
- Not a claim of feature parity. Each vendor above does more than the slice an agent needs, and Oya buys some of its own inputs. The comparison prices the slice a team would have to procure and assemble, not the whole of any product.
Bring us the agent you already built.
Six questions, about a minute. If it is a fit we will run your own workflow side by side with what you have today, and you keep the comparison either way. Prefer to talk first? Book time with a founder or email [email protected].
What best describes your business?