4b7494be72
Closes CODAGT-835 ## Summary `chat_messages.runtime_ms` becomes the billing source of truth for Coder Agents runtime (summed hourly by #27312), but it was built for debugging: the June refactor (#26270) silently stopped recording tool-step runtime, compaction was never measured, and interrupted turns lost their partial runtime entirely. This PR defines the billable metric, closes the paths that dropped it, and documents the definition where the data lives. ## The billable definition **`runtime_ms` is the wall-clock duration of the model invocation that produced the persisted message content**, measured from just before the provider stream opens until it is fully consumed. What counts: - Assistant generation steps, in top-level and sub-agent chats (sub-agents are ordinary chats on the same generation path). - Compaction summarization calls, persisted on the compaction assistant message (**new**). - Interrupted attempts: the message-part episode's lifetime is persisted on the partial assistant message committed by `FinishInterruption`, so partial generation time survives interruption (**new**; measured via a new `Buffer.EpisodeDuration`, which works even though the generation goroutine and the interrupt task are different tasks). What deliberately does not count (each is documented in code and docs): - **Local tool execution.** Tool wall time includes idle waits, most importantly `wait_agent` polling a sub-agent chat that already bills its own model invocations; billing the batch would double count, and excluding one tool from a concurrent batch's wall time is ill-defined. Pre-refactor instrumentation did include tool time; this makes the exclusion an explicit product definition instead of a silent regression. - **Failed model calls whose output is discarded** (retried attempts, terminal errors, content-filter refusals). They persist no content, so they bill nothing; billing errs toward undercounting. Notably a stream-silence timeout can burn 10 idle minutes before a retry, which should not be billable "active generation". If product later wants failed attempts billed, that needs a place to persist runtime on error turns (`FinishError` inserts no rows today) and is a deliberate follow-up, not instrumentation drift. - **Ancillary calls that produce no chat messages** (title generation, advisor, turn summaries) and all idle/parked time (`requires_action`, queueing). The definition is documented as `COMMENT ON COLUMN chat_messages.runtime_ms` (migration 000551, surfacing as a Go doc comment on `ChatMessage.RuntimeMs`), on `chatloop.PersistedStep.Runtime`, in the chatd architecture doc, and in the Spend Management docs page. ## Index for the hourly scan None needed: `GetTotalChatMessageRuntimeMsInRange` (#27312) filters an hour-wide `created_at` range, which the existing `idx_chat_messages_created_at` b-tree already serves; the residual `runtime_ms IS NOT NULL` filter applies to one hour of rows. A partial index would add permanent write amplification for a query that runs once an hour. > [!NOTE] > Migration 000551 is also claimed by #27312; whichever merges second renumbers via `fix_migration_numbers.sh`. ## Tests - End-to-end: the existing full-server generation test now asserts `RuntimeMs.Valid` on the committed assistant row (it previously read `.Int64` without checking `.Valid`, so it passed on NULL). - Interrupted turn: full task-level test (real DB, mock clock) asserting the partial assistant message persists the attempt's runtime. - Errored stream: asserts a failed invocation yields no step and no runtime. - Tool-using turn: asserts runtime lands on the assistant row only and tool rows stay NULL. - Compaction: asserts the summarization call duration is recorded and lands on the compaction assistant message only. - `messagepartbuffer.EpisodeDuration` unit coverage. Blocks: CODAGT-843 (B3), CODAGT-838 (D8). --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> Co-authored-by: Hugo Dutka <hugo@coder.com>
About
Coder is a self-hosted platform for running AI coding agents and cloud development environments on infrastructure you control. It works with any cloud, IDE, OS, Git provider, and IDP.
Coder Workspaces
Coder Workspaces are cloud development environments defined with Terraform, connected through a secure Wireguard tunnel, and automatically shut down when not in use. Agents and developers share the same workspace infrastructure.
- Defined in Terraform: Templates describe the infrastructure for each workspace, from EC2 VMs and Kubernetes Pods to Docker containers.
- Any architecture and OS: Support ARM and x86-64 across Windows, Linux, and macOS from a single deployment.
- Managed by admins: Platform teams create and maintain templates that enforce approved images, resource limits, and security policies.
- Accessed from any IDE: Connect through VS Code, JetBrains, Cursor, a web terminal, remote desktop, or SSH.
- Automatic shutdown: Idle workspaces stop automatically to reduce cloud spend, and restart in seconds when needed.
Coder Agents
Coder Agents is a native AI coding agent built into Coder. The agent loop runs in the Coder control plane on your infrastructure, not in the workspace and not in a vendor's cloud. Developers interact with agents through the web UI or the REST API for programmatic and CI-driven workflows.
- Self-hosted agent loop: The control plane handles planning, model calls, and tool dispatch. Workspaces have zero AI awareness.
- No API keys in workspaces: LLM credentials stay in the control plane.
- Any model: Anthropic, OpenAI, Google, Bedrock, or self-hosted endpoints. Switching is a configuration change.
- Governance and cost controls: Centralized model approval, per-user spend limits, and audit logging.
- Open source and inspectable: The full platform is available to audit and extend.
IDE support
You can use:
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Any Web IDE, such as
- code-server
- JetBrains Projector
- Jupyter
- And others
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Your existing remote development environment:
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A file sync such as Mutagen
Why remote development
Provisioning consistent development environments for a large engineering team is difficult. Each developer has preferences for operating systems, editors, and toolchains, and ensuring a reliable build environment across all of them is a maintenance burden. A missed step during onboarding or an unsupported local configuration can cost hours of debugging.
Remote development solves this by moving the environment off the developer's machine and into managed infrastructure. The developer's laptop becomes a portal into the actual compute where work happens. If a device is lost or replaced, access is simply revoked; no source code or credentials are stored locally.
This approach provides:
- Speed: Server-grade hardware accelerates builds, tests, and large workloads without requiring expensive local machines.
- Consistency: Infrastructure tools such as Terraform, nix, Docker, and Dev Containers produce identical environments for every developer.
- Security: Source code stays on private servers. Users and groups are managed through SSO and RBAC.
- Compatibility: Workspaces share infrastructure configurations with staging and production, reducing configuration drift.
- Accessibility: Browser-based IDEs and remote IDE extensions let developers work from any device, including lightweight laptops, Chromebooks, and tablets.
Read more on the Coder blog, the Slack engineering blog, or from Alex Ellis at OpenFaaS.
Why Coder
The key difference between Coder and other platforms is that the entire system, agent loop, control plane, model routing, and workspace provisioning, runs on infrastructure you control.
For agents, this means platform teams can:
- Run the entire agent loop on their infrastructure, with no SaaS dependency for orchestration.
- Define MCP servers, skills, and system prompts centrally so every agent session starts with the same tools, policies, and context.
- Keep LLM credentials out of workspaces entirely.
- Tie every agent action to an authenticated user identity.
- Support air-gapped and restricted-network deployments with self-hosted models.
For workspaces, this means admins can:
- Support any architecture (ARM, x86-64) and operating system (Windows, Linux, macOS).
- Modify pod/container specs, such as adding disks, managing network policies, or setting/updating environment variables.
- Use VM or dedicated workspaces, developing with Kernel features (no container knowledge required).
- Enable persistent workspaces, which are like local machines, but faster and hosted by a cloud service.
Pricing
Coder is free and open source under the GNU Affero General Public License v3.0. All developer productivity features are included in the open source version. A Premium license is available for enhanced support and custom deployments.
How Coder works
Coder workspaces are represented with Terraform, but you do not need to know Terraform to get started. The Coder Registry provides production-ready templates for AWS EC2, Azure, Google Cloud, Kubernetes, and other providers.
Providers and compute environments
Workspaces can include more than just compute. Terraform can add storage buckets, secrets, sidecars, and other resources.
See the templates documentation for details.
What Coder is not
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Coder is not an infrastructure as code (IaC) platform.
- Terraform is the first IaC provisioner in Coder, allowing Coder admins to define Terraform resources as Coder workspaces.
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Coder is not a DevOps/CI platform.
- Coder workspaces can be configured to follow best practices for cloud-service-based workloads, but Coder is not responsible for how you define or deploy the software you write.
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Coder is not an online IDE.
- Coder supports common editors, such as VS Code, vim, and JetBrains, all over HTTPS or SSH.
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Coder is not a collaboration platform.
- You can use Git with your favorite Git platform and dedicated IDE extensions for pull requests, code reviews, and pair programming.
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Coder is not a SaaS/fully-managed offering.
- Coder is a self-hosted solution. You must host Coder in a private data center or on a cloud service, such as AWS, Azure, or GCP.