Michael Suchacz
95a2c2ba02
## Stack Context
This stack removes native chat cost tracking and native chat usage
limits, making the AI Gateway the single source of AI spend data and
budget enforcement.
1. **This PR:** re-back the per-chat cost endpoint with AI Gateway data.
2. Remove native chat usage limits end to end, rewiring the sidebar
indicator to gateway spend.
3. Remove native chat cost tracking end to end, deleting the
Analytics/Spend cost UI.
## What?
`GET /api/experimental/chats/{chat}/cost` summed
`chat_messages.total_cost_micros`, which native chat cost tracking
maintained. It now aggregates AI Gateway interception data instead, and
has no native fallback.
- New `GetAIBridgeChatCost` query, authorized through the root chat so
members can read their own chat's cost without gaining access to raw
interception rows.
- Response fields renamed: `priced_message_count` -> `request_count`,
`unpriced_messages_having_usage_count` -> `unpriced_request_count`.
- The chat summary sidebar keys its cost cache by root chat, and hides
the cost row where the AI Gateway is off or unlicensed. The root cost is
invalidated when a chat leaves an active status and when a generated
title lands, since title generation bills its own gateway request.
`GetChatModelUsageCostByChatID` and the rest of native cost tracking are
untouched here; PR 3 removes them.
## Why?
Native cost tracking duplicates what the AI Gateway already records, and
the two disagree. Repointing the endpoint first means the cost UI keeps
working while the native implementation is deleted later in the stack.
Two behaviour changes follow from gateway semantics and are intentional:
- **Requests, not messages.** The gateway records interceptions, so
counts are requests. Title-generation traffic now counts.
- **Whole-tree totals.** The gateway records the *spawning* chat's ID as
the interception session ID, so a subagent's requests are attributed to
its immediate parent, not always the root. Only a whole chat tree can be
summed, so the query resolves the root and aggregates the tree, and
every chat in a tree reports the same total. Native returned per-subtree
totals.
## Attribution and counting semantics
The aggregate groups token usage per interception before counting, so
the reported numbers are per request even though a request records one
usage row per provider response:
- `RequestCount` counts finished `Coder Agents` interceptions in the
tree, including unpriced ones.
- `UnpricedRequestCount` counts requests with at least one usage row the
gateway could not price. It is a subset of `RequestCount`.
- `TotalCostMicros` omits only unpriced usage, so a partially priced
request still contributes its priced portion. The sidebar therefore says
`Excludes unpriced usage from N request(s)` rather than claiming whole
requests were dropped.
A recorded cost of zero is a free request, not an unpriced one. Usage
without an effective group is excluded, matching what never reached
`ai_user_daily_spend`.
## Authorization
Reads go through `ExtractChatParam` plus `ResourceChat`, with no
cost-specific RBAC widening. `TestGetChatCost/MemberCanReadOwnChat`
covers a scoped `agents-access` member reading their own chat's cost,
and `MemberCannotReadOtherUsersChat` still asserts 404 for a non-owner.
Plain members without `agents-access` cannot create or read chats at
all, so they never reach this endpoint.
## Known limitation
AI Gateway data has its own retention period, 60 days by default and
configured independently of chat retention, so spend for requests older
than that is no longer reported. A chat whose gateway records have all
been purged reports zero cost, which is indistinguishable from genuinely
free usage under this contract. The endpoint documents the caveat;
#27330 documents it on the Spend Management page.
In-flight interceptions are excluded, since cost is only known once the
response is recorded. A chat's cost therefore lags the active turn by
one request.
## Rebase note
Rebased onto `main` after #27579 removed the `ai-gateway-cost-control`
experiment. The per-chat cost row is now gated on the `aibridge` feature
alone, matching how #27579 degated the other cost-control surfaces.
> Mux prepared this PR on Mike's behalf.
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:
-
Any Web IDE, such as
- code-server
- JetBrains Projector
- Jupyter
- And others
-
Your existing remote development environment:
-
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
-
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.
-
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.
-
Coder is not an online IDE.
- Coder supports common editors, such as VS Code, vim, and JetBrains, all over HTTPS or SSH.
-
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.
-
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.