Subscriptions
Per-seat plans across coding assistants, IDEs, and team tools.
For B2B software teams operating a fragmented AI stack
See what each AI coding workflow actually costs.
Knotic connects tokens, estimated cost, latency, provider, model, tools, and context to the individual call and the workflow it belongs to.
Provider dashboards show invoices. Workflow visibility helps Engineering understand what created the spend.
HQ Monitoring
Workflow cost explorer
Workflow
Checkout validation
Calls
3
Estimated cost
$0.24
Call 03 · Retry
Repair checkout validation
- Input
- 18.4k tokens
- Output
- 1.2k tokens
- Latency
- 8.4s
- Est. cost
- $0.12
14 files · repository memory · 2 tool results
Retry linked to the same workflow intent
Call 01
Provider B · Standard model · $0.04
Call 02
Provider A · Premium model · $0.08
The real operating cost
The seat price is only one line item.
As adoption spreads, AI coding spend becomes a system of subscriptions, inference, credentials, routing decisions, and repeated work.
API consumption
Usage-based inference billed separately by one or more providers.
Personal and team keys
BYOK, centrally managed credentials, and keys held by individual developers.
Premium model drift
Routine work gradually routed to higher-cost models without an explicit policy.
Retries
Repeated calls after failures, weak outputs, or incomplete context.
Oversized context
Files, tool results, and repository knowledge included beyond what the task needs.
Duplicated tools
Overlapping products and licenses solving the same workflow across the team.
Provider invoice vs workflow visibility
The invoice answers how much. The workflow explains why.
Provider billing views and workflow telemetry solve different questions. The goal is not to replace vendor invoices, but to connect aggregated spend back to engineering activity.
Provider view
Total usage by vendor
- Total usage by vendor
- Billing period
- Account or organization
- Aggregated model consumption
Workflow view
Cost connected to engineering activity
- Individual call and workflow
- Provider and model
- Input and output tokens
- Latency and retry path
- Tools and files involved
- Context composition
- Estimated call cost
Exact fields depend on the instrumentation, provider, and workflow configuration available to the team.
The central mechanism
HQ Monitoring turns usage into operational evidence.
Review AI coding activity at the level where teams can act: the call, the workflow, the provider decision, and the context that shaped the request.
Explore HQ Monitoring- 01
Call
The request inside the engineering workflow.
- 02
Provider + model
Where the request was routed and which model handled it.
- 03
Tokens + cost
Usage and estimated cost connected to that specific call.
- 04
Latency + retries
How long the flow took and where work repeated.
- 05
Tools + context
Which tools, files, and context contributed to the payload.
Prevent cost before the call
Remove noisy context before it consumes tokens.
Context Lens exposes the payload and token budget before send. Developers can inspect files, memory, and other context blocks while there is still time to trim what the task does not need.
- Inspect the assembled payload before the provider receives it.
- Identify irrelevant, stale, or duplicated context.
- Keep context quality and cost decisions inside the engineering workflow.
AI Coding Cost & Provider Audit Sheet
Inventory the stack before optimizing the workflow.
Use one shared view to map licenses, API consumption, keys, workflow ownership, and the visibility gaps that make monthly spend difficult to explain.
Request the audit sheetThe sheet is provided on request. This page does not link to a placeholder download.
Audit structure
One row per tool or provider setup
01Tool
02Provider
03Model
04Users
05Seat cost
06API cost
07Keys
08Workflow owner
09Token visibility
10Retries
11Context size
12Monthly estimate
What to measure first
Start with four signals your team can inspect and own.
Establish a baseline from your own workflows. No external benchmark is required to find routing, retry, or context decisions worth reviewing.
01
Cost per workflow
Group calls around a real engineering outcome, then review the provider, model, and repeated work behind it.
Which workflows consume the most, and who owns the decision?
02
Payload size
Inspect the input context attached to each request, including files, memory, and tool output.
Is every context block relevant to the task?
03
Retry frequency
Track when a workflow repeats because of an error, incomplete answer, or avoidable context gap.
Where does the team pay twice for the same intent?
04
Model/provider mix
Review which providers and model tiers handle each category of engineering work.
Is routing deliberate, or simply the default?
Evaluation questions
Answers for Engineering, Platform, and Finance.
Is this another provider dashboard?
No. Provider billing views remain useful for invoices and vendor-level consumption. Knotic HQ Monitoring adds the engineering workflow layer by connecting a call to its provider, model, tokens, latency, tools, context, and estimated cost.
Can Knotic work with multiple providers?
Yes. Knotic supports multi-provider workflows so teams can make provider and model choices visible in one operating surface. The available configuration depends on the providers and models your team enables.
Does Context Lens help reduce cost?
Context Lens helps teams inspect and trim noisy or irrelevant context before a request is sent. That can reduce unnecessary token consumption, but the outcome depends on the workflow and no fixed saving is assumed.
Can teams use BYOK?
Yes. Teams can use bring-your-own-key workflows. The right setup for personal keys, shared credentials, and provider routes should be defined during implementation.
Where should we start?
Start by inventorying tools, providers, models, users, keys, seat spend, and API spend. Then select a small number of important workflows and measure cost, payload size, retries, and routing decisions.
Make the stack explainable
Connect the invoice to the work.
Start with the audit sheet, then assess the workflows where provider choice, retries, or context size deserve a closer look.