Pricing you can budget for

Tracking never does: events go into a non-blocking in-memory queue and a background thread flushes them every 500ms. Budget enforcement adds one lightweight pre-call check, and it is optional. Turn it off and Capsera is fully out of your call path. Either way, if the backend is ever unreachable, everything fails open and your calls proceed untouched.

Enforcement lives in the SDK, inside your own process. Before each provider call it checks the budgets that match the calling agent; a budget with a block action stops the call before any provider spend, while throttle and downgrade reshape it. If Capsera is ever unreachable the check fails open, so you lose enforcement for that moment, never availability.

Thirteen, across three kinds. Model APIs: Anthropic, OpenAI, Google, Mistral, Cohere, DeepSeek and Kimi. Inference platforms: Groq, Together, Fireworks and Cerebras. Cloud model services: Vertex AI and Bedrock. Sync and async clients both. The SDK patches the provider libraries automatically at init, so there is no proxy, no gateway, and no change to your existing call sites.

Name each agent where it makes its calls, with a decorator or a context manager, then give it a budget envelope with an action at the limit: alert, throttle, or block. Because identity is inherited through the call context, an agent that spawns sub-agents covers them too, so putting every agent on a budget does not mean enumerating them by hand. Budgets can also be scoped to a team or the whole organisation, and the scopes compose.

Yes, and that is the main reason to use Capsera rather than a provider spend limit. A key-level cap stops every agent sharing that credential when one misbehaves; a per-agent envelope stops only the offender and leaves the rest running. Budgets scope to a single agent, a team, or the organisation, over daily, weekly, or monthly periods with calendar-aware resets.

Capsera instruments the provider clients rather than the framework, so calls made through LangGraph, CrewAI, LangChain, AutoGen, LlamaIndex or plain SDK code are all captured, and helpers attribute spend to LangGraph nodes and CrewAI agents and tasks. One honest caveat: CrewAI routes some calls through litellm internally, so direct client patching can miss those. Framework-level interception that closes this gap is the next scanner change.

Never. Prompt analysis extracts structural metadata only: token estimates, message counts, cache usage, context window utilization. The text of your prompts and completions stays in your infrastructure.

Budgets scope to a single agent, a team, or your whole organization, over daily, weekly, or monthly periods with calendar-aware resets. Each budget picks its action at the limit, either alert, throttle, or block, and you get an alert at your threshold (80% by default) plus an exceeded alert at 100%.

Costs are computed from exact per-model pricing tables using decimal arithmetic, based on the real input, output, and cache token counts returned by each API response, not estimates.

Ingestion returns a clear limit response with your current usage and reset date, and the SDK stops sending events until the month rolls over. Hitting the plan limit never blocks your LLM calls. Only tracking pauses.