SOLUTIONS / AI ENGINEERING
Trace multi-step agent workflows end to end. Track cost, tokens, and latency by model and provider. Query the same APL your engineers use for system observability. Native MCP so your agents can query the data, too.
WHY YOUR CURRENT AI-OBSERVABILITY STACK CAN'T KEEP UP
LLM features fail across the seams between your model calls and the system that runs them — and today that means a different tool for each seam.
Adds an LLM lens to the Datadog stack, but it sits inside the same Standard Indexing economics. AI workloads generate prompt IDs, model versions, and per-request token counts at exactly the cardinality those economics break on.
Proxy-based logging for LLM calls. Useful for the call-level view, but it doesn't see the surrounding system telemetry: the database query that timed out, the auth service that 503’d, the queue that backed up.
Per-provider, per-API. Multi-provider AI systems show up as fragments across as many dashboards as you have providers. Cross-provider questions get answered by exporting CSVs.
Drop spans and prompts into Snowflake or BigQuery and write SQL. Works for retrospectives, not for incident debugging — and the AI engineer doesn't want to be the warehouse engineer.
These are where evaluation lives, and Axiom doesn't replace them. What changes is that you no longer need a second observability vendor for AI on top of them.
HOW AXIOM SOLVES IT
ONE WATERFALL
Axiom captures AI telemetry on the same event store as the rest of your observability. AI workflow tracing visualizes multi-step agent workflows end to end — prompts, model calls, tools, and services in a waterfall view alongside the database, auth, and queue spans that surround them. The TypeScript AI SDK captures prompts, model, provider, tokens, latency, cost, and custom metadata as structured events on EventDB.
COST, TOKENS, LATENCY
Cost, tokens, and latency track by model, provider, and route automatically, so spend ties to quality and regressions surface early. Long-term active retention keeps every prompt and run queryable across versions and providers, no sampling decisions about which runs to keep. Evaluation and experimentation (in preview) score variant runs over time.
QUERYABLE BY AGENTS
The whole thing is queryable in APL, the same language your engineers use for system observability. AI agents query the same data through the native MCP server using the same primitives. No per-vendor adapter, no custom query bridge.
CUSTOMER HIGHLIGHT
BACKGROUND
Voice agent platform, multi-provider LLM.
PROBLEM
Bad-call debugging spanned three tools.
WHY AXIOM
One event store, APL, MCP.
IMPLEMENTATION
TypeScript AI SDK plus OpenTelemetry.
RESULTS
Cost, latency, traces in one tool.
PARTNERSHIP
Ongoing as platform scales.
“Adding cost calculations to LLM spans from ai-sdk has opened up a whole new world as well! Very simple dashboard and I can see where all our costs are going. ‘Ahh, 40% for just the input tokens for that one feature.’”
— Claras.ai
WHAT CHANGES FOR EACH ROLE
FOR AI ENGINEERS
Stop reconstructing the debugging trail across three vendors. Every prompt, every model call, every tool invocation, every surrounding span lands on the same event store. Trace a bad call end to end in one tool, in one query language.
FOR ENGINEERING LEADERS
Unify AI and system observability on one backbone, one query language, one bill. Cost, tokens, and latency tied to model and provider automatically. Keep the eval platform of choice; remove the second observability vendor.
FOR PLATFORM TEAMS
No second backend to operate for AI telemetry. AI SDK, OpenTelemetry, and Events API all write to the same EventDB. Self-serve enterprise controls cover the AI workloads the same way they cover the rest.
WHAT YOU'LL USE
AI workflow tracing — end-to-end waterfall views across prompts, model calls, tools, services
TypeScript AI SDK — captures prompts, model, provider, tokens, latency, cost, custom metadata
Vercel AI SDK integration — telemetry from the SDK lands in Axiom out of the box
Cost and latency tracking — by model, provider, and route, in dashboards rather than exports
Long-term active retention — replay agent behavior across versions and providers, no re-ingest
Evaluation and experimentationPreview — compare prompts, models, agent strategies over time
Unified with logs, events, traces, metrics — AI spans sit next to surrounding system spans on EventDB
HOW THIS COMPARES
FAQ
SCOPE AND FIT
No. Eval platforms (Braintrust, LangSmith, Langfuse, Arize) live in a different category, and Axiom is built to complement them. Keep the eval platform; Axiom is the observability backbone underneath.
Tools like Weights & Biases, MLflow, Comet, and Neptune sit in ML training workflows, which is a separate scope. Axiom is built for the LLM-powered features software engineers ship into product, not model training.
INSTRUMENTATION
The Vercel AI SDK integration captures telemetry from ai-sdk-instrumented apps out of the box. The Axiom TypeScript AI SDK is the deeper integration when you want to capture prompts, model, provider, tokens, latency, cost, and custom metadata directly.
Yes, OTel-native. AI traces and the surrounding system traces sit on the same event store.
TRACING AND QUERYING
Yes. Prompts, model calls, and tool invocations across OpenAI, Anthropic, or any other provider show up in the same waterfall.
Through the native MCP server, in APL. The same primitives your engineers use.
RETENTION AND EVALUATION
None. Long retention is active retention; historical runs stay queryable in APL, not archived behind a re-ingest.
Currently in preview. Score variant runs over time, compare prompts, models, and agent strategies. Generally available rollout in progress.
Always free to start. Usage-based scaling. Fully managed.