Stop runaway AI agents before they cost you.
Trace every LLM call, enforce runtime limits, and investigate failures before they become expensive production incidents.
Developer-First Architecture
Instrument in-process with the lightweight SDK without being in the provider request path, or route traffic through the proxy gateway to actively block prompt injections and enforce circuit breakers.
Works with your stack
Native: OpenAI · Anthropic · Gemini | Tested endpoints: Groq · Mistral · Cohere · Ollama
Just drop in our Proxy or SDK
Point a supported OpenAI-compatible client at the Observyze proxy, provide your project ID, and send a test request using the documented authentication headers.
importosfromopenaiimportOpenAI
# Authenticate with an Observyze project API key
client = OpenAI(
api_key=os.environ["OBSERVYZE_API_KEY"],
base_url="https://api.observyze.com/api/v1/proxy/openai/v1",
default_headers={"x-project-id": "your-project-id"}
)
# Supported proxy calls are traced; configured pre-dispatch controls apply.
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "Hello World"}]
)
From SDK install to production in under 2 minutes
Watch a full walkthrough — trace an AI agent, spot a hallucination, and see the circuit breaker trigger — all in real time.
Observe failures, then enforce the controls you configure.
Observyze combines tracing with pre-dispatch input policies and persisted cost or safety circuit state. Output evaluations are asynchronous: they explain completed responses and can change how subsequent requests are handled.
A configured budget can open circuit state and stop subsequent proxy requests after its threshold is crossed.
Asynchronous output evaluation records hallucination and safety signals for investigation and later policy decisions.
Input policies can reject configured prompt-injection patterns before a supported proxy request reaches the provider.
A retry loop can keep spending until a configured budget or circuit rule stops subsequent calls.
This example illustrates budget-driven circuit behavior. Actual timing and savings depend on request cost, state-store availability, and the thresholds configured for a deployment.
Full Visibility Into Every AI Call: Real-Time LLM Observability
See exactly what happened — from the first prompt to the final output. Every latency spike, every token spent, every guardrail triggered.
Before Observyze
Chaotic flows, cost spikes, and failures reaching users.
After Observyze
Structured control, guarded outputs, and optimized system behavior.
Engineered for High-Stakes Agent Workloads
From lightweight trace ingestion to active proxy interception, Observyze gives your team the controls needed to ship generative AI safely.
High-Fidelity Tracing
Capture supported prompt, tool-call, and model-response boundaries. Debug agentic chains in a searchable trace timeline.
Automated Guardrails
Block configured input-policy violations before provider dispatch, then use persisted circuit and budget state to stop subsequent risky or runaway requests.
Prompt Tuning
Compare prompt variants side-by-side. Identify regressions, tighten instructions, and feed improvements back into production.
Cost Attribution
Attribute provider-reported or estimated token cost to a model, project, and request context. Configure budgets that can stop subsequent calls.
Two Ways to Integrate
Choose pure in-process telemetry with the SDK, or active runtime interception and circuit breakers with our Proxy Gateway.
OBSERVE via SDK
Telemetry & In-Process Controls
Wrap your favorite LLM client with a single line of code. Telemetry is batched and sent asynchronously outside your critical request path.
OBSERVE + PROTECT via Proxy Gateway
Active Runtime Interception & Circuit Breakers
Route requests through our high-performance proxy gateway by simply updating the base URL in your existing client configuration.
OBSERVE → DETECT → STOP → INVESTIGATE
A closed-loop runtime control architecture designed to catch agent loops, budget overruns, and hallucinations before they become production incidents.
Distributed Tracing & Token Costs
Capture full multi-turn agent conversations, tool calls, latency percentiles, and exact per-step token and dollar cost attribution.
Evals, Injections & Anomalies
Run asynchronous hallucination checks, catch prompt injection patterns pre-dispatch, and detect abnormal token spikes automatically.
Execution Budgets & Circuit Breakers
Configure per-trace spend caps and Redis-backed circuit breakers that instantly halt runaway retry loops before bills escalate.
Trace Replay & Root-Cause Debugging
Inspect exact prompt payloads, retrieved context chunks, tool call arguments, side-by-side prompt diffs, and error stack traces.
Your AI operations — in one dashboard
Traces, costs, latency, evaluations, and alerts in a single interface. No tab-switching, no guessing.
Built for the teams actually shipping AI
Agent Builders
Debug multi-step agent workflows. See every tool call, retrieval, and reasoning step in a single trace timeline.
See how it worksAI-Powered Products
Ship LLM features with built-in cost controls, safety guardrails, and latency monitoring your product team can rely on.
See how it worksGrowing Teams
Scale from prototype to production with audit trails, alerting, and per-project cost attribution as your AI footprint grows.
See how it worksFull Runtime Control. Free During Early Access.
All new accounts receive 90 days of complimentary Pro access. Up to 100,000 traces/mo with execution budgets, circuit breakers, and evaluations. No credit card required.
Observatory Pro
Planned pricing after Early Access ($79/mo)
Complete runtime control, tracing, and evaluation for AI engineering teams.
Galactic Enterprise
Planned enterprise tier pricing
High-volume throughput, custom retention, and dedicated VPC / on-prem deployment.
Got questions? We've got answers.
Learn how Observyze helps you trace, govern, and optimize your generative AI workloads.
Loved by AI Engineers
“The trace visibility was really useful for my RAG bot, especially for checking the user query, context sent to the LLM, model response, latency, errors, and cost.
The biggest value for me is debugging wrong answers and figuring out whether the issue is with retrieval, prompts, the model, or user input... traces, cost tracking, and failure debugging already look very useful.”
Take Control of Your AI Systems Today
Deploy observability, guardrails, and optimization in one motion so your team can ship AI with confidence.
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