What is Sedai and how does it differ from other LLM observability tools?
Sedai is an optimization platform that brings governance, observability, and intelligent routing to every LLM call. Unlike most LLM observability tools that only display metrics or propose changes, Sedai acts autonomously in production: it reroutes, retries, or fails over to another model when a call becomes expensive, slow, or fails. Sedai is designed to optimize the agents you've already built, running as an intelligent layer that captures cost, token usage, and latency across providers. Its Smart Routing feature builds a dedicated router for each agent from real production traffic and sends each prompt to the best-fit model for your cost, latency, or accuracy goals. Note: Sedai is not a tracing or evaluation workbench and does not provide deep trace debugging or annotation queue management like Langfuse or Braintrust. Provider coverage is currently OpenAI, AWS Bedrock, Vertex AI, and Azure Foundry, with more to come. [Source]
How does Sedai's autonomous optimization work for LLM and agent applications?
Sedai acts on observability data in real time by rerouting, retrying, or failing over LLM calls based on cost, latency, or reliability issues. Its Smart Routing builds a per-agent router from actual production traffic, identifies a Pareto set of candidate models, and sends each prompt to the best fit for your goals. Sedai translates requests and responses between provider formats, allowing applications written for one provider (e.g., OpenAI) to be routed to another (e.g., Anthropic or Google) without code changes. Governance policies control model access, and built-in reliability handles retries and cross-provider fallbacks. Note: Sedai is focused on governance, routing, and reliability, not deep trace debugging or hallucination scoring. [Source]
What are the main benefits of using Sedai for LLM observability and optimization?
Sedai provides real-time, autonomous action on observability data, reducing manual intervention and operational toil. Key benefits include: traffic-trained routing for each agent, consolidated cost and latency tracking across providers, zero-code adoption (one import, no rewrites), and governance policies for model usage. Sedai can reduce cloud costs by up to 50%, improve application performance by reducing latency by up to 75%, and deliver up to 6X productivity gains for engineering teams. Note: Sedai is best for teams wanting observability data acted on autonomously; teams needing deep trace debugging or custom evaluation suites may require additional tools. [Source]
Features & Technical Capabilities
What integrations does Sedai support for LLM and agent observability?
Sedai integrates with OpenAI, AWS Bedrock, Vertex AI, and Azure Foundry for LLM and agent optimization. Integration is a one-line import, requiring no code rewrites, and Sedai translates requests and responses between provider formats. For broader cloud optimization, Sedai also integrates with 12 APMs (including Prometheus, Datadog, AWS CloudWatch, Azure Monitor, Google Cloud Monitoring), Kubernetes autoscalers (HPA/VPA, Karpenter), CI/CD tools (GitHub, GitLab, Bitbucket, Terraform), ITSM tools (ServiceNow, PagerDuty, Jira), and notification platforms. Note: Provider coverage for LLMs is still expanding; check Sedai's documentation for the latest supported providers. [Source]
How easy is it to implement Sedai for LLM observability and optimization?
Initial setup for Sedai can be completed in as little as 15 minutes using agentless or agent-based deployment. For AI Agent Optimization, implementation typically takes two to three weeks. Integration for LLMs is a one-line import across supported providers, with no code rewrites required. Sedai offers personalized onboarding, extensive documentation, and a community Slack channel for support. Note: Some advanced features or environments may require additional configuration. [Source]
What security and compliance certifications does Sedai have?
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements for data protection and compliance. This certification ensures Sedai meets industry standards for handling sensitive data. For more details, visit the Sedai Security page. Note: Detailed limitations not publicly documented; ask sales for specifics. [Source]
Pricing & Plans
How is Sedai priced for LLM observability and optimization?
Sedai uses a resource-based pricing model, where costs are determined by the resources optimized and the value delivered. For Kubernetes environments, Sedai offers tailored pricing. All costs are transparently outlined on Sedai's pricing page, with no hidden fees. Customers connecting their cloud billing account will see discounts (e.g., Reserved Instances or Savings Plans) factored into cost and savings calculations. Note: For specific pricing details for LLM optimization, contact Sedai's sales team. [Source]
Competition & Comparison
How does Sedai compare to Langfuse for LLM observability?
Langfuse is an open-source LLM observability platform (MIT license) focused on tracing, prompt management, and evaluations. It is self-hostable and framework-agnostic, with broad support for OpenAI SDK, LangChain, LlamaIndex, and OpenTelemetry. Langfuse automates tracing and evaluation but does not act on findings autonomously—application remediation remains external. Sedai, by contrast, acts autonomously in production, rerouting, retrying, and failing over LLM calls to optimize cost, latency, and reliability. Choose Sedai if you want observability data acted on in production; choose Langfuse if you need deep trace debugging, prompt management, and self-hosted control. Note: Langfuse requires SDK or OTel instrumentation and does not provide autonomous optimization. [Source]
How does Sedai compare to Braintrust for LLM evaluation and observability?
Braintrust is a proprietary platform built around evaluations, combining an eval framework, logging, a prompt playground, and an assistant called Loop. It automates analysis and proposes prompt changes, but a human must confirm changes by default. Sedai, in contrast, acts autonomously in production, rerouting and optimizing LLM calls without human intervention. Choose Sedai if you want autonomous action on observability data; choose Braintrust if you need evaluation-driven workflows and CI/CD quality gates. Note: Braintrust is closed-source and self-hosting is only available on the Enterprise tier. [Source]
What are the limitations of Sedai compared to other LLM observability tools?
Sedai is not a tracing or evaluation workbench and does not provide features like custom test suite authoring or annotation queue management (as found in Langfuse or Braintrust). Its analysis depth is focused on governance, routing, and reliability rather than deep trace debugging or hallucination scoring. Provider coverage is currently limited to OpenAI, AWS Bedrock, Vertex AI, and Azure Foundry, with more providers planned. Note: Teams needing deep trace debugging or custom evaluation workflows may require additional tools alongside Sedai. [Source]
Use Cases & Customer Success
Who should use Sedai for LLM observability and optimization?
Sedai is best for teams that want observability data acted on autonomously in production, especially those running agents across multiple providers who need routing, reliability, and governance in one layer. It is suitable for IT/cloud operations, SREs, FinOps, technology leaders, and platform engineering teams seeking to reduce operational toil, optimize costs, and improve reliability. Note: Teams needing deep trace debugging or custom evaluation workflows may require additional tools alongside Sedai. [Source]
Can Sedai be used alongside other LLM observability and evaluation tools?
Yes, Sedai is designed to run alongside existing tracing and evaluation tools such as Langfuse, MLflow, Arize Phoenix, Braintrust, and others. Sedai acts on observability data in production, while other tools may provide deep trace debugging, prompt management, or evaluation workflows. Most teams use more than one tool together, choosing the right tool for each job. Note: Sedai does not replace tracing or evaluation workbenches. [Source]
What customer success stories are available for Sedai?
Notable customer success stories include KnowBe4, which achieved up to 50% cost savings and reduced average response time from 18.5 seconds to 80 milliseconds (a 99.5% reduction) using Sedai for AWS Lambda optimization. Palo Alto Networks saved $3.5 million through Sedai's autonomous optimization. Belcorp reduced AWS Lambda latency by 77%, and Campspot achieved a 34% reduction in latency. For more details, see Sedai's resources page. Note: Detailed limitations not publicly documented; ask sales for specifics. [Source]
LLM observability tools capture what happens inside your LLM and agent applications: the traces, token costs, latency, errors, and output quality of every model and agent call. Teams use them to both debug failures and control spend.
Key Takeaways
Most of these tools observe, evaluate, or execute predefined controls; only one acts autonomously on cost, latency, and reliability in production
Open source is a real option: Langfuse, MLflow, Helicone, Portkey, and DeepEval are openly licensed; Arize Phoenix is source-available
The tools are categorized into five jobs: Tracing platforms, evaluation-first tools, gateways, enterprise monitoring, and full-stack with autonomous action
Most teams run more than one tool, so they should evaluate which tool is right for their specific job
Why LLM Observability Matters Now
LLM observability has shifted from optional to expected because AI systems are now in production, spending real money, and failing in ways traditional monitoring was never built to catch. Gartner expects 60% of software engineering teams to adopt AI evaluation and observability platforms by 2028, up from 18% in 2025, a jump that tracks how quickly agents and LLM features are reaching customers.
Debugging is the main pressure point because LLM and agent behavior is non-deterministic. In a 2025 study of 38 practitioners, 79% named non-deterministic execution as a major problem. And because ordinary logs do not capture the complete agent flow, 77% of practitioners in the study reported difficulty diagnosing root causes.
Cost adds a second problem, since token spend accumulates across providers, models, and teams. Without per-call attribution, a spike is hard to trace back to its source.
What makes this practical to solve now is standardization. Many of the tools below build on OpenTelemetry's GenAI semantic conventions, which cover OpenAI, Anthropic, AWS Bedrock, and Azure AI Inference, so instrumentation is becoming portable rather than locked to one vendor.
Every tool on this list had to be production-ready and under active development as of July 2026. Beyond that baseline, we scored each tool on six criteria:
Scope. Whether the tool covers tracing, evaluation, gateway routing, or the full stack
Integration effort. Whether adoption requires code changes, an SDK, an OpenTelemetry exporter, or a base-URL swap
Self-serve or sales-gated. Whether you can start for free or have to talk to sales
Multi-provider support. Whether it works across OpenAI, Anthropic, Bedrock, and others
Acts or displays. Whether the tool acts on its findings or surfaces them for a human to act on
Enterprise governance. Whether it offers access control, PII handling, and deployment options regulated teams need
We also organized the tools into five distinct categories:
Full-stack observability with autonomous action. Tools that act on what they observe, not just display it
Open-source observability platforms. Self-hostable tracing, evaluation, and prompt management you run yourself
Evaluation-first platforms. Tools that measure output quality and gate releases on it
Gateways with observability. Tools that sit in the request path and add cost tracking, caching, and routing
Ecosystem and enterprise. Deep ties to a framework like LangChain, or enterprise-grade monitoring and governance
We describe every tool using its own documentation and list real limitations for each one. And because most teams run more than one of these tools together, we note where a tool complements another rather than replacing it.
Sedai is an optimization platform that brings governance, observability, and intelligent routing to every LLM call. Sedai is unlike other LLM observability tools because it autonomously takes action to reduce the cost and improve performance of AI agents, going beyond simple metric reporting.
How It Works
When a model call turns expensive, slows down, or fails, Sedai reroutes, retries, or fails over to another model autonomously. It is designed to optimize the agents you've already built, so it sits alongside your existing stack rather than replacing your tracing or eval tools.
Sedai runs as an intelligent layer on every call, capturing cost, token usage, and latency directly from its SDK so teams get a single source of truth across every provider instead of reconciling separate vendor bills.
Its Smart Routing then acts on that data. Rather than static, rule-based routing, it:
Builds a dedicated router for each agent from that agent's real production traffic
Identifies a Pareto set of candidate models
Sends each prompt to the best fit for your cost, latency, or accuracy goals
Because the router translates requests and responses between provider formats on the fly, an application written for OpenAI, for example, can be routed to a better-fit model on Anthropic or Google without refactoring code.
Governance policies control which models each team can use, and built-in reliability handles retries and cross-provider fallbacks.
Integration is a one-line import across OpenAI, AWS Bedrock, Vertex AI, and Azure Foundry, with more to release in the future.
Key Features
Acts autonomously in production: Reroutes, retries, and fails over instead of alerting a human
Traffic-trained routing: Smart Routing builds a per-agent router from real production traffic, not generic benchmarks, with Manual and Auto modes
Single source of truth: Consolidated cost, usage, and latency across every provider, project, and model
Governance: Org and project-level policies control which models each team can use
Zero-code adoption: One import, no rewrites, with cross-provider API translation so you can switch models without touching code.
Limitations
Not a tracing or eval workbench: You cannot author your own test suites or manage annotation queues the way you can in Langfuse or Braintrust
Provider coverage still expanding: OpenAI, AWS Bedrock, Vertex AI, and Azure Foundry today, with more to come.
Analysis depth: Built for governance, routing, and reliability rather than deep trace debugging or hallucination scoring
Why Choose Sedai
You want observability acted on autonomously and safely in production, not just shown on a dashboard.
You run agents across several providers and want routing, reliability, and governance in one layer
You want per-agent routing tuned to your traffic without manual benchmarking or code changes
Best For
Teams that want observability across providers and want the data acted on autonomously in production.
Open-source observability platforms
Langfuse
Langfuse is an open-source LLM observability platform (MIT) offering tracing, prompt management, evaluations, a playground, and datasets. It suits teams that want a self-hostable backend they fully control.
The core does not impose product-level usage caps, although teams pay for and manage the underlying infrastructure. Run it self-hosted on Docker or Kubernetes, or use the managed Langfuse Cloud. Its evaluations cover both LLM-as-a-judge and code-based checks.
You can connect Langfuse through:
Its native Python and JavaScript SDKs
Drop-in integrations for the OpenAI SDK and more than 100 libraries and frameworks
A standard OpenTelemetry exporter
That OpenTelemetry support keeps you flexible, since the same instrumentation can point at another backend later instead of locking you in.
Key Features
Open source (MIT): Some enterprise modules require a license key.
Complete platform: Tracing, prompt management, evaluations, playground, and datasets in one place
Deploy anywhere: Self-hostable via Docker or Kubernetes, or use Langfuse Cloud
Limitations
Open-core licensing: Some enterprise modules require a license key
No zero-code path: Adoption means SDK or OpenTelemetry instrumentation
Displays and evaluates: Automates tracing and evaluation; application remediation remains external
Arize Phoenix
Arize Phoenix is an OpenTelemetry-native observability and evaluation tool that runs from a single pip install or Docker container. It fits engineers who want a lightweight tracing and eval workbench during development.
How It Works
Phoenix is built on OpenTelemetry and OpenInference instrumentation, which makes its traces portable.
It documents four features:
Tracing that captures model calls, retrieval, and tool use step by step
Evaluation through LLM-based evaluators, code checks, or human labels
Prompt engineering with versioning and replay
Datasets and experiments for comparing versions systematically
Key Features
Lightweight start: Pip install or a single Docker container
Portable traces: OpenTelemetry- and OpenInference-native
Full dev workbench: Tracing, evaluation, prompt engineering, and dataset experiments
Free to self-host
Limitations
Production limitations:Managed alerting and online evals live in commercial Arize AX
Source-available, not open source: Elastic License 2.0 allows self-hosting but ELv2 is not an OSI-approved open-source license
MLflow
MLflow is the open-source ML platform whose tracing feature is a fully OpenTelemetry-compatible way to observe LLM and agent applications. It fits teams already running MLflow who want agent traces in the same place as their experiments.
How It Works
MLflow Tracing captures the inputs, outputs, and metadata of each intermediate step in a request. Instrumentation can be a single line, for example mlflow.openai.autolog(), or manual decorators for custom spans. It integrates with OpenAI, LangChain, LlamaIndex, and DSPy.
MLflow also supports human feedback on traces and systematic evaluation, and it is licensed under Apache 2.0 with no feature gating. Tracing was introduced in MLflow 2.14.0 and expanded for generative AI in MLflow 3.0.
Key Features
Apache 2.0, fully free: No feature gating
One-line auto-tracing: For example, mlflow.openai.autolog(), plus manual decorators
OpenTelemetry-compatible: Integrates with OpenAI, LangChain, LlamaIndex, and DSPy
Mature ecosystem: Reuses MLflow's experiment-tracking foundation
Limitations
You run the server: Self-hosted tracking-server model unless you use Databricks
Newer at LLM observability: Tracing is recent next to its classic ML heritage
Focused on trace capture and evaluation: Alerting/anomaly detection come from separate tooling or managed Databricks
Evaluation-First Platforms
Braintrust
Braintrust is a proprietary platform built around evaluations, combining an eval framework, logging, a prompt playground, and an assistant called Loop. It fits teams that want quality evaluations wired into their CI/CD pipeline.
How It Works
Braintrust’s core primitive combines a dataset, a task, and scorers into a repeatable eval. It automatically wraps OpenAI and Anthropic clients without requiring manual instrumentation around each call.
As for the assistant, Loop, it:
Analyzes logs and traces
Generates SQL filters from natural language
Creates datasets from log patterns
Generates scorers
Proposes prompt changes.
Per Braintrust’s Loop documentation, Loop asks for confirmation before executing certain actions by default, and auto-accept is an option a user can turn on. So Braintrust automates the analysis and proposes changes, but a human must still approve them.
Key Features
Evaluation-first workflow: Dataset, task, and scorers, built to fit CI/CD
No-code trace capture: Auto-wraps OpenAI and Anthropic clients
Loop assistant: Analyzes logs and proposes prompts, scorers, and datasets
Developer tooling: Prompt playground and multi-language SDKs
Limitations
Closed-source: Self-hosting only on the Enterprise tier
Free tier caps: Starter limits processed data and score volume
Proposes, doesn't act: A human confirms Loop's changes by default
Confident AI
Confident AI is the hosted platform built on top of DeepEval, the open-source LLM evaluation framework. It fits teams that want a rigorous, code-first evaluation suite with score-regression gates.
How It Works
DeepEval is open source under Apache 2.0. It ships more than 50 research-backed metrics from general scorers like G-Eval and DAG to agent-specific ones like task completion and tool correctness.
Evaluations can be written as standard pytest-style tests, so teams can run them automatically on every code change and fail the build when output quality drops below a configured threshold.
Everything above is the open-source framework you run yourself. Confident AI is the hosted version on top, adding the pieces teams need to run evals in production, like shared datasets, release-blocking quality gates, and red-teaming.
Key Features
Built on DeepEval (Apache 2.0): More than 50 research-backed metrics
Code-first workflow: Pytest-style tests with score-regression gates
Agent-ready metrics: Task completion, tool correctness, and red-teaming
Local-first evaluation
Limitations
Evaluation-first, not full APM: Built for LLM tracing and evals, not infra monitoring
Judge-model overhead: LLM-as-a-judge metrics carry cost and variance
Sales-gated extras: Advanced and on-premises features require a sales conversation
Galileo
Galileo is a proprietary reliability platform that observes, evaluates, and guardrails GenAI and agent applications using its own small evaluation models. It fits teams that want to evaluate all of their production traffic cheaply.
How It Works
Galileo's Luna-2 evaluation models stay under 200ms even when running 10 to 20 checks at once on L4 GPUs. And at $0.12 per million tokens versus $5.00 for a frontier model like GPT-5.4, they cost roughly 98% less than using that model as the judge.
Its Insights engine automatically clusters similar failures, surfaces root-cause patterns, and prescribes fixes such as adding few-shot examples; a single click can generate an evaluator from an identified issue. Guardrails can also be applied.
Key Features
Low-cost, low-latency evaluation: Luna-2 stays under 200ms even at 10 to 20 checks at once, at about 98% less than a frontier-model judge ($0.12 vs. $5.00 per million tokens)
Automated root cause: Insights engine auto-clusters failures and surfaces patterns
No-code guardrails: Applied without changes to agent code
Free tier available
Limitations
Closed-source: Trace-based pricing scales with volume
Recommends, doesn't implement: Insights prescribes fixes, engineers apply them
Gateways with observability
Helicone
Helicone is an open-source observability platform and AI gateway that instruments LLM apps through a simple base-URL change. It fits teams that want cost tracking and caching live in minutes.
How It Works
Helicone offers two modes: gateway and async logger.
As a gateway, it sits in front of your provider and adds edge caching with savings shown in the dashboard, plus automatic fallbacks and rate limiting. As an async logger, its SDK records requests after they reach the provider, which keeps Helicone off the critical path but gives up the gateway features. Both modes include cost and latency tracking with prompt management.
The platform is Apache 2.0 licensed and self-hostable.
Key Features
One-line integration: A base-URL swap
Open source (Apache 2.0) and self-hostable
Gateway feature set: Cost tracking, edge caching, rate limiting, and fallbacks across 100+ models
Limitations
Inline dependency: Gateway mode sits in the critical request path, adding potential latency and another availability dependency
Observes and controls requests: Does not act on your application logic
Portkey
Portkey is an AI gateway with an open-source core that sits between your application and more than 1,600 models. It fits teams that want resilient multi-provider routing with built-in controls.
Integration is a base-URL swap against an OpenAI-compatible endpoint or its SDK. The gateway is MIT-licensed and self-hostable through a single command, Docker, Kubernetes, or a VPC, and the managed platform adds a hosted layer.
Key Features
Open-source gateway (MIT): Self-hostable, including in a VPC
Resilient routing: Fallbacks and load balancing across 1,600+ models
Guardrails built in: 20+ deterministic and LLM-based guardrails, including PII redaction and prompt injection
Base-URL swap integration
Limitations
Tier-limited retention: The free tier caps logs at 3-day retention and 10k per month; longer retention and alerts require paid tiers
Tier-gated budgets: Budget controls sit in higher tiers
Inline middleware: Every request flows through it
Not autonomous: Routing is configuration-driven
Watching LLMs Isn't Optimizing Them
Most LLM observability tools tell you what's going wrong. Sedai fixes it for you.
Ecosystem and Enterprise
LangSmith
LangSmith is LangChain's proprietary observability and evaluation platform, with native integration for LangChain and LangGraph. It fits teams building on that ecosystem.
How It Works
LangSmith provides:
Trace-level debugging
Offline and online evaluations
Dataset management
Monitoring dashboards
Annotation queues
A prompt hub
It works with or without the LangChain framework and supports OpenTelemetry natively, so you can point any OTel exporter at its endpoint. Its strongest use is turning production issues into offline test cases within the LangChain workflow.
Key Features
Deepest LangChain and LangGraph integration
Full eval loop: Trace-level debugging plus offline and online evaluations
Expert review tooling: Annotation queues, dataset management, and monitoring dashboards
Native OpenTelemetry support: Works beyond LangChain
Limitations
Self-hosting: Enterprise-only and requires teams to operate supporting infrastructure
Ecosystem-optimized: Built first for LangChain workflows
Detects and surfaces: Humans act on what it finds
Datadog LLM Observability
Datadog LLM Observability brings LLM and agent tracing into the broader Datadog platform, correlating it with existing APM and infrastructure monitoring. It fits teams already on Datadog.
How It Works
Datadog captures end-to-end traces of LLM chains and agent workflows. At production scale, it clusters conversations by topic and flags anomalies, so quality problems surface without anyone reading individual traces. It also covers the security side, scanning prompts and responses for sensitive data, redacting it, and detecting prompt injections.
Instrumentation is an auto-instrumenting Python SDK, and the product natively supports OpenTelemetry’s GenAI semantic conventions, mapping attributes like token usage and provider name automatically.
Key Features
One platform: Correlates LLM traces with existing APM and infrastructure monitoring
Production-scale analysis: Topic clustering and anomaly detection
Security built in: PII redaction and prompt-injection detection
Standards-native: OpenTelemetry GenAI conventions support with an auto-instrumenting SDK
Limitations
Platform lock: Requires Datadog
Cost scaling: Priced per LLM span, so cost tracks LLM-call volume (agent and tool spans aren't billed).
Surfaces and evaluates: Remediation is a human or ops task
Fiddler
Fiddler is a proprietary enterprise platform for AI observability and security across traditional ML models, LLM applications, and multi-agent systems. It fits regulated organizations where AI decisions carry legal or safety consequences.
How It Works
Fiddler's Centor Models scores outputs for safety, privacy, and quality using purpose-built models. The checks include toxicity, jailbreaks, PII, and hallucination.
Its purpose-built Centor evaluation models return results in under 80ms with no external API calls, and its guardrails integrate with NVIDIA NeMo Guardrails to intercept prompts and responses at runtime.
It retains the drift, explainability, and model-monitoring capabilities of its ML heritage. Detection, scoring, and guardrail interception run automatically.
Key Features
Enterprise trust and safety: Governance across ML and LLM systems
Trust Service scoring: Built-in safety, privacy, and quality metrics (toxicity, PII, jailbreaks, hallucination).
Sub-80ms evaluation: Centor models return safety, quality, and privacy scores in under 80ms with no external API calls
