The Best Langfuse Alternatives: Smarter Ways to Track Your AI Work in 2025

Hey, if you’re knee-deep in building AI apps and feeling the pinch from clunky tracing tools, you’re not alone. Tools like Langfuse do a solid job at watching over your large language models-logging prompts, spotting bottlenecks, all that good stuff-but sometimes you just need something that fits your workflow better, maybe with easier setup or beefier analytics. I’ve been there, sifting through options, and in 2025, the landscape’s packed with sharp alternatives from leading AI platforms. These aren’t just swaps; they’re upgrades that save time and headaches. Let’s dive into five standout ones that teams swear by for observability, evaluation, and keeping things humming without the hassle.

1. Snippets AI

At Snippets AI, we built this as a central spot to handle all the prompts your group uses with AI tools. It lets you grab any prompt and drop it right where you need it, skipping the old habit of digging through docs or notes. We keep things simple with an organization that puts everything in one place, so prompts stay easy to find and pull up fast. Voice comes in handy too, if typing feels like a drag, turning spoken ideas straight into usable snippets without the fuss.

We noticed how easy it is to lose track of those little bits of magic that make AI responses click, so workspaces help bundle them up, whether private for your own stuff or public ones to browse and borrow from. Desktop focus makes sense for us, since that’s where most deep work happens anyway. No big evaluations or tracing here, just straightforward ways to reuse what works and cut the repetition.

Key Highlights

• Instant selection and insertion of prompts
• Organization in dedicated workspaces
• Voice input for creating prompts
• Access to public workspaces for reuse
• Desktop-optimized interface

Who it’s best for

• Groups juggling multiple AI prompts daily
• Folks tired of copying from scattered docs
• Users who prefer voice over typing for ideas

Contact Information

• Website: www.getsnippets.ai
• Email: team@getsnippets.ai
• Address: Skolas iela 3, Jaunjelgava, Aizkraukles nov., Latvija, LV-5134
• LinkedIn: www.linkedin.com/company/getsnippetsai
• Twitter: x.com/getsnippetsai

2. Lunary

Lunary streamlines the process of managing and refining LLM chatbots, focusing on how they hold up in real-world scenarios. Developers can log prompts, monitor live user interactions, and dive into traces to pinpoint issues like errors or sluggish responses. The platform offers analytics on model costs and conversation topics, with options to replay chats or score outputs directly in custom dashboards. It sets up quickly, whether hosted on your servers with Docker or in the cloud, and its open-source nature lets curious folks inspect the code.

It shines in use cases like internal help desks, where it pulls from knowledge bases for fast answers, or customer-facing bots that parse documents for precise replies. For autonomous agents, real-time alerts catch hiccups during tasks like generating reports. Prompt templates are shareable, letting non-technical users experiment with versioning or run A/B tests. Security features like PII masking and role-based access keep data safe, especially for complex workflows involving text, voice, or images.

Key Highlights

• Logs traces and errors for agent debugging
• Tracks model costs and user satisfaction metrics
• Supports prompt versioning with A/B testing
• Includes PII masking and role-based access
• Open-source core with self-hosting via Kubernetes
• Integrates one-line with OpenAI and similar clients

Who it’s best for

• Folks building chatbots for support or internal queries
• Teams needing quick replays of user sessions
• Developers handling autonomous agents in production

Contact Information

• Website: lunary.ai
• Email: security@lunary.ai
• Twitter: x.com/lunary_hq

3. Helicone

Helicone serves as a gateway for LLM requests, simplifying connections to various models while keeping a close eye on performance. By wrapping around providers like OpenAI or Gemini with just a line of code, it logs requests and sessions to help debug odd outputs or trace prompt sequences. Analytics break down user flows through segments and properties, making it easy to spot patterns without complex setup. Its cloud-based gateway uses passthrough billing, so there are no extra charges.

It fits well in apps that demand reliability, like those juggling multiple models or testing prompts in a playground. Sessions are organized for quick review, and evaluators check datasets to catch issues early. Developers only need to tweak a base URL to start logging, whether in TypeScript, Python, or curl. A seven-day free trial, no card required, lets you explore before opting into paid plans that unlock deeper experiment tools and custom evaluations.

Key Highlights

• Single API for over a hundred models
• Session-based tracing for multi-turn interactions
• Prompt playground and evaluator tools
• Passthrough billing on cloud gateway
• Quick integration via base URL change
• Seven-day free trial, no card required

Who it’s best for

• Builders juggling multiple LLM providers
• Apps focused on debugging request flows
• Startups testing observability on a trial basis

Contact Information

• Website: helicone.ai
• Email: contact@helicone.ai
• LinkedIn: linkedin.com/company/helicone
• Twitter: x.com/helicone_ai

4. LangWatch

LangWatch offers a complete pipeline for testing and evaluating LLM agents, from crafting prompts to monitoring live performance. It runs simulations to catch edge cases before they reach users, tracking responses and tool usage in complex chains. Datasets and annotations help label outputs for improved training, while evaluations guard against hallucinations or regressions. With OpenTelemetry, it plugs into any framework, ensuring data flows freely without getting trapped.

The platform fosters collaboration, letting engineers script flows in code while non-technical users tweak datasets or review annotations via the interface. It supports multimodal setups, like voice agents or RAG systems, and optimizes for tasks like tool selection in multi-turn chats. Self-hosting options keep it local or air-gapped, with enterprise-grade controls for compliance and custom model integration via API. Data exports work seamlessly with existing stacks, avoiding vendor lock-in.

Key Highlights

• Agent simulations to test edge cases
• OpenTelemetry for broad framework support
• Annotations and datasets for output review
• Evaluations to spot response issues
• Self-hostable with on-prem options
• UI and code access for mixed teams

Who it’s best for

• Groups simulating agents pre-deployment
• Evaluators working on RAG or voice flows
• Open-source fans avoiding vendor ties

Contact Information

• Website: langwatch.ai
• LinkedIn: linkedin.com/company/langwatch
• Twitter: x.com/LangWatchAI

5. Maxim

Maxim focuses on simplifying the evaluation and monitoring of AI agents, offering tools to experiment with prompts and track performance in real-world settings. Developers can iterate on prompts, models, and workflows in a low-code playground, while versioning keeps changes organized outside the codebase. Real-time observability captures traces of multi-agent workflows, helping spot issues like regressions or errors quickly, with alerts to keep quality in check. It supports a range of data sources, from simple docs to runtime contexts, for building realistic test scenarios.

The platform suits both coders and non-technical users, with SDKs in Python, TypeScript, and others for developers, alongside a no-code interface for running experiments. It integrates with CI/CD pipelines for automated testing and supports human-in-the-loop evaluations for fine-tuning. Security features like in-VPC deployment and custom SSO cater to enterprise needs. A free tier is available, with a 14-day trial, while paid plans unlock advanced features like priority support and tailored human evaluation workflows.

Key Highlights

• Low-code prompt IDE for testing workflows
• Real-time tracing and debugging of agent interactions
• Supports custom and pre-built evaluators
• Integrates with CI/CD for automated testing
• Multimodal dataset support with easy export
• 14-day free trial with enterprise-grade security options

Who it’s best for

• Developers iterating on complex AI workflows
• Mixed teams needing no-code evaluation tools
• Enterprises requiring secure, in-VPC deployments

Contact Information

• Website: getmaxim.ai
• Email: contact@getmaxim.ai
• LinkedIn: linkedin.com/company/maxim-ai
• Twitter: x.com/getmaximai

6. Arize

Arize tackles AI observability by offering tools to monitor and evaluate LLMs and multi-modal systems in production. Engineers can trace model outputs, debug issues like data drift or hallucinations, and use open-source evaluation libraries to assess performance. The platform integrates with standard data formats for easy interoperability, allowing seamless data exports to other systems. Built on OpenTelemetry, it supports a wide range of frameworks and models without locking users into proprietary setups.

Analytics dashboards provide visibility into model behavior, helping track quality and costs while enabling non-technical users to view outcome reports. Features like span tracing and evaluation libraries support rapid prototyping and continuous improvement. Arize emphasizes transparency, with no black-box eval models, and caters to production environments where trust and reliability matter. A free tier is available, with paid plans unlocking deeper insights and enterprise support.

Key Highlights

• Open-source eval libraries with no proprietary models
• Span tracing for production model monitoring
• Interoperable with standard data formats
• Supports rapid prototyping for AI projects
• Dashboards for technical and non-technical users
• Free tier with paid plans for advanced features

Who it’s best for

• Engineers monitoring production AI systems
• Teams needing transparent, open-source tools
• Businesses requiring interoperable data solutions

Contact Information

• Website: arize.com
• LinkedIn: linkedin.com/company/arizeai
• Twitter: x.com/arizeai

7. Braintrust

Braintrust centers on evaluating and observing AI agents, letting folks iterate on prompts and models while keeping tabs on production performance. Users can set up datasets, define tasks, and create scorers to test for accuracy or safety, with options for automated checks or human input. The playground setup makes tweaking things quick, like swapping models or running batch tests on lots of examples, and diffs show how changes stack up side by side. It’s got a loop agent that helps with stuff like generating synthetic data or refining scorers, all aimed at catching unpredictable fails in agents.

Observability kicks in for real-time views on latency and costs, with alerts for drops in quality or safety concerns. Brainstore handles the logging side, speeding up searches and analysis of interactions. Security-wise, role-based controls and project isolation keep access in check, plus SOC 2 certification for compliance. Deployment can go hybrid for self-hosting if you want tighter data control. No free trial mentioned, but the focus stays on scaling without hiccups.

Key Highlights

• Evaluation with datasets, tasks, and custom scorers
• Playground for prompt and model experimentation
• Real-time monitoring of latency and quality metrics
• Alerts and automations for production issues
• Brainstore for fast log searching and analysis
• Role-based access and SOC 2 compliance

Who it’s best for

• Builders testing AI agents for reliability
• Folks needing quick iteration on prompts
• Enterprises with self-hosting for data control

Contact Information

• Website: braintrust.dev
• Email: info@braintrust.dev
• LinkedIn: linkedin.com/company/braintrust-data
• Twitter: x.com/braintrustdata

8. Orq.ai

Orq.ai brings together tools for experimenting with GenAI setups, pushing them live, and watching how they run, all in a single spot. Orchestration handles routing and serverless deployments, with retries and fallbacks to keep things steady. Guardrails and version control manage changes, while prompt tweaks and knowledge bases feed into the mix. Evaluation digs into use cases for performance checks, and monitoring tracks ongoing tweaks for better results over time.

Security covers the bases with SOC 2 and GDPR nods, letting you pick US or EU hosting for data. Role-based permissions span orgs and projects, and PII masking hides sensitive bits in inputs and outputs. Hosting flexes from cloud to on-prem or VPC, fitting different setups. Solution engineers offer hands-on help for rolling out GenAI. No trial or pricing details show up here, but the flow supports mixed roles without silos.

Key Highlights

• Serverless deployment with routing engine
• Retries, fallbacks, and guardrails
• Prompt management and version control
• PII and output masking for compliance
• Flexible hosting in cloud or on-prem
• Role-based permissions across workspaces

Who it’s best for

• Groups scaling complex LLM apps
• Users needing end-to-end lifecycle tools
• Setups requiring EU or US data residency

Contact Information

• Website: orq.ai
• LinkedIn: linkedin.com/company/orqai
• Twitter: x.com/orq_ai

9. Portkey

Portkey acts as a hub for GenAI production, wrapping gateway access, observability, and guardrails into one system. The unified API connects to loads of LLMs, cutting down on setup time, with caching and routing to trim costs. Observability dashboards spot anomalies in real time, logging traces and errors for deeper looks. Guardrails handle PII redaction automatically, and governance sets budget caps and access rules.

Integration happens fast with a few code lines, working across Node.js or Python, and the MCP client eases agent workflows. It’s open-source in parts, with community input on the gateway side. Pricing kicks off at around sixty bucks a month for plans, though a free start might be there for basics – paid unlocks fuller monitoring and team features. RBAC and SSO keep collaboration secure without extra hassle.

Key Highlights

• Unified API for multiple LLMs
• Intelligent caching and batching
• Real-time anomaly detection in dashboards
• Automatic PII redaction
• Model Context Protocol for agents
• RBAC and SSO for governance

Who it’s best for

• Builders needing quick LLM integrations
• Apps focused on cost and performance tracking
• Open-source users avoiding heavy setups

Contact Information

• Website: portkey.ai
• Address: 2261 Market Street #5205, San Francisco, CA
• LinkedIn: linkedin.com/company/portkey-ai
• Twitter: x.com/portkeyai

10. Comet

Comet offers a setup for handling machine learning workflows, covering experiment tracking to production monitoring for models. Users can log runs, compare outcomes, and tweak things across stages, with a focus on evaluations for LLMs. The platform pulls together tools in one interface, letting folks manage models from start to finish without jumping between apps. It’s got customization options, so adjustments fit specific needs, like explaining results or optimizing performance in real setups.

Beyond basics, it bridges gaps between research-style ML and practical use, helping reproduce work and share insights. Organizations from small groups to bigger outfits use it for tracking and explaining models in production. Remote work vibes show in the spread-out workforce, but the core stays on smoothing out ML pains. No trial details pop up, though starting involves building right in.

Key Highlights

• Experiment management for tracking runs
• Model evaluations with LLM focus
• Production monitoring for ongoing optimization
• Customizable interface for workflows
• Supports sharing and reproducing results
• Covers training through deployment stages

Who it’s best for

• Folks managing ML lifecycles end-to-end
• Users needing experiment comparison tools
• Groups focused on model explainability

Contact Information

• Website: comet.com
• Email: support@comet.com
• LinkedIn: linkedin.com/company/comet-ml
• Facebook: facebook.com/cometdotml
• Twitter: x.com/Cometml

11. AgentOps

AgentOps focuses on tracing and debugging AI agents, providing visibility into LLM calls, tool actions, and multi-agent setups. Developers install the SDK via pip and start logging sessions right away, capturing timings for each step from start to end. Visual replays show how events unfold, like research actions updating a database, while time travel features let users rewind specific moments for closer looks. Audits pull together logs of errors or potential issues like prompt injections, keeping a record from early tests through live runs.

Spending tracking adds another layer, visualizing costs based on token usage and current rates. Integrations hook into frameworks such as OpenAI, CrewAI, and Autogen, plus hundreds of LLMs, all through one SDK that handles the heavy lifting. It’s straightforward for spotting what went wrong in a chain of actions, without needing extra tools scattered around.

Key Highlights

• Visual session replays for event tracking
• Time travel debugging for precise rewinds
• Full audit trails of logs and errors
• Token and spending visualizations
• Single SDK for broad LLM integrations
• Native support for agent frameworks

Who it’s best for

• Developers building multi-agent systems
• Folks debugging LLM chains in production
• Users monitoring costs during prototyping

Contact Information

• Website: agentops.ai
• Address: adam@agentops.ai
• LinkedIn: linkedin.com/company/aistaff
• Twitter: x.com/agentopsai

12. Athina

Athina handles collaborative AI development, covering prompt management to production monitoring for various models. Users can test and run prompts with built-in tools, including custom ones, while experiments let you tweak datasets or flows. Annotation features mark up outputs for review, and prototyping speeds up initial builds. Production views offer a clear look at how features hold up once deployed, pulling in metrics without much setup.

Dataset work stands out, with side-by-side comparisons and SQL queries for digging deeper. It pulls in input from different roles, so non-technical folks can join evaluations or manage prompts via the interface, while coders handle scripting. Documentation covers the nuts and bolts, like interacting with data or setting up tests, keeping things accessible.

Key Highlights

• Prompt testing across any model type
• Dataset comparisons with SQL access
• Annotation for output reviews
• Experiment tools for flow tweaks
• Production monitoring dashboards
• Role-flexible collaboration setup

Who it’s best for

• Mixed groups testing AI features
• Data folks exploring datasets hands-on
• Builders needing quick prototypes

Contact Information

• Website: athina.ai
• Email: hello@athina.ai
• LinkedIn: linkedin.com/company/athina-ai

13. Langtail

Langtail organizes prompt management in a spreadsheet-style setup, making it easy for product groups to build and test AI elements together. Prompts get created with variables for flexibility, then validated through natural language checks, pattern matching, or code snippets. Optimization involves swapping models or parameters to see what works, with insights drawn from performance data and user feedback. The AI Firewall adds a security check, filtering threats like injections or leaks with customizable rules and alerts.

Workflows run from rough sketches to deployment, using a TypeScript SDK or OpenAPI for invoking prompts in apps. Integrations link to providers like OpenAI or Gemini, handling everything from classification tasks to secure outputs. Examples of real slip-ups, such as rogue chatbots or bad suggestions, underscore the need for tight control, but the tools keep it practical for daily tweaks.

Key Highlights

• Spreadsheet interface for prompt handling
• Validation via natural language or code
• Model experiments for optimization
• AI Firewall for threat filtering
• TypeScript SDK and OpenAPI support
• Insights from performance visuals

Who it’s best for

• Product users refining AI outputs
• Groups collaborating on prompt tests
• Developers securing LLM integrations

Contact Information

• Website: langtail.com
• Email: hi@langtail.com
• Address: Záhřebská 562/41 120 00 Praha Czech Republic
• LinkedIn: linkedin.com/company/langtail
• Twitter: x.com/langtail

14. Literal AI

Literal AI covers the full cycle of LLM app development, with logs capturing calls, agent runs, and chats for debugging and dataset creation from actual use. Traces help monitor interactions, while the playground lets users craft prompts with templates, tool calls, and custom models. Monitoring spots failures in live setups through evaluations, pulling in data on costs and speed from a central dashboard. Datasets stay organized to avoid shifts between staging and production, and experiments run against them to tweak without backsliding.

Human review pulls in feedback to refine outputs over time, and prompt versioning supports A/B tests for collaborative tweaks. Integrations link to providers like Anthropic or OpenAI, plus frameworks such as LangChain, all via SDKs in Python or TypeScript. Setup takes minutes with code instrumentation, and self-hosting works on major clouds for those needing it. Security stays front and center, built by folks behind an open-source chat framework.

Key Highlights

• Logs and traces for LLM calls and agents
• Playground for prompt debugging with templating
• Monitoring dashboards for cost and latency
• Dataset management to prevent drifting
• Experiments against datasets for iterations
• Human review for feedback-based refinements

Who it’s best for

• Developers handling LLM app lifecycles
• Groups collaborating on prompt tweaks
• Builders needing production failure detection

Contact Information

• Website: literalai.com
• LinkedIn: linkedin.com/company/literalai
• Twitter: x.com/chainlit_io

15. HoneyHive

HoneyHive acts as a spot for developing and watching AI agents, starting with evaluations to measure quality through test suites that catch issues early. Observability uses traces for end-to-end views of agent flows, digging into logs for quick fixes. Monitoring keeps an eye on costs, speed, and steps like retrieval or reasoning, with alerts for drifts or failures. Artifact management centralizes prompts, tools, and datasets, syncing between code and interface for mixed input.

OpenTelemetry ties it into existing stacks, and enterprise options include compliance like SOC-2 or HIPAA, plus flexible hosting from shared cloud to self-run. A playground experiments with prompts and models, while human reviews grade outputs for better tuning. CI automation runs checks with commits, and A/B tests spot regressions during changes. It’s geared toward steady iteration without surprises.

Key Highlights

• Evaluations with custom metrics and test suites
• Traces for agent debugging and optimization
• Monitoring with alerts for quality drifts
• Artifact syncing between UI and code
• OpenTelemetry for stack integration
• Enterprise hosting with granular permissions

Who it’s best for

• Folks simulating agents before launch
• Users needing visibility into executions
• Setups requiring compliance and alerts

Contact Information

• Website: honeyhive.ai
• LinkedIn: linkedin.com/company/honeyhive-ai
• Twitter: x.com/honeyhiveai

16. Langtrace

Langtrace offers open-source tracing for AI agents, automatically capturing stack details like queries or completions to surface metadata. Metrics dashboards show token use, costs, and latencies, with changes tracked for easy spotting of shifts. API explorations pull in request info, while evaluations baseline performance using curated datasets for tuning. The prompt playground compares versions across models, aiding tweaks without heavy lifting.

Integrations cover frameworks like CrewAI or LangChain, plus LLMs and vector stores, all via simple SDK init in Python or TypeScript. Self-hosting keeps it on-prem for privacy, with SOC-2 compliance built in. Community support comes through Discord, and the GitHub repo invites custom tweaks or audits. It’s practical for turning rough ideas into solid apps, focusing on insights without extra hassle.

Key Highlights

• Automatic tracing of GenAI stacks
• Dashboards for token and latency metrics
• Evaluations with dataset curation
• Prompt playground for model comparisons
• SDK support in Python and TypeScript
• Open-source with self-hosting options

Who it’s best for

• Developers debugging agent performances
• Enterprises prioritizing secure setups
• Builders integrating with multiple frameworks

Contact Information

• Website: langtrace.ai
• LinkedIn: linkedin.com/company/langtrace
• Twitter: x.com/langtrace_ai

Conclusion

So, wrapping this up, there’s no shortage of tools out there for managing and tweaking AI workflows, each with its own spin on making life easier for developers and teams. Some lean hard into tracing every step of an LLM’s thought process, while others focus on quick prompt management or beefy analytics to catch hiccups before they snowball. The variety means you can pick what fits your setup-whether it’s a lightweight solution for tossing prompts around or a heavier platform for end-to-end monitoring. Honestly, it’s less about finding the perfect tool and more about matching the vibe of your project’s needs.

What stands out is how these platforms tackle the messy reality of AI development. They’re built to cut through the chaos of debugging, testing, and deploying, often with neat tricks like open-source code or no-code options for folks who don’t live in terminals. Give a few a spin, maybe poke around their docs or free trials, and you’ll figure out which one clicks for your workflow. It’s all about finding that balance of control and simplicity to keep your AI projects humming without driving you up the wall.