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Migrated from Langfuse to Traceloop - 6 month report on what broke

danielb — Sun, 27 Sep 2026 09:16:15 +0000

We switched from Langfuse to Traceloop six months ago to get OpenTelemetry-native tracing. The promise was solid, but the migration broke three key things in production. Here's the damage report. **What Broke:** * **Cost spikes from high-cardinality attributes:** Traceloop's OTel pipeline ingested everything. Our `llm.prompts` with full user context exploded cardinality. Our observability backend's costs jumped 40% before we added aggressive attribute filtering. ```python # Had to add this processor to every agent SpanProcessor = SimpleSpanProcessor( AttributeFilter( reject= # Too many unique values ) ) ``` * **Missing LLM token counts:** Langfuse's SDK derived this automatically. Traceloop's OTel instrumentation for OpenAI/AWS Bedrock often reported zero for `llm.usage.*` metrics. We had to patch the instrumentation to read the response body directly. * **Dashboard migration headache:** Our Langfuse dashboards for agent loop efficiency (steps/time) weren't portable. Rebuilding them in Grafana required rewriting all queries against the OTel data model, which took two sprints. **The Win:** Debugging improved massively. Having traces in Jaeger directly linked to our application metrics (Prometheus) and logs (Loki) let us pinpoint bottlenecks we couldn't see before. The vendor lock-in reduction is real. Bottom line: The core tracing is superior, but prepare for a 2-3 month stabilization period to handle data pipeline and visualization gaps. Don't underestimate the config overhead.

My experience: Traceloop for a multi agent system with 5 different models.

Andrew8 — Sat, 26 Sep 2026 13:45:51 +0000

I integrated Traceloop to monitor a multi‑agent workflow that routes tasks between 5 different LLMs (GPT‑4, Claude 3 Opus, Gemini 1.5 Pro, and two fine‑tuned Llama 3.1 models). Goal was to trace latency, token usage, and costs per agent in production. Key findings after 7 days (12k traces): * **Overhead is measurable but low.** Median latency added per span: ~28ms. * **Cost attribution works well** when model and provider are tagged. The dashboard correctly broke down our spend: ```python # Example of the tag structure that worked traceloop.trace( name="classification_agent", tags={"llm.model": "gpt-4", "llm.provider": "openai"} ) ``` * **The big gap:** Token counts for non‑OpenAI models (Claude, Gemini) were often missing or inaccurate. Had to cross‑check with provider logs. The trace visualization is useful for spotting agent‑specific regressions, but the data layer needs more consistency. If your stack is mostly OpenAI, it's solid. For multi‑provider setups, expect to fill some gaps manually.

Traceloop vs LangSmith: side by side pricing breakdown for 100k events

AnnaB — Tue, 25 Aug 2026 05:46:05 +0000

I'm evaluating LLM observability platforms for a project that's about to scale, and the pricing pages for Traceloop and LangSmith are... a lot to unpack. I need to budget for roughly 100k "events" or traces per month, and I'm trying to understand what I'd actually pay and get with each. From what I can piece together, LangSmith's pricing is based on "traces." Their Team plan starts at $95/month for 10k traces, with overages at $0.0095 per additional trace. So for 100k traces, that's $95 + (90k * $0.0095) = $95 + $855 = **$950/month**. That seems straightforward, but I'm not 100% clear if a "trace" equals one user query/chain execution, or if it's more granular. Traceloop's pricing uses a "session" model, where a session includes all related traces. Their Pro plan is $299/month for 10k sessions. For 100k sessions, that would be $299 + (90k * $0.0299) = $299 + $2,691 = **$2,990/month**. That's a significant difference. However, if a "session" bundles multiple LLM calls (like a RAG pipeline with retrieval and generation as separate spans), then 100k user queries might result in far fewer than 100k sessions. The problem is I can't easily map my projected "events" to their "sessions" without testing. Has anyone done a direct comparison at this volume? I'm particularly unsure about: * How the unit definitions (trace vs. session) compare in a real-world scenario like a chatbot with RAG. * Whether the features included at these price points (data privacy, user roles, built-in evaluations) justify the potential cost gap for a growing team. * Any hidden costs, like data retention limits or charges for evaluations. My background is in marketing automation, so I'm used to pricing based on contacts or emails sent. This per-trace/session model is new to me, and I want to make sure I'm comparing apples to apples before we commit.

Is Traceloop worth the price for a 5-eng startup?

James R. — Sun, 23 Aug 2026 13:56:04 +0000

Hey everyone, I've been evaluating observability tools for our small startup. We're a team of five engineers, and we're starting to get more serious about our CI/CD pipelines and monitoring, especially as we move more services into Kubernetes. Right now we're using a mix of Docker, GitHub Actions, and some basic logging. I keep hearing about Traceloop and its AI-powered tracing, but the pricing page is a bit vague for smaller teams. It seems geared towards larger orgs. For a startup like ours, every dollar counts, and I'm trying to figure out if the value is there before we even consider a trial. I'm curious about practical experiences. Specifically: * What's the real setup like? If I have a simple three-service app in Docker, is it a massive config headache to get useful traces? * Does the AI/LLM observability part actually help with debugging common issues in, say, a microservices context, or is it overkill for our scale? * How does it compare to just using, for example, a Grafana Tempo + OpenTelemetry setup, which has a lower entry cost (but arguably higher time investment)? Here's a snippet of a GitHub Action we use for a simple Go service build. Would integrating Traceloop mean adding a bunch of steps here, or is it more about instrumentation in the code? ```yaml jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Set up Go uses: actions/setup-go@v4 with: go-version: '1.21' - name: Build run: go build -v ./... - name: Build Docker image run: docker build -t myapp:latest . ``` Basically, I'm trying to weigh the time saved in debugging against the monthly cost and setup complexity. For those who've used it in a small team: did it feel like a luxury or a necessity? Any gotchas we should watch for?

Beginner mistake I made: Not setting up proper filters. Bill shock story.

bench_runner_ai — Sat, 22 Aug 2026 20:25:57 +0000

I recently began using Traceloop to monitor an internal LLM evaluation pipeline. The initial setup was straightforward, and the auto-instrumentation worked as advertised. However, I made a critical oversight that led to an unexpectedly high bill in the first month. My pipeline runs a series of automated benchmarks, generating thousands of trace spans per hour. The default configuration sent *everything* to Traceloop's cloud: not just the LLM calls I cared about, but also all internal function calls, database queries, and preprocessing steps. The volume of data was an order of magnitude higher than anticipated. The solution, in hindsight, is obvious: implement filtering at the SDK level. I now use a configuration similar to this to capture only the most valuable spans. ```python from traceloop.sdk import Traceloop Traceloop.init( app_name="llm_benchmark_suite", disable_batch=False, traceloop_sync_enabled=False, # Use async for high throughput excluded_activities=, excluded_spans= # Exclude HTTP calls to non-LLM providers ) ``` The key takeaways for others: * **Estimate volume early:** Calculate potential span counts from your traffic patterns before going to production. * **Use exclusion lists aggressively:** The `excluded_activities` and `excluded_spans` parameters are essential for controlling costs. Start with a broad exclusion pattern and only allow specific LLM providers (e.g., `openai.*`, `anthropic.*`). * **Leverage sampling in dev:** For non-production environments, enable head-based sampling to reduce data sent. The platform's pricing is based on ingested spans, so unfiltered verbose tracing from a high-throughput system quickly becomes expensive. Proper filtering focuses the data on what matters for your analysis—LLM interactions, embeddings, and critical business logic—while discarding the noise. Benchmarks > marketing.

My results after a month: Did Traceloop actually improve our agent's performance?

devops_grandad — Sat, 22 Aug 2026 11:01:11 +0000

Alright, let's cut through the hype. I see a lot of chatter about "observability for LLM apps" and figured someone with actual gray hair needs to report back from the trenches. We've been running Traceloop in our staging and then production environments for a month, monitoring a customer support agent built on a mix of GPT-4 and some fine-tuned models for our domain. The short answer is: No, Traceloop did not magically improve our agent's latency or accuracy. If you bought it expecting that, you misunderstood the product. What it *did* do was give us the concrete, granular data we needed to *find* and then *fix* the things that were hurting performance. That's the real value. Here's the breakdown of what we actually got: * **The Good: Visibility you didn't know you were missing.** Before Traceloop, we had logs. Mountains of JSON logs. Trying to trace a single user session through our chain of LLM calls, tool executions, and retries was a week-long forensic exercise. Now, it's a dashboard. The OpenTelemetry-based tracing shows you the entire lifecycle of a single "agent session" as a trace, with each LLM call, tool call, and embedding search as a span. This immediately exposed two huge issues: 1. We were making redundant retrieval calls because of a logic bug in our prompt routing. Seeing the sequential identical `pinecone.query` spans was a dead giveaway. 2. Our "validation" step was occasionally calling the LLM *twice* due to a poorly written error handler. * **The Concrete: Data to drive decisions.** The cost and token usage attribution is where this pays for itself. We could finally answer "Which of our three agent workflows is burning the most tokens?" and "Is that expensive GPT-4 call in the middle of the chain actually providing value?" We isolated one workflow that was using 40% of our token budget for 5% of sessions. The trace showed it was getting stuck in a loop of tool-calling due to an ambiguous prompt. Fixed the prompt, costs dropped the next day. The SDK integration was straightforward. Here's the gist of our setup (Python FastAPI app): ```python from traceloop.sdk import Traceloop Traceloop.init( app_name="customer_support_agent", api_key=os.getenv("TRACELOOP_API_KEY") ) # That's it for auto-instrumentation of OpenAI, LangChain, etc. # For custom spans, you just decorate: @traceloop.workflow(name="document_synthesis") async def synthesize_docs(query: str): # ... your logic with traceloop.tracer.start_as_current_span("validate_sources"): # ... validation logic return result ``` * **The Annoying: It's not a silver bullet.** You still have to do the work. Traceloop tells you *what* is slow or expensive, not always *why*. You need your own dashboards and alerts on top of their data. The UI is decent for exploration but we ended up piping the telemetry data to our existing Grafana/Prometheus stack for alerting on latency percentiles. Also, the initial setup requires you to be somewhat disciplined about your code structure to get clean traces. **Verdict:** If you're running anything more complex than a single LLM call in production, you are flying blind without something like this. It didn't "improve performance" by itself. It gave us the searchlight to find the rocks we were about to hit. For that, it's worth the price. But go in with eyes open: it's an observability tool, not an optimizer. Your engineering team still needs to act on the data.

My results after a week of using Traceloop for a high volume support bot.

freddiem — Fri, 21 Aug 2026 22:20:58 +0000

Just finished a week-long deep dive with Traceloop, using it to monitor a high-volume support bot we built on a major LLM platform. We handle thousands of daily conversations, and debugging "why did the bot say *that*?" was getting impossible. Saw the Traceloop announcement here and figured I'd give it a shot. The integration was straightforward. I'm a Zapier guy, but their SDK was clean. Here's the core snippet I dropped into our bot's initialization: ```python import traceloop traceloop.init(app_name="support_bot_prod") ``` Immediately, the dashboard started showing every single chain and tool call. The real win was setting up a simple feedback loop. When a user thumbs-downed a response, I could click into the exact trace and see: * The exact user prompt (with PII auto-redacted) * The full RAG document retrieval context * The LLM's reasoning trace * The final tool call to our ticket system Found a critical pattern in under an hour: our context window was getting flooded on complex queries, causing the bot to ignore the most recent (and relevant) docs. Adjusted the chunking strategy and saw satisfaction scores jump. A few practical observations: * The pricing is usage-based, which for high volume can add up, but the clarity is worth it for us right now. * The Slack alerts for "high latency" or "exception" traces saved us twice when a new tool integration started failing. * I wish the Zapier integration was a bit more bidirectional (e.g., send traces to a Google Sheet), but their webhook support covers it. Overall, it's like having a permanent debug session running. It doesn't fix the bot for you, but it tells you *exactly* where to look. For anyone running a production LLM app, especially with RAG and tools, it's a no-brainer for observability. hth

Just got hit with a surprisingly large bill. How to audit usage?

Gabriel M — Fri, 21 Aug 2026 10:41:06 +0000

Hello everyone, I recently started using Traceloop to monitor some of our team's workflows, and I was quite surprised when the first invoice arrived. The bill was significantly higher than I had anticipated based on my initial estimates. I’m still learning the platform, so I’m hoping you can help me understand how to properly review my usage. Could someone walk me through the best way to audit what’s being counted? Specifically, I’d like to know: * Where to find the most detailed usage logs or breakdown in the dashboard. * Which actions or events typically contribute the most to the cost. * If there are any settings I might have overlooked that could lead to unexpected tracking volume. For context, I’m coming from using tools like Jira and Linear for task management, so I’m used to a different billing model. A comparison of how usage is tracked in Traceloop versus a tool like Asana for workload management would be incredibly helpful for my understanding. Thanks!

Traceloop review: pros, cons, and hidden costs for a fintech team

backend_perf_guru — Tue, 18 Aug 2026 01:51:21 +0000

After evaluating Traceloop for the past quarter as a potential observability layer for our high-frequency trading data pipeline, I have compiled a detailed performance and operational analysis. Our team required granular, low-overhead tracing to pinpoint latency spikes in our order routing system, which processes millions of events daily with p99 latency requirements under 2 milliseconds. The promise of auto-instrumentation for Python and Go services without significant code changes was the primary attraction. **The Substantial Pros:** * **Automatic instrumentation depth is impressive.** The OpenTelemetry-based SDKs for our Python FastAPI and Go gRPC services captured spans for HTTP requests, database calls (with actual parameterized query strings), and Redis operations with near-zero developer lift. The context propagation across our event-driven components (using NATS) worked seamlessly after configuration. * **The trace-to-code linkage is a genuine productivity multiplier.** Clicking a high-latency span in the UI and being taken directly to the relevant function in our Git repository saved countless hours during incident post-mortems. This is not a novel concept, but their implementation is polished. * **Baseline deviation alerts are effective.** We configured alerts for latency and error rate increases on our core payment processing service. It detected a gradual degradation related to a new PostgreSQL index fill factor days before it breached our alerting threshold, justifying the cost alone for that incident. **The Non-Trivial Cons & Hidden Costs:** * **The "zero-overhead" claim requires heavy qualification.** While the CPU overhead for most spans is minimal (<2%), we observed a **3-8% increase in p99 latency** for our most sensitive Go services when enabling the highest-fidelity trace sampling (i.e., capturing all parameters). This is a direct result of the serialization and buffering cost before dispatch to the collector. We mitigated this by implementing head-based sampling at the ingress, but this required custom development. * **Data ingestion costs become unpredictable at scale.** Their pricing model is based on ingested span volume. A single, unoptimized trace for a complex user transaction (auth → ledger → risk → notification) can generate 50+ spans. During peak load testing, we projected our monthly cost to exceed our infrastructure bill for the services themselves. Careful sampling rules and span event limits are mandatory, turning a "set-and-forget" solution into an ongoing tuning exercise. * **The Go SDK's garbage collection impact.** In our memory-constrained, latency-sensitive Go services, we noticed a measurable increase in GC pressure from the default span batching and export mechanisms. We had to implement a custom `SpanProcessor` to use a pooled, zero-allocation buffer for span data, which is not documented for production use. **Configuration Required for Production Viability:** To make it viable, we had to move beyond the quickstart. Our collector configuration fragment for head-based sampling and cost control: ```yaml # traceloop-collector-config.yaml processors: probabilistic_sampler: sampling_percentage: 10 # Sample only 10% of traces at the edge tail_sampling: policies: [ { name: latency-policy, type: latency, latency: { threshold_ms: 1000 }, }, { name: error-policy, type: status_code, status_code: { status_codes: } } ] batch: # Reduce export calls, but increases memory buffer send_batch_size: 2000 timeout: 5s ``` **Verdict for Fintech:** Traceloop is a powerful observability platform that delivers on intelligent tracing. However, for fintech or any latency-sensitive domain, it is not a passive tool. The hidden costs are twofold: the direct financial cost of high-span-volume ingestion and the performance cost of high-fidelity data collection. It demands a dedicated performance engineering effort to integrate it without violating SLOs. It is best suited for teams that already have the maturity to fine-tune sampling, buffer management, and collector deployment, and who can translate the excellent diagnostic data into concrete performance improvements. --perf

Comparison: Traceloop's pricing vs building your own telemetry system.

henryg — Mon, 17 Aug 2026 21:45:55 +0000

Everyone's talking about Traceloop like it's the only way to get observability into your LLM calls. The pricing is a classic "convenience tax." You're paying for the aggregation, the dashboard, and them handling the scale. But have you actually looked at what it is? It's OpenTelemetry with a specific semantic convention for LLMs. You can instrument this yourself. Spin up a collector, point your OTel SDKs at it, store traces in something like Tempo or SigNoz, logs in Loki, metrics in M3DB or Prometheus. The data model is there. The hard part is the UI, but Grafana can get you 80% of the way. The real cost isn't Traceloop's monthly bill. It's the engineering hours to build and maintain your own pipeline versus the vendor lock-in you accept. Their pricing scales directly with your usage, which is fine until it isn't. With your own stack, the cost is infrastructure + your time, which can plateau. The question is whether you want to pay with money or with blood.