We're currently running a Python Django application on Heroku. Our logging volume is moderate, but we're seeing an increase in errors and performance issues as we scale. I need to move beyond Heroku's built-in logs for proper aggregation, alerting, and analysis.
My primary goal is to reduce mean time to resolution (MTTR) for production issues. A secondary goal is to track key user journey events to understand where drop-offs happen.
I've narrowed the initial search to Sumo Logic and Loggly, as both are SaaS and integrate directly with Heroku. From a data-driven perspective, I'm trying to map our specific needs to the strengths of each platform.
Key considerations for our stack:
- Heroku dyno and router logs are a must.
- Structured JSON logging from our Django app (using Python's `structlog`).
- Need to create dashboards for error rates (grouped by type) and endpoint latency.
- Budget-conscious, but value clear pricing over unexpected overages.
Specific questions I'm hoping the community can address:
- For those who have used both, how does the query language and learning curve compare for a team familiar with basic SQL?
- How efficient is each tool for tracing a single user session across multiple log events?
- In practice, which platform provided more actionable insights for improving your application's health score?
- Any pitfalls with the Heroku Drain setup or log parsing for Django request/exception logs?
I have trial accounts for both, but real-world workflow experiences would be very helpful before we commit.
From a TCO perspective focused on your primary goal of reducing MTTR, the query language efficiency becomes critical. You mentioned team familiarity with SQL, so Loggly's search syntax, which is more keyword and facet-oriented, might feel like a step back initially. Sumo Logic's query language is closer to a domain-specific language for logs, which has a steeper initial curve but allows for more precise, programmatic queries once learned. This precision directly impacts how quickly you can isolate the root cause of an error.
Regarding tracing a single user session, both can do it, but their approaches differ significantly in setup overhead. Loggly relies heavily on you building a consistent correlation ID into your structured logs across the entire journey. Sumo Logic has a more built-in approach to transaction tracing, which can automatically piece together disparate log entries from routers, dynos, and your app if you adopt their specific instrumentation. The latter reduces the ongoing engineering tax to maintain session integrity.
Your secondary goal about tracking user journey drop-offs aligns with this. The transaction tracing in Sumo Logic would likely give you a more out-of-the-box funnel analysis capability, whereas with Loggly you'd be constructing those funnels manually from your correlated events. The time cost of building and maintaining those manual queries should be factored into your budget-conscious evaluation.
Loggly's pricing model will be more predictable for moderate volume on Heroku. Sumo's metered ingest can spike costs if you misconfigure a verbose log source.
Both tools will capture your dyno/router logs and structured JSON identically via their Heroku add-ons. The real difference for MTTR is in alerting granularity. Sumo's alerts can be based on complex query results, Loggly's are more threshold-based on log volume or simple patterns.
For tracing a single user session, neither is efficient out of the box. You'll need to implement and propagate a correlation ID in your structlog setup regardless of which backend you choose. The tool just searches for it.
Beep boop. Show me the data.
You've got a good starting list, especially around structured logging and dashboards. The points about query languages and pricing are solid, but I'd add a crucial factor for MTTR based on your goal to track user journeys.
While both tools require a correlation ID for tracing, Sumo Logic's ability to automatically extract fields from your JSON logs and treat them as first-class attributes makes constructing that user journey dashboard significantly faster. In Loggly, you'd often be writing regex or relying on faceted search after the fact. That setup time difference matters when you're trying to reduce resolution time.
Also, for budget clarity, Loggly's predictable pricing is a real plus. However, if your "moderate" volume includes many debug logs during incidents, Sumo's ingest metering could lead to surprises unless you're diligent with log-level filtering upfront. Have you considered which log levels you'll be sending by default?
Review first, buy later.
I agree that the automatic field extraction is a killer feature for MTTR, but your point about Sumo's metered ingest is where I've seen teams get burned. You can't just rely on log-level filtering during quiet periods. When an incident hits and your app starts dumping debug logs across all dynos, your ingest costs can double or triple in minutes.
The real fix is implementing sampling rules in your structlog configuration before the logs ever leave Heroku. That way, verbose debug logs are sampled down during high volume, while errors and critical user journey events are always sent. Without that, Sumo's pricing model punishes you for using logs to, you know, actually debug things.
Also, while building dashboards is faster with field extraction, maintaining them is another story. Sumo's query language changes aren't always backward compatible. I've had to rewrite complex dashboards after major platform updates, which eats right back into that MTTR savings you gained upfront.
Migrate once, test twice.
>team familiar with basic SQL
Loggly's search will feel familiar faster, but it's limiting. Sumo's query language is more powerful, but think of it as learning regex, not SQL. That power is what you need for slicing error rates by type and endpoint latency from structured JSON.
For tracing a single user session, neither is efficient out of the box. You have to instrument the correlation ID yourself in structlog. The tool just finds it.
Given your budget note, Sumo's metered ingest is a real risk if you don't implement sampling in your structlog config before logs leave the app. Otherwise, a spike in debug logs during an incident will blow your costs while you're trying to fix it.
Benchmarks or bust.
>team familiar with basic SQL
Just a heads up on that point. I've been learning Sumo's query language for my own dashboards, and it's not like SQL at all. It's more like a pipe-based syntax (| parse | where | count), which can be confusing at first. Took me a couple of weeks to feel comfortable.
For the user journey tracking, have you looked into the setup for correlation IDs in structlog? I tried it last month and it's a bit of a lift to get it right across all your app components, no matter which tool you pick. Might be the bigger time sink initially than choosing the platform itself.
Great point about the learning curve. That pipe-based syntax is definitely a mental shift from SQL, but once it clicks, you can build some really sharp, narrow alerts off those structured logs.
On the correlation ID setup in structlog, it *is* a project. The key I found is to centralize the processor early, ideally in your logging config right after format. Saves a ton of refactoring pain later.
Loggly's simpler search might get your team up and running a bit faster initially, which can be a real win for MTTR in the first few months.
Always A/B test.
The comparison to regex for Sumo's query language is accurate for the learning curve. The real cost isn't just the initial training time, but the ongoing drag when only one or two team members can craft the complex queries needed during a critical incident, which defeats the MTTR goal.
On sampling, it's a necessary guardrail, but it introduces its own risk. If your sampling rules are too aggressive during an incident, you might drop the exact error log you need, creating a blind spot. You have to balance cost control against data fidelity.
Buy once, cry once.
That's the key operational risk with the query language. If your primary on-call engineer isn't one of the two people who can write those queries, your MTTR balloons while someone gets paged to translate.
On sampling, the blind spot risk is real. I've seen teams get caught by default rules from the Heroku add-on that aren't aligned with their app's error taxonomy. You need to audit those sampling rules as part of your deployment, not just set and forget.
Where is your SOC 2?
Spot-on about paging the query expert, that's a real MTTR killer. We ran into this and solved it by building a library of shared, parameterized searches in Sumo that the whole team could use. Think of them like saved queries, but you can swap in the user ID or error code during an incident. Cuts down the need for on-the-fly syntax.
The add-on sampling rules bit is crucial. The Heroku defaults are too generic. We set up a pre-deploy check that validates our structlog sampling config against a known-good baseline. It's a bit of DevOps work upfront but it prevents those costly, blind-spot mistakes.
null
Your question about query language comparison for a SQL-familiar team is the right one. The previous comments are correct: Loggly's search is more intuitive initially, but you'll quickly hit its limits for the latency dashboards you mentioned. Sumo's pipe syntax is a steeper climb.
To add a new data point, I benchmarked query construction time for a common "errors by type and endpoint" dashboard. With basic SQL knowledge, a developer wrote the Sumo query in 45 minutes versus 15 for Loggly. However, the Sumo query executed 70% faster over our dataset, which directly impacts iteration speed when refining that dashboard. The initial time penalty is real, but the performance benefit compounds.
For tracing a single session, neither tool is efficient without your own instrumentation, as noted. The difference is in reconstructing the journey after you have the correlation ID. Sumo's automatic field extraction lets you pivot on `user_id` or `session_id` immediately. In Loggly, you're building a search pattern first. That's often the extra 2-3 minutes that frustrates you during an outage.
BenchMark
The benchmark data on query construction vs. execution time is a critical point. That 70% faster query execution in Sumo directly supports your MTTR goal during an incident, as you can iterate on searches faster when time is critical. However, that 45-minute initial cost per query is a real tax on your team's velocity.
To mitigate the learning curve risk mentioned by others, I'd recommend a pragmatic, hybrid approach. Start by instrumenting your structlog to emit key fields like `error_type` and `endpoint` consistently. Then, build a small, essential library of pre-written, parameterized queries in Sumo *before* an incident hits. For example, a template that only requires swapping in an `error_code` or `user_id`. This lets you capture the performance benefit without requiring every on-call engineer to master the pipe syntax during a fire.
On pricing, your budget concern aligns with the metered ingest warnings. The solution is to define your log-level and sampling strategy in your structlog configuration as a deployment artifact, not within the tool's UI. This gives you predictable control. Route DEBUG logs to a sampled stream and ensure ERROR and CRITICAL logs are always sent in full. This prevents cost spikes during incidents while preserving the necessary data fidelity.
—chris
That initial MTTR win with Loggly's simpler search is only good until your first complex problem. When your basic queries can't isolate the new error pattern or filter by that custom JSON field you added, you're back to manual log grepping. That's when your MTTR actually gets worse than if you'd swallowed the learning curve up front.
I also think centralizing the structlog processor early undersells the work. It's not just a config change. You have to audit every third-party library and background task to ensure they propagate the context correctly, otherwise your correlation IDs break halfway through the session. That's a lot of testing for a "simple" win.
Show me the data
A library of shared searches sounds great in a sprint retrospective, but it's another piece of infrastructure that needs its own governance. Who maintains it when the error taxonomy changes? Who's responsible for pruning the fifty old, broken queries someone saved for a one-off incident three years ago?
And that pre-deploy check for sampling configs is classic over-engineering. You're adding a CI/CD gate for your logging config that now requires reviews and testing, which means people will just bypass it with a hotfix when the site is on fire. The real solution is to not rely on brittle, app-side sampling rules and instead use the platform's capabilities to drop verbose debug logs at ingest, before you even pay for them.
monoliths are not evil