Axiom vs Parseable: S3-Native Log Analytics Compared (2026)

Updated: February 2026 | Reading time: 12 min

Introduction

Axiom and Parseable both represent a generational shift in how observability platforms handle storage. Both reject the legacy approach of maintaining expensive Elasticsearch clusters or proprietary index files on fast SSDs. Instead, they leverage object storage and columnar formats to store telemetry data at commodity prices. On the surface, they look like they are solving the same problem the same way.

But underneath the shared architectural philosophy, the differences are fundamental. Axiom is a closed-source, SaaS-only platform. Parseable is open source, self-hostable, and available as a managed cloud offering. Axiom focuses primarily on log analytics with some metrics support. Parseable is a unified MELT observability platform handling logs, metrics, events, and traces. Axiom keeps your data on their infrastructure. Parseable gives you full data sovereignty.

If you are evaluating axiom alternatives because you need more control over your data, the ability to self-host, or true unified observability, this comparison will give you the technical details to make an informed decision.

TL;DR: Both Axiom and Parseable use object storage for cost-efficient telemetry. But Parseable is available as a managed cloud service (Parseable Cloud starting at $0.37/GB ingested) and as a self-hosted deployment, stores data in Apache Parquet on your own S3 buckets, offers full MELT observability (logs, metrics, events, traces), and uses standard SQL for querying. Axiom is closed-source SaaS with a proprietary query language and no self-hosted option. If data sovereignty, deployment flexibility, or unified observability matters to you, Parseable is the stronger choice.

Quick Comparison Table

Architecture Comparison: Same Philosophy, Different Execution

Both Axiom and Parseable belong to the same architectural generation of observability tools: they separate storage from compute and use object storage as the primary data tier. This is the right approach. Object storage at ~$0.023/GB/month is orders of magnitude cheaper than the fast SSDs that Elasticsearch, ClickHouse, or Splunk indexers require. But the execution differs significantly.

Axiom's Architecture

Axiom uses a serverless, multi-tenant SaaS architecture. When you send data to Axiom, it is ingested into their managed infrastructure, stored in a proprietary columnar format on object storage that Axiom controls. Queries execute on compute resources that Axiom provisions on demand, which is the "serverless" part of their pitch.

This approach has real advantages. You do not manage any infrastructure. There are no clusters to tune, no storage to provision, no upgrades to coordinate. Axiom handles all of that. For small teams that want to send logs somewhere and query them occasionally, this is genuinely convenient.

The trade-off is that you have no visibility into how your data is stored, no ability to access the underlying storage directly, and no option to run Axiom on your own infrastructure. Your data lives on Axiom's cloud, period.

Parseable's Architecture

Parseable takes a fundamentally different approach. It is a single Rust binary that connects directly to any S3-compatible object storage: AWS S3, Google Cloud Storage, Azure Blob Storage, MinIO, or any other S3-compatible backend. Data is stored in Apache Parquet format, an open industry standard used across the data engineering ecosystem.

┌──────────────┐     ┌──────────────────┐     ┌───────────────┐
│  OTel Agent  │────>│  Parseable       │────>│  Your S3      │
│  or HTTP     │     │  (Single Binary) │     │  Bucket       │
└──────────────┘     └──────────────────┘     │  (Parquet)    │
                            │                 └───────────────┘
                     ┌──────┴──────┐
                     │  SQL Query  │
                     │  Interface  │
                     └─────────────┘

The Parseable binary handles ingestion, storage, and querying. In production, you can run it in cluster mode with smart caching for high availability. But the underlying storage is always your object storage, under your control, in an open format.

This means you can:

• Access your data independently of Parseable using any tool that reads Parquet (DuckDB, Apache Spark, pandas, Trino, Athena)
• Run Parseable anywhere: on any cloud, on-premises, in air-gapped environments, at the edge
• Migrate away without data loss: your data is in standard Parquet on your own buckets
• Meet compliance requirements for data residency by choosing where your buckets live

Why This Matters

The architectural difference is not just a technical footnote. It determines who controls your data, where your data lives, and what happens if you want to leave the platform. With Axiom, your data is on their infrastructure in a proprietary format. Leaving means exporting through their APIs and converting to another format. With Parseable, your data is already on your infrastructure in an open format. There is nothing to export.

Data Sovereignty and Compliance

For organizations operating under regulatory frameworks like GDPR, HIPAA, SOC 2, PCI-DSS, or government-specific mandates (FedRAMP, ITAR), data sovereignty is not a preference. It is a requirement.

The Problem with SaaS-Only Platforms

Axiom is a SaaS-only platform. Your telemetry data, which often contains sensitive information like user identifiers, IP addresses, request payloads, and error messages, flows to Axiom's infrastructure. You have limited control over:

• Data residency: Where exactly is your data stored geographically?
• Encryption key management: Who holds the encryption keys?
• Data lifecycle: What happens to your data when you stop paying?
• Third-party access: Who at Axiom (or their sub-processors) can access your data?
• Audit trail: Can you independently verify data access patterns?

For many organizations, these are not theoretical concerns. Regulated industries, government agencies, and companies handling PII often have contractual or legal obligations that make SaaS-only observability a non-starter.

How Parseable Solves This

Parseable gives you complete control over your data pipeline:

• Self-hosted deployment: Run Parseable on your own infrastructure. Your data never leaves your network.
• Your storage, your keys: Data is stored in your S3 buckets with your encryption keys (SSE-S3, SSE-KMS, or SSE-C).
• Data residency: Choose exactly which region and availability zone your data lives in.
• Air-gapped environments: Parseable runs as a single binary with no external dependencies. It works in fully disconnected environments.
• Open format: Data in Apache Parquet can be audited, verified, and processed by your existing data governance tools.

For organizations that need to comply with data residency requirements, Parseable eliminates the compliance risk entirely. Your telemetry data stays where you put it, in a format you control, encrypted with keys you manage.

Transparency and Deployment Flexibility

The deployment model and source code availability have concrete engineering implications.

Axiom: Closed Source

Axiom's source code is not available. This means:

• No code audit: You cannot verify how your data is processed, stored, or secured
• No self-hosting: You must use Axiom's SaaS platform
• No forking: If Axiom changes pricing, features, or terms, you have no recourse
• No community contributions: Bug fixes, performance improvements, and feature additions come entirely from Axiom's team
• Vendor dependency: Your observability stack depends entirely on a single company's continued operation and goodwill

Axiom has built a solid product, and closed-source SaaS is a legitimate business model. But it does mean that your observability platform is a black box whose behavior, security, and longevity you take on trust.

Parseable: Cloud and Self-Hosted

Parseable Cloud is a managed observability platform starting at $0.37/GB ingested ($29/month minimum). For teams that need full infrastructure control, Parseable is also available as a self-hosted deployment with source code on GitHub. This means:

• Deployment flexibility: Use Parseable Cloud for zero-ops managed observability, or self-host on any infrastructure you control.
• Full transparency: Every line of code is auditable. Security teams can verify data handling.
• Community-driven: Bug reports, feature requests, and contributions from a growing community.
• No vendor lock-in: Open source code, open Parquet data format, standard SQL queries.
• Extensibility: Fork, modify, and integrate with your existing tooling as needed.

The practical benefit is risk mitigation. When your observability platform stores data in an open format on your own storage with source code available, you have eliminated the most common forms of vendor lock-in (proprietary software and proprietary data formats). The third, query language lock-in, is addressed by Parseable's use of standard SQL.

Unified MELT Observability vs Log-Focused Analytics

This is where the platforms diverge most sharply in terms of scope.

Axiom: Primarily Log Analytics

Axiom was built as a log analytics platform with a strong emphasis on developer experience for web application developers. It has expanded into metrics and traces support, but logs remain the core product and the area where Axiom invests most heavily.

Axiom has particularly strong integrations with the Vercel and Next.js ecosystem, which makes it a natural choice for frontend-focused teams deploying on serverless platforms. Its APL (Axiom Processing Language) is optimized for log exploration, and the query experience for log data is genuinely good.

However, if you need a platform that treats metrics, events, and traces as first-class citizens alongside logs, Axiom's coverage is not as deep. Metrics support exists but is secondary to the log analytics focus. Trace visualization and correlation are functional but less mature than dedicated tracing platforms.

Parseable: Full MELT Observability

Parseable was designed from the ground up as a unified observability platform. Logs, Metrics, Events, and Traces (MELT) are all first-class data types, ingested through a single pipeline, stored in the same object storage backend, and queryable through the same SQL interface.

This matters for incident response. When you are investigating a production issue, you need to correlate a metric anomaly (CPU spike) with the traces that were executing at that time, drill into the specific spans that were slow, and find the log lines that explain why. In a unified platform, this is a single workflow. With a log-focused tool, you are context-switching between multiple systems, losing time and missing correlations.

Parseable's native OTLP endpoint (both HTTP and gRPC) means you can point your existing OpenTelemetry collectors at Parseable and start ingesting all signal types immediately. No proprietary agents, no translation layers, no separate pipelines for each telemetry type.

# OpenTelemetry Collector config pointing to Parseable
exporters:
  otlphttp:
    endpoint: "http://parseable:8000/v1"
    headers:
      Authorization: "Basic <base64-credentials>"
 
service:
  pipelines:
    logs:
      exporters: [otlphttp]
    metrics:
      exporters: [otlphttp]
    traces:
      exporters: [otlphttp]

All three signal types flow through a single exporter to a single endpoint. Parseable handles the rest.

Query Language: SQL vs APL

Axiom Processing Language (APL)

Axiom uses APL, a query language inspired by Kusto Query Language (KQL) used in Azure Data Explorer. APL is competent for log exploration:

['my-dataset']
| where level == "error"
| where _time > ago(1h)
| summarize count() by service
| order by count_ desc

APL is well-designed for pipe-based data transformations, and developers who have used KQL will feel at home. But it is a proprietary language. Your team needs to learn it, your queries are not portable, and your institutional knowledge is locked into Axiom's ecosystem.

Parseable: Standard SQL

Parseable uses standard SQL via Apache Arrow DataFusion. This is the same SQL your engineers use for databases, data warehouses, and analytics platforms:

SELECT service, COUNT(*) as error_count
FROM app_logs
WHERE level = 'error'
  AND p_timestamp > NOW() - INTERVAL '1 hour'
GROUP BY service
ORDER BY error_count DESC

The advantage is not just familiarity. SQL is the most widely known query language in the world. Every engineer on your team already speaks it. There is no training cost, no certification program, and no proprietary syntax to memorize. When you hire a new engineer, they can query your observability data on day one.

SQL also means your queries are portable. The same analytical patterns you use in Parseable work in PostgreSQL, DuckDB, Trino, Athena, and every other SQL-compatible system. You are not accumulating non-transferable query knowledge.

Pricing Comparison

Axiom Pricing

Axiom offers a tiered pricing model:

Axiom's free tier is generous for small projects and side projects. The Team tier at $25/month with 2 TB is competitive for small teams. But at enterprise scale (100+ GB/day), pricing moves to custom territory, and the total cost depends on negotiation.

Additional considerations:

• All tiers are SaaS-only. There is no self-hosted option at any price.
• Data retention beyond the included period requires higher tiers.
• Query compute is included (serverless model), but heavy query users on the free tier may hit rate limits.

Parseable Pricing

Parseable has two pricing models depending on deployment:

Self-Hosted (Open Source):

• Software cost: $0 (AGPL license)
• Storage cost: S3 pricing at ~$0.023/GB/month
• Compute cost: A single Parseable instance runs on modest hardware (<50MB RAM baseline)
• At 100 GB/day with 90% Parquet compression: ~$750/year in S3 storage
• At 100 GB/day without compression adjustment: ~$8,400/year in S3 storage

Parseable Cloud (app.parseable.com):

• $0.37/GB ingested, minimum $29/month, 30-day retention included
• Managed offering with SaaS convenience and data sovereignty
• No infrastructure to manage, no S3 bills to track, no compute to provision
• Enterprise tier available at custom pricing ($999/month minimum) with SSO/SAML and dedicated support

Cost Comparison at Scale

The self-hosted Parseable numbers reflect raw S3 storage costs with Parquet compression. You will also need compute for the Parseable binary, but a single instance handles substantial throughput, and the compute cost is a fraction of the storage cost. At scale, the total cost of ownership for self-hosted Parseable is typically 80-90% lower than any SaaS observability platform.

Why Parseable Wins the Axiom vs Parseable Comparison

The comparison table and architecture sections lay out the technical differences. But the real question is: what does choosing Parseable over Axiom mean for your engineering organization in practice? Here is a deeper look at the advantages.

True Data Ownership Changes Everything

When your observability data is stored in Apache Parquet files on your own S3 buckets, you own it in every meaningful sense. You can query it with Parseable's SQL interface for real-time investigation. You can also query the same data with DuckDB for ad-hoc analysis, with Apache Spark for batch processing, with AWS Athena for serverless querying, or with pandas in a Jupyter notebook for data science workflows.

This is not a theoretical benefit. Engineering teams routinely need to analyze observability data in ways that the observability platform itself does not support. Maybe you need to join log data with business metrics from your data warehouse. Maybe you need to feed historical telemetry into an ML model for anomaly detection. Maybe your security team needs to run forensic queries that span six months of data. With Parseable, your data is already in the format and location that makes all of this possible.

With Axiom, your data is on their infrastructure in their format. Accessing it requires their APIs, subject to their rate limits, in the structure they define. You are a tenant, not an owner.

Self-Hosting for Compliance Without Compromise

Many organizations cannot use SaaS observability platforms because of regulatory, contractual, or security requirements. Financial services firms handling trading data. Healthcare organizations processing PHI. Government agencies operating under FedRAMP or ITAR. Defense contractors in air-gapped environments.

Axiom cannot serve these use cases at all. It is SaaS-only, with no self-hosted option.

Parseable serves these use cases natively. A single binary deployment on your own infrastructure, connected to your own S3-compatible storage, with your own encryption keys. No data ever leaves your network. For air-gapped environments, Parseable runs with zero external dependencies. For multi-region compliance, deploy Parseable instances in each region with region-local storage.

This is not a niche advantage. As data privacy regulations tighten globally and supply chain security becomes a board-level concern, the ability to self-host your observability stack is becoming a requirement, not a preference.

Full MELT Observability Eliminates Tool Sprawl

If you adopt Axiom for logs and then need metrics and traces, you are shopping for additional tools. That means separate systems, separate query languages, separate dashboards, and the constant context-switching that slows down incident response.

Parseable handles logs, metrics, events, and traces in a single platform with a single query language (SQL). Your OpenTelemetry collectors send all signal types to one endpoint. Your engineers learn one tool. Your dashboards correlate across all signal types. When an alert fires on a metric, you can trace the exact requests involved and read the log lines that explain the root cause, all without leaving Parseable.

This unified approach is not just convenient. It directly reduces mean time to resolution (MTTR) by eliminating the investigation latency that comes from switching between multiple observability tools.

Rust Performance Means Lower Infrastructure Costs

Parseable is built in Rust, and it shows in the resource footprint. A single Parseable instance starts with under 50MB of RAM and can handle significant ingestion throughput. Compare this to Java-based observability backends that require gigabytes of heap memory, or Go-based systems that need careful garbage collection tuning at high throughput.

The practical impact is lower compute costs. You need fewer instances, smaller instance types, and less memory to handle the same workload. For self-hosted deployments, this translates directly to lower cloud bills. For organizations running observability at the edge or in resource-constrained environments, Parseable's efficiency is a critical advantage.

Open Standards Mean You Are Never Locked In

The most important advantage of Parseable over Axiom is the combination of open data formats, standard SQL, and deployment flexibility. These properties together mean that your investment in Parseable is portable in every dimension:

• Deployment: Parseable Cloud for zero-ops, or self-hosted on any infrastructure
• Data: Apache Parquet on your own storage, readable by hundreds of tools
• Queries: Standard SQL, transferable to any SQL-compatible system
• Agents: OpenTelemetry standard, usable with any OTLP-compatible backend

If you ever decide to move away from Parseable, your data is already in an open format on your own infrastructure, your queries are in standard SQL, and your instrumentation is based on OpenTelemetry. The switching cost is minimal. With Axiom, every dimension of your observability stack is proprietary.

AI-Native Log Analysis

Parseable integrates with Claude and other LLMs for natural language log analysis. Instead of writing SQL queries from scratch, you can describe what you are looking for in plain English and get query suggestions, anomaly explanations, and incident summaries powered by AI. This is particularly valuable for on-call engineers who need to investigate unfamiliar services at 3 AM.

Migration Guide: Moving from Axiom to Parseable

If you are currently using Axiom and want to evaluate Parseable, here is how to get started.

Step 1: Deploy Parseable

Option 1: Parseable Cloud (Recommended)

Sign up at app.parseable.com — free tier available, production-ready in under a minute. Starts at $0.37/GB ingested. No Docker, no S3 configuration needed. Skip to Step 2.

Option 2: Self-Hosted

Use the single-line install:

# Single binary deployment
curl https://sh.parseable.io | sh

Or with Docker:

docker run -p 8000:8000 \
  parseable/parseable:latest \
  parseable local-store

For production with S3 backend:

docker run -p 8000:8000 \
  -e P_S3_URL=https://s3.amazonaws.com \
  -e P_S3_ACCESS_KEY=your-access-key \
  -e P_S3_SECRET_KEY=your-secret-key \
  -e P_S3_BUCKET=your-observability-bucket \
  -e P_S3_REGION=us-east-1 \
  parseable/parseable:latest \
  parseable s3-store

Step 2: Create a Log Stream

curl -X PUT "http://localhost:8000/api/v1/logstream/app-logs" \
  -H "Authorization: Basic <base64-credentials>"

Step 3: Point OpenTelemetry Collectors at Parseable

If you are using OpenTelemetry collectors (which you should be, for vendor portability), update the exporter configuration:

exporters:
  otlphttp:
    endpoint: "http://parseable-host:8000/v1"
    headers:
      Authorization: "Basic <base64-credentials>"
 
service:
  pipelines:
    logs:
      receivers: [otlp]
      exporters: [otlphttp]
    metrics:
      receivers: [otlp]
      exporters: [otlphttp]
    traces:
      receivers: [otlp]
      exporters: [otlphttp]

Step 4: Send Data Directly via HTTP

For applications that send logs directly via HTTP (as many Axiom users do), the Parseable HTTP API is straightforward:

curl -X POST "http://localhost:8000/api/v1/logstream/app-logs" \
  -H "Content-Type: application/json" \
  -H "Authorization: Basic <base64-credentials>" \
  -d '[
    {
      "level": "info",
      "service": "api-gateway",
      "message": "Request processed successfully",
      "duration_ms": 42,
      "trace_id": "abc123"
    }
  ]'

Step 5: Query with SQL

Once data is flowing, query it with standard SQL through the Parseable console or API:

-- Error rate by service in the last hour
SELECT service,
       COUNT(*) FILTER (WHERE level = 'error') as errors,
       COUNT(*) as total,
       ROUND(100.0 * COUNT(*) FILTER (WHERE level = 'error') / COUNT(*), 2) as error_pct
FROM app_logs
WHERE p_timestamp > NOW() - INTERVAL '1 hour'
GROUP BY service
ORDER BY error_pct DESC;

Step 6: Run Dual-Write During Evaluation

During migration, send data to both Axiom and Parseable simultaneously using the OpenTelemetry Collector's multi-exporter capability. This lets you validate Parseable's query performance and feature set against your actual workload before fully cutting over.

exporters:
  otlphttp/parseable:
    endpoint: "http://parseable-host:8000/v1"
  otlphttp/axiom:
    endpoint: "https://api.axiom.co"
 
service:
  pipelines:
    logs:
      receivers: [otlp]
      exporters: [otlphttp/parseable, otlphttp/axiom]

Frequently Asked Questions

Is Axiom open source?

No. Axiom is a closed-source, SaaS-only platform. There is no publicly available source code and no self-hosted option. If you need an open-source alternative to Axiom with similar object-storage economics, Parseable is the closest architectural match with the added benefit of full transparency and self-hosting capability.

Can I self-host Axiom?

No. Axiom is available exclusively as a SaaS product. There is no self-hosted, on-premises, or bring-your-own-cloud deployment option. If self-hosting is a requirement for your organization due to compliance, data sovereignty, or security concerns, Parseable provides a self-hosted observability platform that uses the same object-storage architecture philosophy but runs entirely on your infrastructure.

What query language does Axiom use?

Axiom uses APL (Axiom Processing Language), a proprietary query language inspired by Kusto Query Language (KQL). While APL is well-designed for log exploration with its pipe-based syntax, it is not a standard language. Queries written in APL are not portable to other platforms. Parseable uses standard SQL, which is universally known and transferable to any SQL-compatible system.

How does Parseable compare to Axiom for performance?

Both platforms deliver fast query performance for log analytics. Axiom's serverless compute model provisions resources on demand, which works well for variable query loads. Parseable, built in Rust with Apache Arrow DataFusion, delivers sub-second queries on columnar Parquet data stored on S3. For most operational queries (filtering recent logs, aggregating error rates, tracing requests), both platforms are responsive. Parseable has the additional advantage that you can scale compute independently by adding instances, without being subject to SaaS rate limits or quotas.

Does Parseable support the same integrations as Axiom?

Parseable takes a standards-based approach to integrations. Its native OTLP endpoint (HTTP and gRPC) means any OpenTelemetry-instrumented application or collector can send data to Parseable directly. This covers the vast majority of modern infrastructure and application stacks. Axiom has built specific integrations for platforms like Vercel and Next.js, which are convenient for developers in that ecosystem. Parseable's OpenTelemetry-native approach is more broadly compatible but may require configuring an OTel Collector rather than using a platform-specific library.

Can Parseable replace Axiom for my team?

Yes, for most use cases. If you are using Axiom primarily for log analytics, Parseable provides equivalent or superior functionality with the added benefits of open source, self-hosting, data sovereignty, and full MELT observability. The migration path is straightforward: deploy Parseable, point your OpenTelemetry collectors at it, and start querying with SQL. The main scenario where Axiom might be preferred is if your team is deeply invested in the Vercel/Next.js-specific integrations and wants a zero-ops SaaS experience with no self-hosting requirement. For everything else, Parseable offers more capability, more control, and lower cost.

Axiom vs Parseable: The Verdict

Axiom and Parseable share a modern architectural philosophy: use object storage to dramatically reduce the cost of storing and querying telemetry data. But the similarities end there.

Axiom is a solid SaaS product for teams that want simple, managed log analytics with no infrastructure to run. It is a good fit for small to mid-sized teams, particularly those in the Vercel/Next.js ecosystem, who are comfortable with SaaS-only delivery and do not have strict data sovereignty requirements.

Parseable is the right choice when you need any combination of: open-source transparency, self-hosted deployment, data sovereignty, full MELT observability, standard SQL querying, or the freedom to avoid vendor lock-in. It delivers the same object-storage cost economics as Axiom while giving you complete control over your data, your deployment, and your future migration options.

For teams evaluating axiom alternatives in 2026, the decision often comes down to a simple question: do you want your observability platform to be a service you rent, or infrastructure you own? If the answer is the latter, Parseable is the platform to evaluate.

Ready to try Parseable?

• Start with Parseable Cloud — starts at $0.37/GB, free tier available
• Self-hosted deployment — single binary, deploy in 2 minutes
• Read the docs — guides, API reference, and tutorials
• Join our Slack — community and engineering support

Related reading:

• Top Splunk Alternatives for Log Management
• Open Source Log Management Tools
• The Future of S3-Native Log Analytics